{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Other OTUs as input\n", "\n", "### (1) Lab_Pathogens (2) Microbiome_Pathogens (3) Farm Practices and (4) Physicochemicals as Targets" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#Import necessary libraries\n", "%matplotlib inline\n", "import scipy as sp\n", "import matplotlib.pyplot as plt\n", "from sklearn.inspection import partial_dependence\n", "import pandas as pd\n", "import numpy as np\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn import metrics\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.inspection import plot_partial_dependence\n", "from sklearn.metrics import roc_curve, roc_auc_score\n", "from sklearn.model_selection import cross_val_score\n", "import matplotlib.ticker as mtick\n", "import seaborn as sns\n", "import warnings\n", "from warnings import simplefilter\n", "simplefilter(action='ignore', category=FutureWarning)\n", "simplefilter(action='ignore', category=UserWarning)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of duplicates SampleID in raw microbiome sheet1: 0\n", "Rows vs columns in sheet1: (364, 1319)\n" ] }, { "data": { "text/html": [ "
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IndexSampleIDUnassigned;Other;Other;Other;Other;Otherk__Archaea;p__Crenarchaeota;c__MBGA;o__NRP-J;f__;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__Cenarchaeaceae;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__SAGMA-X;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__Candidatus Nitrososphaerak__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobacteriumk__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobrevibacterk__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanosphaera...k__Bacteria;p__WS3;c__PRR-12;o__Sediment-1;f__;g__k__Bacteria;p__WS3;c__PRR-12;o__Sediment-1;f__PRR-10;g__k__Bacteria;p__WS4;c__;o__;f__;g__k__Bacteria;p__WS5;c__;o__;f__;g__k__Bacteria;p__ZB3;c__BS119;o__;f__;g__k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinococcusk__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__Trueperak__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Meiothermusk__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Thermus
0A1-10.0036950.00.00.00.00.0000000.00.0006470.0...0.00.0000000.00.00.0000000.000000.00.00.00.0
1A1-100.0241690.00.00.00.00.0737360.00.0002150.0...0.00.0000590.00.00.0000590.000000.00.00.00.0
2A1-110.0021780.00.00.00.00.0001020.00.0001170.0...0.00.0000000.00.00.0000000.000000.00.00.00.0
3A1-120.0085000.00.00.00.00.0001400.00.0000600.0...0.00.0000000.00.00.0000000.000040.00.00.00.0
4A1-130.0870240.00.00.00.00.0000000.00.0002610.0...0.00.0000000.00.00.0000000.000000.00.00.00.0
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5 rows × 1319 columns

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" ], "text/plain": [ "Index SampleID Unassigned;Other;Other;Other;Other;Other \\\n", "0 A1-1 0.003695 \n", "1 A1-10 0.024169 \n", "2 A1-11 0.002178 \n", "3 A1-12 0.008500 \n", "4 A1-13 0.087024 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__MBGA;o__NRP-J;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__Cenarchaeaceae;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__SAGMA-X;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__Candidatus Nitrososphaera \\\n", "0 0.000000 \n", "1 0.073736 \n", "2 0.000102 \n", "3 0.000140 \n", "4 0.000000 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobacterium \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobrevibacter \\\n", "0 0.000647 \n", "1 0.000215 \n", "2 0.000117 \n", "3 0.000060 \n", "4 0.000261 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanosphaera \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index ... k__Bacteria;p__WS3;c__PRR-12;o__Sediment-1;f__;g__ \\\n", "0 ... 0.0 \n", "1 ... 0.0 \n", "2 ... 0.0 \n", "3 ... 0.0 \n", "4 ... 0.0 \n", "\n", "Index k__Bacteria;p__WS3;c__PRR-12;o__Sediment-1;f__PRR-10;g__ \\\n", "0 0.000000 \n", "1 0.000059 \n", "2 0.000000 \n", "3 0.000000 \n", "4 0.000000 \n", "\n", "Index k__Bacteria;p__WS4;c__;o__;f__;g__ k__Bacteria;p__WS5;c__;o__;f__;g__ \\\n", "0 0.0 0.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "\n", "Index k__Bacteria;p__ZB3;c__BS119;o__;f__;g__ \\\n", "0 0.000000 \n", "1 0.000059 \n", "2 0.000000 \n", "3 0.000000 \n", "4 0.000000 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinococcus \\\n", "0 0.00000 \n", "1 0.00000 \n", "2 0.00000 \n", "3 0.00004 \n", "4 0.00000 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__Truepera \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Meiothermus \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Thermus \n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "[5 rows x 1319 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Preprocess microbiome sheet 1\n", "microbiome_1 = pd.read_excel(\"PP Genus Level QIIME1.9 taxa tables.xlsx\", sheet_name=0,skiprows = 1)\n", "microbiome_1.dropna(how='all', axis=1, inplace=True)\n", "microbiome_1.rename(columns={\"#OTU ID\":\"Index\"}, inplace = True)\n", "microbiome_1 = microbiome_1.set_index('Index').transpose()\n", "microbiome_1.reset_index(inplace = True)\n", "microbiome_1.rename(columns={\"index\":\"SampleID\"}, inplace = True)\n", "microbiome_1.replace({'\\.': '-','01': '1','02': '2','03': '3','04': '4','05': '5','06': '6','07': '7','08': '8','09': '9'}, regex=True, inplace=True)\n", "print(\"Number of duplicates SampleID in raw microbiome sheet1:\", microbiome_1.SampleID.duplicated().sum()) \n", "print(\"Rows vs columns in sheet1:\", microbiome_1.shape)\n", "microbiome_1.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of duplicates SampleID in raw microbiome sheet2: 0\n", "Rows vs columns in sheet2: (314, 1396)\n" ] }, { "data": { "text/html": [ "
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IndexSampleIDUnassigned;Other;Other;Other;Other;Otherk__Archaea;p__Crenarchaeota;c__MCG;o__pGrfC26;f__;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__SAGMA-X;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__Candidatus Nitrososphaerak__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobacteriumk__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobrevibacterk__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanosphaerak__Archaea;p__Euryarchaeota;c__Methanomicrobia;o__Methanocellales;f__Methanocellaceae;g__Methanocellak__Archaea;p__Euryarchaeota;c__Methanomicrobia;o__Methanomicrobiales;f__;g__...k__Bacteria;p__WS4;c__;o__;f__;g__k__Bacteria;p__WWE1;c__[Cloacamonae];o__[Cloacamonales];f__;g__k__Bacteria;p__WWE1;c__[Cloacamonae];o__[Cloacamonales];f__[Cloacamonaceae];Otherk__Bacteria;p__ZB3;c__;o__;f__;g__k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinococcusk__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__R18-435k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__B-42k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__Trueperak__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Thermus
0A2-10.0014050.00.00.0000000.00.0000640.00.00.0...0.00.00.00.00.00.00.00.00.00.0
1A2-100.0118490.00.00.0316240.00.0000000.00.00.0...0.00.00.00.00.00.00.00.00.00.0
2A2-110.0046210.00.00.0000750.00.0001490.00.00.0...0.00.00.00.00.00.00.00.00.00.0
3A2-120.0020960.00.00.0000620.00.0000620.00.00.0...0.00.00.00.00.00.00.00.00.00.0
4A2-130.0002230.00.00.0000500.00.0000000.00.00.0...0.00.00.00.00.00.00.00.00.00.0
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5 rows × 1396 columns

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" ], "text/plain": [ "Index SampleID Unassigned;Other;Other;Other;Other;Other \\\n", "0 A2-1 0.001405 \n", "1 A2-10 0.011849 \n", "2 A2-11 0.004621 \n", "3 A2-12 0.002096 \n", "4 A2-13 0.000223 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__MCG;o__pGrfC26;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__SAGMA-X;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__Candidatus Nitrososphaera \\\n", "0 0.000000 \n", "1 0.031624 \n", "2 0.000075 \n", "3 0.000062 \n", "4 0.000050 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobacterium \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobrevibacter \\\n", "0 0.000064 \n", "1 0.000000 \n", "2 0.000149 \n", "3 0.000062 \n", "4 0.000000 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanosphaera \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanomicrobia;o__Methanocellales;f__Methanocellaceae;g__Methanocella \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanomicrobia;o__Methanomicrobiales;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index ... k__Bacteria;p__WS4;c__;o__;f__;g__ \\\n", "0 ... 0.0 \n", "1 ... 0.0 \n", "2 ... 0.0 \n", "3 ... 0.0 \n", "4 ... 0.0 \n", "\n", "Index k__Bacteria;p__WWE1;c__[Cloacamonae];o__[Cloacamonales];f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__WWE1;c__[Cloacamonae];o__[Cloacamonales];f__[Cloacamonaceae];Other \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__ZB3;c__;o__;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinococcus \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__R18-435 \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__B-42 \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__Truepera \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Thermus \n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "[5 rows x 1396 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Preprocess microbiome sheet 2\n", "microbiome_2 = pd.read_excel(\"PP Genus Level QIIME1.9 taxa tables.xlsx\", sheet_name=1,skiprows = 1)\n", "microbiome_2.dropna(how='all', axis=1, inplace=True)\n", "microbiome_2.rename(columns={\"#OTU ID\":\"Index\"}, inplace = True)\n", "microbiome_2 = microbiome_2.set_index('Index').transpose()\n", "microbiome_2.reset_index(inplace = True)\n", "microbiome_2.rename(columns={\"index\":\"SampleID\"}, inplace = True)\n", "microbiome_2.replace({'\\.': '-','01': '1','02': '2','03': '3','04': '4','05': '5','06': '6','07': '7','08': '8','09': '9'}, regex=True, inplace=True)\n", "print(\"Number of duplicates SampleID in raw microbiome sheet2:\", microbiome_2.SampleID.duplicated().sum()) \n", "print(\"Rows vs columns in sheet2:\", microbiome_2.shape)\n", "microbiome_2.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of duplicates SampleID in raw microbiome sheet3: 0\n", "Rows vs columns in sheet3: (331, 1421)\n" ] }, { "data": { "text/html": [ "
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IndexSampleIDUnassigned;Other;Other;Other;Other;Otherk__Archaea;p__Crenarchaeota;c__;o__;f__;g__k__Archaea;p__Crenarchaeota;c__MBGA;o__NRP-J;f__;g__k__Archaea;p__Crenarchaeota;c__MBGB;o__;f__;g__k__Archaea;p__Crenarchaeota;c__MCG;o__;f__;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__Cenarchaeaceae;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__Cenarchaeaceae;g__Nitrosopumilusk__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__SAGMA-X;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__Candidatus Nitrososphaera...k__Bacteria;p__[Caldithrix];c__KSB1;o__GW-22;f__;g__k__Bacteria;p__[Caldithrix];c__KSB1;o__Ucn15732;f__;g__k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinobacteriumk__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinococcusk__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;Otherk__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__B-42k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__Trueperak__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Meiothermusk__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Thermus
0A6-10.0082090.00.00.00.00.00.00.00.000019...0.00.00.00.0000190.00.00.00.00.00.0
1A6-100.0102100.00.00.00.00.00.00.00.002054...0.00.00.00.0000000.00.00.00.00.00.0
2A6-110.0002320.00.00.00.00.00.00.00.000000...0.00.00.00.0000000.00.00.00.00.00.0
3A6-120.0081970.00.00.00.00.00.00.00.000087...0.00.00.00.0000000.00.00.00.00.00.0
4A6-130.0017970.00.00.00.00.00.00.00.000143...0.00.00.00.0000000.00.00.00.00.00.0
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5 rows × 1421 columns

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" ], "text/plain": [ "Index SampleID Unassigned;Other;Other;Other;Other;Other \\\n", "0 A6-1 0.008209 \n", "1 A6-10 0.010210 \n", "2 A6-11 0.000232 \n", "3 A6-12 0.008197 \n", "4 A6-13 0.001797 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__;o__;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__MBGA;o__NRP-J;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__MBGB;o__;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__MCG;o__;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__Cenarchaeaceae;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__Cenarchaeaceae;g__Nitrosopumilus \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__SAGMA-X;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__Candidatus Nitrososphaera \\\n", "0 0.000019 \n", "1 0.002054 \n", "2 0.000000 \n", "3 0.000087 \n", "4 0.000143 \n", "\n", "Index ... k__Bacteria;p__[Caldithrix];c__KSB1;o__GW-22;f__;g__ \\\n", "0 ... 0.0 \n", "1 ... 0.0 \n", "2 ... 0.0 \n", "3 ... 0.0 \n", "4 ... 0.0 \n", "\n", "Index k__Bacteria;p__[Caldithrix];c__KSB1;o__Ucn15732;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinobacterium \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinococcus \\\n", "0 0.000019 \n", "1 0.000000 \n", "2 0.000000 \n", "3 0.000000 \n", "4 0.000000 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;Other \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__B-42 \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__Truepera \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Meiothermus \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Thermus \n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "[5 rows x 1421 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Preprocess microbiome sheet 3\n", "microbiome_3 = pd.read_excel(\"PP Genus Level QIIME1.9 taxa tables.xlsx\", sheet_name=2,skiprows = 1)\n", "microbiome_3.dropna(how='all', axis=1, inplace=True)\n", "microbiome_3.rename(columns={\"#OTU ID\":\"Index\"}, inplace = True)\n", "microbiome_3 = microbiome_3.set_index('Index').transpose()\n", "microbiome_3.reset_index(inplace = True)\n", "microbiome_3.rename(columns={\"index\":\"SampleID\"}, inplace = True)\n", "microbiome_3.replace({'\\.': '-','01': '1','02': '2','03': '3','04': '4','05': '5','06': '6','07': '7','08': '8','09': '9'}, regex=True, inplace=True)\n", "print(\"Number of duplicates SampleID in raw microbiome sheet3:\", microbiome_3.SampleID.duplicated().sum()) \n", "print(\"Rows vs columns in sheet3:\", microbiome_3.shape)\n", "microbiome_3.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of duplicates SampleID in raw microbiome sheet4: 0\n", "Rows vs columns in sheet4: (333, 1454)\n" ] }, { "data": { "text/html": [ "
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IndexSampleIDUnassigned;Other;Other;Other;Other;Otherk__Archaea;p__Crenarchaeota;c__MBGA;o__NRP-J;f__;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__Cenarchaeaceae;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__Cenarchaeaceae;g__Nitrosopumilusk__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__SAGMA-X;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__Candidatus Nitrososphaerak__Archaea;p__Euryarchaeota;c__Halobacteria;o__Halobacteriales;f__Halobacteriaceae;g__Haladaptatusk__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobacterium...k__Bacteria;p__WS3;c__PRR-12;o__Sediment-1;f__;g__k__Bacteria;p__WS3;c__PRR-12;o__Sediment-1;f__PRR-10;g__k__Bacteria;p__WS4;c__;o__;f__;g__k__Bacteria;p__WS5;c__;o__;f__;g__k__Bacteria;p__[Caldithrix];c__KSB1;o__MSB-5B5;f__;g__k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinobacteriumk__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinococcusk__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__B-42k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__Trueperak__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Thermus
0A9-10.0029770.00.00.00.00.0000000.00.00.0...0.00.00.00.00.00.00.0000490.00.00.0
1A9-20.0027820.00.00.00.00.0000000.00.00.0...0.00.00.00.00.00.00.0000000.00.00.0
2A9-30.0018570.00.00.00.00.0000550.00.00.0...0.00.00.00.00.00.00.0000000.00.00.0
3A9-40.0003880.00.00.00.00.0000260.00.00.0...0.00.00.00.00.00.00.0000000.00.00.0
4A9-50.0003890.00.00.00.00.0000350.00.00.0...0.00.00.00.00.00.00.0000000.00.00.0
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5 rows × 1454 columns

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" ], "text/plain": [ "Index SampleID Unassigned;Other;Other;Other;Other;Other \\\n", "0 A9-1 0.002977 \n", "1 A9-2 0.002782 \n", "2 A9-3 0.001857 \n", "3 A9-4 0.000388 \n", "4 A9-5 0.000389 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__MBGA;o__NRP-J;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__Cenarchaeaceae;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__Cenarchaeaceae;g__Nitrosopumilus \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__SAGMA-X;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__Candidatus Nitrososphaera \\\n", "0 0.000000 \n", "1 0.000000 \n", "2 0.000055 \n", "3 0.000026 \n", "4 0.000035 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Halobacteria;o__Halobacteriales;f__Halobacteriaceae;g__Haladaptatus \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobacterium \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index ... k__Bacteria;p__WS3;c__PRR-12;o__Sediment-1;f__;g__ \\\n", "0 ... 0.0 \n", "1 ... 0.0 \n", "2 ... 0.0 \n", "3 ... 0.0 \n", "4 ... 0.0 \n", "\n", "Index k__Bacteria;p__WS3;c__PRR-12;o__Sediment-1;f__PRR-10;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__WS4;c__;o__;f__;g__ k__Bacteria;p__WS5;c__;o__;f__;g__ \\\n", "0 0.0 0.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "\n", "Index k__Bacteria;p__[Caldithrix];c__KSB1;o__MSB-5B5;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinobacterium \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinococcus \\\n", "0 0.000049 \n", "1 0.000000 \n", "2 0.000000 \n", "3 0.000000 \n", "4 0.000000 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__B-42 \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__Truepera \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Thermus \n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "[5 rows x 1454 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Preprocess microbiome sheet 4\n", "microbiome_4 = pd.read_excel(\"PP Genus Level QIIME1.9 taxa tables.xlsx\", sheet_name=3,skiprows = 1)\n", "microbiome_4.dropna(how='all', axis=1, inplace=True)\n", "microbiome_4.rename(columns={\"#OTU ID\":\"Index\"}, inplace = True)\n", "microbiome_4 = microbiome_4.set_index('Index').transpose()\n", "microbiome_4.reset_index(inplace = True)\n", "microbiome_4.rename(columns={\"index\":\"SampleID\"}, inplace = True)\n", "microbiome_4.replace({'\\.': '-','01': '1','02': '2','03': '3','04': '4','05': '5','06': '6','07': '7','08': '8','09': '9'}, regex=True, inplace=True)\n", "print(\"Number of duplicates SampleID in raw microbiome sheet4:\", microbiome_4.SampleID.duplicated().sum()) \n", "print(\"Rows vs columns in sheet4:\", microbiome_4.shape)\n", "microbiome_4.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of duplicates SampleID in raw microbiome sheet5: 0\n", "Rows vs columns in sheet5: (336, 1443)\n" ] }, { "data": { "text/html": [ "
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IndexSampleIDUnassigned;Other;Other;Other;Other;Otherk__Archaea;p__Crenarchaeota;c__MBGA;o__NRP-J;f__;g__k__Archaea;p__Crenarchaeota;c__MCG;o__pGrfC26;f__;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__SAGMA-X;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__Candidatus Nitrososphaerak__Archaea;p__Euryarchaeota;c__DSEG;o__104A5;f__;g__k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobacterium...k__Bacteria;p__WS4;c__;o__;f__;g__k__Bacteria;p__WS5;c__;o__;f__;g__k__Bacteria;p__ZB3;c__;o__;f__;g__k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinococcusk__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__R18-435k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__B-42k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__Trueperak__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Meiothermusk__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Thermus
0B5-10.0019800.00.00.00.00.0001130.00.00.0...0.00.00.00.0000000.00.00.00.00.00.0
1B5-20.0014380.00.00.00.00.0001330.00.00.0...0.00.00.00.0000120.00.00.00.00.00.0
2B5-30.0013990.00.00.00.00.0001820.00.00.0...0.00.00.00.0000000.00.00.00.00.00.0
3B5-40.0012060.00.00.00.00.0001010.00.00.0...0.00.00.00.0000000.00.00.00.00.00.0
4B5-50.0018230.00.00.00.00.0007200.00.00.0...0.00.00.00.0000000.00.00.00.00.00.0
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5 rows × 1443 columns

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" ], "text/plain": [ "Index SampleID Unassigned;Other;Other;Other;Other;Other \\\n", "0 B5-1 0.001980 \n", "1 B5-2 0.001438 \n", "2 B5-3 0.001399 \n", "3 B5-4 0.001206 \n", "4 B5-5 0.001823 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__MBGA;o__NRP-J;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__MCG;o__pGrfC26;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__SAGMA-X;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__Candidatus Nitrososphaera \\\n", "0 0.000113 \n", "1 0.000133 \n", "2 0.000182 \n", "3 0.000101 \n", "4 0.000720 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__DSEG;o__104A5;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobacterium \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index ... k__Bacteria;p__WS4;c__;o__;f__;g__ \\\n", "0 ... 0.0 \n", "1 ... 0.0 \n", "2 ... 0.0 \n", "3 ... 0.0 \n", "4 ... 0.0 \n", "\n", "Index k__Bacteria;p__WS5;c__;o__;f__;g__ k__Bacteria;p__ZB3;c__;o__;f__;g__ \\\n", "0 0.0 0.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinococcus \\\n", "0 0.000000 \n", "1 0.000012 \n", "2 0.000000 \n", "3 0.000000 \n", "4 0.000000 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__R18-435 \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__B-42 \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__Truepera \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Meiothermus \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Thermus \n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "[5 rows x 1443 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Preprocess microbiome sheet 5\n", "microbiome_5 = pd.read_excel(\"PP Genus Level QIIME1.9 taxa tables.xlsx\", sheet_name=4,skiprows = 1)\n", "microbiome_5.dropna(how='all', axis=1, inplace=True)\n", "microbiome_5.rename(columns={\"#OTU ID\":\"Index\"}, inplace = True)\n", "microbiome_5 = microbiome_5.set_index('Index').transpose()\n", "microbiome_5.reset_index(inplace = True)\n", "microbiome_5.rename(columns={\"index\":\"SampleID\"}, inplace = True)\n", "microbiome_5.replace({'\\.': '-','01': '1','02': '2','03': '3','04': '4','05': '5','06': '6','07': '7','08': '8','09': '9'}, regex=True, inplace=True)\n", "print(\"Number of duplicates SampleID in raw microbiome sheet5:\", microbiome_5.SampleID.duplicated().sum()) \n", "print(\"Rows vs columns in sheet5:\", microbiome_5.shape)\n", "microbiome_5.head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of duplicates SampleID in raw microbiome sheet6: 0\n", "Rows vs columns in sheet6: (318, 1380)\n" ] }, { "data": { "text/html": [ "
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IndexSampleIDUnassigned;Other;Other;Other;Other;Otherk__Archaea;p__Crenarchaeota;c__MBGA;o__NRP-J;f__;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__Cenarchaeaceae;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__SAGMA-X;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__Candidatus Nitrososphaerak__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;Otherk__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobacteriumk__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobrevibacter...k__Bacteria;p__WS3;c__PRR-12;o__Sediment-1;f__PRR-10;g__k__Bacteria;p__WS4;c__;o__;f__;g__k__Bacteria;p__WWE1;c__[Cloacamonae];o__[Cloacamonales];f__SHA-116;g__k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinobacteriumk__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinococcusk__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__R18-435k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__B-42k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__Trueperak__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Meiothermus
0E1-260.0077630.00.00.00.0032620.00.00.00.000015...0.00.00.00.00.0003320.00.0000450.00.0000760.0
1J1-10.0007430.00.00.00.0000000.00.00.00.000000...0.00.00.00.00.0000000.00.0000000.00.0000000.0
2J1-20.0018130.00.00.00.0000250.00.00.00.000124...0.00.00.00.00.0000000.00.0000000.00.0000000.0
3J1-30.0014370.00.00.00.0000340.00.00.00.000103...0.00.00.00.00.0000000.00.0000000.00.0000000.0
4J1-40.0009230.00.00.00.0001420.00.00.00.000071...0.00.00.00.00.0000000.00.0000000.00.0000000.0
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5 rows × 1380 columns

\n", "
" ], "text/plain": [ "Index SampleID Unassigned;Other;Other;Other;Other;Other \\\n", "0 E1-26 0.007763 \n", "1 J1-1 0.000743 \n", "2 J1-2 0.001813 \n", "3 J1-3 0.001437 \n", "4 J1-4 0.000923 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__MBGA;o__NRP-J;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__Cenarchaeaceae;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__SAGMA-X;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__Candidatus Nitrososphaera \\\n", "0 0.003262 \n", "1 0.000000 \n", "2 0.000025 \n", "3 0.000034 \n", "4 0.000142 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;Other \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobacterium \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobrevibacter \\\n", "0 0.000015 \n", "1 0.000000 \n", "2 0.000124 \n", "3 0.000103 \n", "4 0.000071 \n", "\n", "Index ... k__Bacteria;p__WS3;c__PRR-12;o__Sediment-1;f__PRR-10;g__ \\\n", "0 ... 0.0 \n", "1 ... 0.0 \n", "2 ... 0.0 \n", "3 ... 0.0 \n", "4 ... 0.0 \n", "\n", "Index k__Bacteria;p__WS4;c__;o__;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__WWE1;c__[Cloacamonae];o__[Cloacamonales];f__SHA-116;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinobacterium \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinococcus \\\n", "0 0.000332 \n", "1 0.000000 \n", "2 0.000000 \n", "3 0.000000 \n", "4 0.000000 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__R18-435 \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__ \\\n", "0 0.000045 \n", "1 0.000000 \n", "2 0.000000 \n", "3 0.000000 \n", "4 0.000000 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__B-42 \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__Truepera \\\n", "0 0.000076 \n", "1 0.000000 \n", "2 0.000000 \n", "3 0.000000 \n", "4 0.000000 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Meiothermus \n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "[5 rows x 1380 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Preprocess microbiome sheet 6\n", "microbiome_6 = pd.read_excel(\"PP Genus Level QIIME1.9 taxa tables.xlsx\", sheet_name=5,skiprows = 1)\n", "microbiome_6.dropna(how='all', axis=1, inplace=True)\n", "microbiome_6.rename(columns={\"#OTU ID\":\"Index\"}, inplace = True)\n", "microbiome_6 = microbiome_6.set_index('Index').transpose()\n", "microbiome_6.reset_index(inplace = True)\n", "microbiome_6.rename(columns={\"index\":\"SampleID\"}, inplace = True)\n", "microbiome_6.replace({'\\.': '-','01': '1','02': '2','03': '3','04': '4','05': '5','06': '6','07': '7','08': '8','09': '9'}, regex=True, inplace=True)\n", "print(\"Number of duplicates SampleID in raw microbiome sheet6:\", microbiome_6.SampleID.duplicated().sum()) \n", "print(\"Rows vs columns in sheet6:\", microbiome_6.shape)\n", "microbiome_6.head()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of duplicates SampleID after merging all sheets: 6\n", "Number of duplicates SampleID after drop function: 0\n", "Microbiome Rows (SampleID) vs Columns (OTUS): (1990, 1825)\n" ] } ], "source": [ "#Merged the 6 processed sheets\n", "microbiome = pd.concat([microbiome_1,microbiome_2,microbiome_3,microbiome_4,microbiome_5,microbiome_6], axis=0, ignore_index=True)\n", "print(\"Number of duplicates SampleID after merging all sheets:\", microbiome.SampleID.duplicated().sum()) \n", "microbiome.drop_duplicates(subset=\"SampleID\", inplace=True)\n", "print(\"Number of duplicates SampleID after drop function:\", microbiome.SampleID.duplicated().sum()) \n", "microbiome.fillna(0, inplace=True)\n", "print(\"Microbiome Rows (SampleID) vs Columns (OTUS):\", microbiome.shape)\n", "microbiome.head()\n", "\n", "#drop listeria at genus level as it contains no information\n", "microbiome.drop('k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Listeriaceae;g__Listeria', axis=1, inplace=True)\n", "#Listeria detectection was at the family level, rename as genus for analysis purpose\n", "microbiome.rename(columns = {'k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Listeriaceae;Other':\n", " 'k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Listeriaceae;g__Listeria'},inplace=True)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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IndexSampleIDUnassigned;Other;Other;Other;Other;Otherk__Archaea;p__Crenarchaeota;c__MBGA;o__NRP-J;f__;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__Cenarchaeaceae;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__SAGMA-X;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__Candidatus Nitrososphaerak__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobacteriumk__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobrevibacterk__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanosphaera...k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Oxalobacteraceae;g__Telluriak__Bacteria;p__Proteobacteria;c__Deltaproteobacteria;o__Syntrophobacterales;f__Syntrophaceae;g__Desulfobaccak__Bacteria;p__Proteobacteria;c__Deltaproteobacteria;o__Syntrophobacterales;f__Syntrophaceae;g__Desulfomonilek__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacteriales;f__Enterobacteriaceae;g__Photorhabdusk__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Oceanospirillales;f__Hahellaceae;g__k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Oceanospirillales;f__Oceanospirillaceae;Otherk__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pasteurellales;f__Pasteurellaceae;g__Pasteurellak__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Vibrionales;f__Vibrionaceae;g__Salinivibriok__Bacteria;p__WS1;c__;o__;f__;g__k__Bacteria;p__WWE1;c__[Cloacamonae];o__[Cloacamonales];f__SHA-116;g__
1991MP1-70.0099510.00.00.0000000.00.0156660.00.0001280.0...0.00.00.00.00.00.00.00.00.00.0
1992MP1-80.0083010.00.00.0000150.00.0107010.00.0000000.0...0.00.00.00.00.00.00.00.00.00.0
1993MP1-90.0076490.00.00.0000000.00.0096120.00.0000130.0...0.00.00.00.00.00.00.00.00.00.0
1994MP1-100.0081140.00.00.0000000.00.0069700.00.0000470.0...0.00.00.00.00.00.00.00.00.00.0
1995STANDARD0.0005070.00.00.0000000.00.0000280.00.0000000.0...0.00.00.00.00.00.00.00.00.00.0
\n", "

5 rows × 1824 columns

\n", "
" ], "text/plain": [ "Index SampleID Unassigned;Other;Other;Other;Other;Other \\\n", "1991 MP1-7 0.009951 \n", "1992 MP1-8 0.008301 \n", "1993 MP1-9 0.007649 \n", "1994 MP1-10 0.008114 \n", "1995 STANDARD 0.000507 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__MBGA;o__NRP-J;f__;g__ \\\n", "1991 0.0 \n", "1992 0.0 \n", "1993 0.0 \n", "1994 0.0 \n", "1995 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__Cenarchaeaceae;g__ \\\n", "1991 0.0 \n", "1992 0.0 \n", "1993 0.0 \n", "1994 0.0 \n", "1995 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__SAGMA-X;g__ \\\n", "1991 0.000000 \n", "1992 0.000015 \n", "1993 0.000000 \n", "1994 0.000000 \n", "1995 0.000000 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__ \\\n", "1991 0.0 \n", "1992 0.0 \n", "1993 0.0 \n", "1994 0.0 \n", "1995 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__Candidatus Nitrososphaera \\\n", "1991 0.015666 \n", "1992 0.010701 \n", "1993 0.009612 \n", "1994 0.006970 \n", "1995 0.000028 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobacterium \\\n", "1991 0.0 \n", "1992 0.0 \n", "1993 0.0 \n", "1994 0.0 \n", "1995 0.0 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobrevibacter \\\n", "1991 0.000128 \n", "1992 0.000000 \n", "1993 0.000013 \n", "1994 0.000047 \n", "1995 0.000000 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanosphaera \\\n", "1991 0.0 \n", "1992 0.0 \n", "1993 0.0 \n", "1994 0.0 \n", "1995 0.0 \n", "\n", "Index ... \\\n", "1991 ... \n", "1992 ... \n", "1993 ... \n", "1994 ... \n", "1995 ... \n", "\n", "Index k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Oxalobacteraceae;g__Telluria \\\n", "1991 0.0 \n", "1992 0.0 \n", "1993 0.0 \n", "1994 0.0 \n", "1995 0.0 \n", "\n", "Index k__Bacteria;p__Proteobacteria;c__Deltaproteobacteria;o__Syntrophobacterales;f__Syntrophaceae;g__Desulfobacca \\\n", "1991 0.0 \n", "1992 0.0 \n", "1993 0.0 \n", "1994 0.0 \n", "1995 0.0 \n", "\n", "Index k__Bacteria;p__Proteobacteria;c__Deltaproteobacteria;o__Syntrophobacterales;f__Syntrophaceae;g__Desulfomonile \\\n", "1991 0.0 \n", "1992 0.0 \n", "1993 0.0 \n", "1994 0.0 \n", "1995 0.0 \n", "\n", "Index k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacteriales;f__Enterobacteriaceae;g__Photorhabdus \\\n", "1991 0.0 \n", "1992 0.0 \n", "1993 0.0 \n", "1994 0.0 \n", "1995 0.0 \n", "\n", "Index k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Oceanospirillales;f__Hahellaceae;g__ \\\n", "1991 0.0 \n", "1992 0.0 \n", "1993 0.0 \n", "1994 0.0 \n", "1995 0.0 \n", "\n", "Index k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Oceanospirillales;f__Oceanospirillaceae;Other \\\n", "1991 0.0 \n", "1992 0.0 \n", "1993 0.0 \n", "1994 0.0 \n", "1995 0.0 \n", "\n", "Index k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pasteurellales;f__Pasteurellaceae;g__Pasteurella \\\n", "1991 0.0 \n", "1992 0.0 \n", "1993 0.0 \n", "1994 0.0 \n", "1995 0.0 \n", "\n", "Index k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Vibrionales;f__Vibrionaceae;g__Salinivibrio \\\n", "1991 0.0 \n", "1992 0.0 \n", "1993 0.0 \n", "1994 0.0 \n", "1995 0.0 \n", "\n", "Index k__Bacteria;p__WS1;c__;o__;f__;g__ \\\n", "1991 0.0 \n", "1992 0.0 \n", "1993 0.0 \n", "1994 0.0 \n", "1995 0.0 \n", "\n", "Index k__Bacteria;p__WWE1;c__[Cloacamonae];o__[Cloacamonales];f__SHA-116;g__ \n", "1991 0.0 \n", "1992 0.0 \n", "1993 0.0 \n", "1994 0.0 \n", "1995 0.0 \n", "\n", "[5 rows x 1824 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "microbiome.tail()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Unique Taxonomy\n" ] }, { "data": { "text/plain": [ "Index\n", "Kingdom 3\n", "Phylum 63\n", "Class 176\n", "Order 305\n", "Family 389\n", "Genus 877\n", "dtype: int64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Check number of unique taxonomy at each of the 6 levels\n", "microbiome.rename(columns={\"SampleID\":\"Index\"}, inplace = True)\n", "microbiome = microbiome.set_index('Index').transpose()\n", "microbiome.reset_index(inplace = True)\n", "microbiome.rename(columns={\"Index\":\"OTU\"}, inplace = True)\n", "\n", "microbiomecopy = microbiome.copy()\n", "microbiomecopy[['Kingdom','Phylum','Class','Order','Family','Genus']] = microbiomecopy[\"OTU\"].str.split(pat=\";\", expand=True)\n", "microbiomecopy = microbiomecopy[['Kingdom','Phylum','Class','Order','Family','Genus']]\n", "print(\"Unique Taxonomy\")\n", "microbiomecopy.nunique()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Unique Kingdom to Genus= 1823\n", "Unique Kingdom to Family= 842\n", "Unique Kingdom to Order= 479\n", "Unique Kingdom to Class= 176\n" ] } ], "source": [ "#Check uniques from kingdom down to lower levels\n", "microbiomecopy[\"Genus\"] = microbiomecopy[\"Kingdom\"]+\";\"+microbiomecopy[\"Phylum\"]+\";\"+ microbiomecopy[\"Class\"]+\";\"+microbiomecopy[\"Order\"]+\";\"+microbiomecopy[\"Family\"]+\";\"+microbiomecopy[\"Genus\"]\n", "print(\"Unique Kingdom to Genus=\", microbiomecopy.Genus.nunique())\n", "\n", "microbiomecopy[\"Family\"] = microbiomecopy[\"Kingdom\"]+\";\"+microbiomecopy[\"Phylum\"]+\";\"+ microbiomecopy[\"Class\"]+\";\"+microbiomecopy[\"Order\"]+\";\"+microbiomecopy[\"Family\"]\n", "print(\"Unique Kingdom to Family=\", microbiomecopy.Family.nunique())\n", "\n", "microbiomecopy[\"Order\"] = microbiomecopy[\"Kingdom\"]+\";\"+microbiomecopy[\"Phylum\"]+\";\"+ microbiomecopy[\"Class\"]+\";\"+microbiomecopy[\"Order\"]\n", "print(\"Unique Kingdom to Order=\", microbiomecopy.Order.nunique())\n", "\n", "microbiomecopy[\"Order\"] = microbiomecopy[\"Kingdom\"]+\";\"+microbiomecopy[\"Phylum\"]+\";\"+ microbiomecopy[\"Class\"]\n", "print(\"Unique Kingdom to Class=\", microbiomecopy.Class.nunique())" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Out of 1824 genera, unique ones = 877 , the rest are just repeated g_ or Other\n" ] } ], "source": [ "#Compute unique genera\n", "microbiomecopy[['Kingdom','Phylum','Class','Order','Family','Genus']] = microbiomecopy[\"Genus\"].str.split(pat=\";\", expand=True)\n", "microbiome[\"Genus\"] = microbiomecopy[\"Genus\"]\n", "print(\"Out of 1824 genera, unique ones =\",microbiome.Genus.nunique(),\", the rest are just repeated g_ or Other\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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GenusSampleIDOtherg__g__02d06g__1-68g__4-29g__5-7N15g__A17g__Acaryochlorisg__Acetobacter...g__cc_115g__gutg__heteroC45_4Wg__p-75-a5g__ph2g__rc4-4g__u114g__vadinCA02g__vadinCA11g__vadinHB04
0A1-10.0224480.1206470.00.00.0000000.00.0000000.00.0...0.0002770.00.0000000.0000000.0000000.00.00.00.0000920.0
1A1-100.0521810.5339220.00.00.0000000.00.0010920.00.0...0.0000200.00.0001950.0000200.0000000.00.00.00.0000000.0
2A1-110.0064760.0232130.00.00.0000150.00.0000000.00.0...0.0000000.00.0000000.0000000.0000150.00.00.00.0000290.0
3A1-120.1899010.0880240.00.00.0000000.00.0000200.00.0...0.0002210.00.0000200.0000000.0000400.00.00.00.0000800.0
4A1-130.1060450.3439290.00.00.0000000.00.0000000.00.0...0.0018240.00.0000000.0002610.0000000.00.00.00.0007820.0
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5 rows × 878 columns

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" ], "text/plain": [ "Genus SampleID Other g__ g__02d06 g__1-68 g__4-29 g__5-7N15 \\\n", "0 A1-1 0.022448 0.120647 0.0 0.0 0.000000 0.0 \n", "1 A1-10 0.052181 0.533922 0.0 0.0 0.000000 0.0 \n", "2 A1-11 0.006476 0.023213 0.0 0.0 0.000015 0.0 \n", "3 A1-12 0.189901 0.088024 0.0 0.0 0.000000 0.0 \n", "4 A1-13 0.106045 0.343929 0.0 0.0 0.000000 0.0 \n", "\n", "Genus g__A17 g__Acaryochloris g__Acetobacter ... g__cc_115 g__gut \\\n", "0 0.000000 0.0 0.0 ... 0.000277 0.0 \n", "1 0.001092 0.0 0.0 ... 0.000020 0.0 \n", "2 0.000000 0.0 0.0 ... 0.000000 0.0 \n", "3 0.000020 0.0 0.0 ... 0.000221 0.0 \n", "4 0.000000 0.0 0.0 ... 0.001824 0.0 \n", "\n", "Genus g__heteroC45_4W g__p-75-a5 g__ph2 g__rc4-4 g__u114 g__vadinCA02 \\\n", "0 0.000000 0.000000 0.000000 0.0 0.0 0.0 \n", "1 0.000195 0.000020 0.000000 0.0 0.0 0.0 \n", "2 0.000000 0.000000 0.000015 0.0 0.0 0.0 \n", "3 0.000020 0.000000 0.000040 0.0 0.0 0.0 \n", "4 0.000000 0.000261 0.000000 0.0 0.0 0.0 \n", "\n", "Genus g__vadinCA11 g__vadinHB04 \n", "0 0.000092 0.0 \n", "1 0.000000 0.0 \n", "2 0.000029 0.0 \n", "3 0.000080 0.0 \n", "4 0.000782 0.0 \n", "\n", "[5 rows x 878 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#make new dataframe with only unique microbiome\n", "microbiome = microbiome.groupby([\"Genus\"]).sum()\n", "microbiome = microbiome.transpose()\n", "microbiome.reset_index(inplace = True)\n", "microbiome.rename(columns={\"Index\":\"SampleID\"}, inplace = True)\n", "microbiome.head()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sum of rows in microbiome\n", " 0 1.0\n", "1 1.0\n", "2 1.0\n", "3 1.0\n", "4 1.0\n", "5 1.0\n", "6 1.0\n", "7 1.0\n", "8 1.0\n", "9 1.0\n", "dtype: float64\n" ] } ], "source": [ "#Check number of samples vs microbiome\n", "print(\"Sum of rows in microbiome\\n\",microbiome.sum(axis=1).round(2).head(10))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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GenusPathogen_SalmonellaPathogen_CampylobacterPathogen_ListeriaPathogen_EcoliProbiotic_BacillusProbiotic_BifidobacteriumProbiotic_ClostridiumProbiotic_EnterococcusProbiotic_LactobacillusProbiotic_PediococcusProbiotic_PropionibacteriumProbiotic_Streptococcus
00.0000000.0010160.00.00.0001850.0000000.0002770.0036950.7444800.0000000.00.000370
10.0000780.0006830.00.00.0277390.0000000.0058910.0004680.0213600.0000000.00.000156
20.0000000.0032890.00.00.0006140.0000000.0003360.0019880.9238120.0001020.00.001579
30.0000000.0003210.00.00.0006620.0000000.0004610.0032480.6458980.0000000.00.000321
40.0000000.0005210.00.00.0000000.0000000.0015630.0013030.1886400.0000000.00.001563
.......................................
19850.0000000.0000000.00.00.0620760.0000000.0028070.0000510.0020920.0000260.00.000077
19860.0000000.0000000.00.00.0314840.0000300.0032450.0000910.0016750.0000000.00.000060
19870.0000000.0000130.00.00.0293800.0000380.0010130.0001390.0014440.0000250.00.000025
19880.0000000.0000000.00.00.0252660.0000820.0023580.0000820.0012840.0000000.00.000070
19890.0000000.0000000.00.00.0428470.0000560.0024490.0001970.0045040.0000000.00.207702
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1990 rows × 12 columns

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" ], "text/plain": [ "Genus Pathogen_Salmonella Pathogen_Campylobacter Pathogen_Listeria \\\n", "0 0.000000 0.001016 0.0 \n", "1 0.000078 0.000683 0.0 \n", "2 0.000000 0.003289 0.0 \n", "3 0.000000 0.000321 0.0 \n", "4 0.000000 0.000521 0.0 \n", "... ... ... ... \n", "1985 0.000000 0.000000 0.0 \n", "1986 0.000000 0.000000 0.0 \n", "1987 0.000000 0.000013 0.0 \n", "1988 0.000000 0.000000 0.0 \n", "1989 0.000000 0.000000 0.0 \n", "\n", "Genus Pathogen_Ecoli Probiotic_Bacillus Probiotic_Bifidobacterium \\\n", "0 0.0 0.000185 0.000000 \n", "1 0.0 0.027739 0.000000 \n", "2 0.0 0.000614 0.000000 \n", "3 0.0 0.000662 0.000000 \n", "4 0.0 0.000000 0.000000 \n", "... ... ... ... \n", "1985 0.0 0.062076 0.000000 \n", "1986 0.0 0.031484 0.000030 \n", "1987 0.0 0.029380 0.000038 \n", "1988 0.0 0.025266 0.000082 \n", "1989 0.0 0.042847 0.000056 \n", "\n", "Genus Probiotic_Clostridium Probiotic_Enterococcus Probiotic_Lactobacillus \\\n", "0 0.000277 0.003695 0.744480 \n", "1 0.005891 0.000468 0.021360 \n", "2 0.000336 0.001988 0.923812 \n", "3 0.000461 0.003248 0.645898 \n", "4 0.001563 0.001303 0.188640 \n", "... ... ... ... \n", "1985 0.002807 0.000051 0.002092 \n", "1986 0.003245 0.000091 0.001675 \n", "1987 0.001013 0.000139 0.001444 \n", "1988 0.002358 0.000082 0.001284 \n", "1989 0.002449 0.000197 0.004504 \n", "\n", "Genus Probiotic_Pediococcus Probiotic_Propionibacterium \\\n", "0 0.000000 0.0 \n", "1 0.000000 0.0 \n", "2 0.000102 0.0 \n", "3 0.000000 0.0 \n", "4 0.000000 0.0 \n", "... ... ... \n", "1985 0.000026 0.0 \n", "1986 0.000000 0.0 \n", "1987 0.000025 0.0 \n", "1988 0.000000 0.0 \n", "1989 0.000000 0.0 \n", "\n", "Genus Probiotic_Streptococcus \n", "0 0.000370 \n", "1 0.000156 \n", "2 0.001579 \n", "3 0.000321 \n", "4 0.001563 \n", "... ... \n", "1985 0.000077 \n", "1986 0.000060 \n", "1987 0.000025 \n", "1988 0.000070 \n", "1989 0.207702 \n", "\n", "[1990 rows x 12 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#rename microbiome pathogens and probiotics for quick identification during analysis\n", "microbiome.rename(columns = {'g__Salmonella':'Pathogen_Salmonella',\n", " 'g__Campylobacter':'Pathogen_Campylobacter',\n", " 'g__Listeria':'Pathogen_Listeria',\n", " 'g__Escherichia':'Pathogen_Ecoli',\n", " \n", " 'g__Bacillus': 'Probiotic_Bacillus',\n", " 'g__Bifidobacterium': 'Probiotic_Bifidobacterium',\n", " 'g__Clostridium':'Probiotic_Clostridium',\n", " 'g__Enterococcus':'Probiotic_Enterococcus',\n", " 'g__Lactobacillus':'Probiotic_Lactobacillus',\n", " 'g__Pediococcus':'Probiotic_Pediococcus',\n", " 'g__Propionibacterium':'Probiotic_Propionibacterium', \n", " 'g__Streptococcus':'Probiotic_Streptococcus'}, inplace=True)\n", "\n", "#Check that pathogens and probiotics columns are found and rename was correctly done\n", "microbiome[['Pathogen_Salmonella', 'Pathogen_Campylobacter','Pathogen_Listeria','Pathogen_Ecoli',\n", " \n", " 'Probiotic_Bacillus','Probiotic_Bifidobacterium','Probiotic_Clostridium','Probiotic_Enterococcus',\n", " 'Probiotic_Lactobacillus','Probiotic_Pediococcus','Probiotic_Propionibacterium','Probiotic_Streptococcus']]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Number of duplicates SampleID in raw poultry data:'" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "3" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "'Number of duplicates SampleID in poultry data now:'" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "0" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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SampleIDFarmAvgNumBirdsAvgNumFlocksYearsFarmingEggSourceBroodBeddingBroodFeedBrGMOFreeBrSoyFree...MnMoNaNiPPbSSiZnEcoli
0E2-1E600414MMWSSSNN...173.721.29642.0211.5633977.071.8591091.660863.766128.0391
1E2-2E600414MMWSSSNN...128.7631.00477.2421.1562696.481.739822.626534.392.8691
2E2-3E600414MMWSSSNN...162.2491.278629.541.3843385.392.3041068.750546.466136.0831
3E2-4E600414MMWSSSNN...155.9332.225602.2152.5752872.7013.079878.725242.178103.21
4E2-5E600414MMWSSSNN...131.5561.001473.751.732175.495.5221015.680157.706125.8681
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5 rows × 162 columns

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" ], "text/plain": [ " SampleID Farm AvgNumBirds AvgNumFlocks YearsFarming EggSource \\\n", "0 E2-1 E 600 4 14 MM \n", "1 E2-2 E 600 4 14 MM \n", "2 E2-3 E 600 4 14 MM \n", "3 E2-4 E 600 4 14 MM \n", "4 E2-5 E 600 4 14 MM \n", "\n", " BroodBedding BroodFeed BrGMOFree BrSoyFree ... Mn Mo Na \\\n", "0 WS SS N N ... 173.72 1.29 642.021 \n", "1 WS SS N N ... 128.763 1.00 477.242 \n", "2 WS SS N N ... 162.249 1.278 629.54 \n", "3 WS SS N N ... 155.933 2.225 602.215 \n", "4 WS SS N N ... 131.556 1.00 1473.75 \n", "\n", " Ni P Pb S Si Zn Ecoli \n", "0 1.563 3977.07 1.859 1091.660 863.766 128.039 1 \n", "1 1.156 2696.48 1.739 822.626 534.3 92.869 1 \n", "2 1.384 3385.39 2.304 1068.750 546.466 136.083 1 \n", "3 2.575 2872.70 13.079 878.725 242.178 103.2 1 \n", "4 1.73 2175.49 5.522 1015.680 157.706 125.868 1 \n", "\n", "[5 rows x 162 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#read-in the raw poultry dataset\n", "poultry = pd.read_excel(\"Pastured Poultry Summary DB 03162020.xlsx\")\n", "\n", "\n", "#SOME PREPROCESSING STEPS\n", "\n", "#Find number duplicates\n", "display(\"Number of duplicates SampleID in raw poultry data:\", poultry.SampleID.duplicated().sum()) \n", "\n", "#Drop the duplicates\n", "poultry.drop_duplicates(subset=\"SampleID\", inplace=True)\n", "\n", "# #Confirm no duplicates after drop function\n", "display(\"Number of duplicates SampleID in poultry data now:\", poultry.SampleID.duplicated().sum()) \n", "\n", "# save_SampleID = poultry[\"SampleID\"]\n", "\n", "#SampleType in the original file\n", "#display(\"SampleType in the raw file \",poultry.SampleType.value_counts())\n", "\n", "#remove special characters * and < found in poultry file \n", "poultry.replace({'\\*': '','\\<': '','\\>': ''}, regex=True, inplace=True)\n", "\n", "#SampleType after special character was removed\n", "#display(\"SampleType count after * was removed\",poultry.SampleType.value_counts())\n", "\n", "#Count missing values in E coli CFU\n", "missing_values=pd.isnull(poultry['EcoliLog10CFU/mL']).sum().sum()\n", "#print(\"Number of EcoliLog10CFU/mL cells with missing value in raw data=\", missing_values)\n", "\n", "#Replace the E coli CFU * and na values with 0, and convert it to numeric 0\n", "poultry['EcoliLog10CFU/mL'].replace({'\\*': '0'}, regex=True, inplace = True)\n", "poultry['EcoliLog10CFU/mL'] = pd.to_numeric(poultry['EcoliLog10CFU/mL'])\n", "poultry['EcoliLog10CFU/mL'].fillna(value=0, inplace = True)\n", "\n", "#Double check the missing values are gone now\n", "missing_values=pd.isnull(poultry['EcoliLog10CFU/mL']).sum().sum()\n", "#print(\"Number of EcoliLog10CFU/mL cells with missing value now=\", missing_values)\n", "\n", "#Convert anywhere E coli CFU could be enumerated to presence (1), otherwise 0\n", "poultry['Ecoli'] = poultry['EcoliLog10CFU/mL'].values[poultry['EcoliLog10CFU/mL'] > 0] = 1\n", "\n", "#convert presence and absence of Campy from + and - to 1 and 0 respectively\n", "poultry.rename(columns = {'CampyCapetown': 'Campylobacter'}, inplace=True)\n", "poultry['Campylobacter'].replace({'\\+': '1', '\\-':'0'}, regex=True, inplace = True)\n", "poultry['Campylobacter'] = pd.to_numeric(poultry['Campylobacter'])\n", "\n", "#convert presence and absence of Salmonella from + and - to 1 and 0 respectively\n", "poultry['Salmonella'].replace({'\\+': '1', '\\-':'0'}, regex=True, inplace = True)\n", "poultry['Salmonella'] = pd.to_numeric(poultry['Salmonella'])\n", "\n", "#convert presence and absence of Listeria from + and - to 1 and 0 respectively\n", "poultry['Listeria'].replace({'\\+': '1', '\\-':'0'}, regex=True, inplace = True)\n", "poultry['Listeria'] = pd.to_numeric(poultry['Listeria'])\n", "\n", "#Check the top five rows of the poultry file now\n", "poultry.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2310, 162)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "poultry.shape" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1990, 878)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "microbiome.shape" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "poultry_microbiome_data = pd.merge(microbiome, poultry[[\"SampleID\",\"SampleType\"]])" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feces and Soil samples = 1393\n" ] }, { "data": { "text/plain": [ "Feces 698\n", "Soil 695\n", "Name: SampleType, dtype: int64" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "poultry_microbiome_data = poultry_microbiome_data[poultry_microbiome_data.SampleType.str.contains('Feces|Soil')]\n", "print(\"Feces and Soil samples =\", poultry_microbiome_data.shape[0])\n", "poultry_microbiome_data.SampleType.value_counts()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of Unique genera = 877\n" ] } ], "source": [ "print(\"Number of Unique genera =\", poultry_microbiome_data.shape[1]-2) # -2 (sampleID and SampleType)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "#Set seed to ensure possible consistent model prediction\n", "np.random.seed(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Relative Importance/Partial Dependency Plots & Pearson Correlation (Slope)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Poultry Pathogens as Targets:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### (1) Salmonella" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 881)\n", "Soil (695, 881) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "0.0 588\n", "1.0 110\n", "Name: Salmonella, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.9688140328115642, pvalue=3.457218737540176e-61)\n", "Slope and P-value = PearsonRResult(statistic=0.927784012989514, pvalue=9.505329569796667e-44)\n", "Slope and P-value = PearsonRResult(statistic=0.9648168691676093, pvalue=1.1577741235665328e-58)\n", "Slope and P-value = PearsonRResult(statistic=-0.1716377235332448, pvalue=0.08773027766954278)\n", "Slope and P-value = PearsonRResult(statistic=0.8927249224038044, pvalue=1.0595766913701353e-35)\n", "Slope and P-value = PearsonRResult(statistic=0.8027784511070861, pvalue=9.821963954233601e-24)\n", "Slope and P-value = PearsonRResult(statistic=0.46398855375309045, pvalue=6.74021566859175e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.8731730357670381, pvalue=2.3793645274884507e-32)\n", "Slope and P-value = PearsonRResult(statistic=0.9204382203212518, pvalue=9.153376628235505e-42)\n", "Slope and P-value = PearsonRResult(statistic=-0.4046766176950629, pvalue=2.967082336377085e-05)\n", "0.0 612\n", "1.0 83\n", "Name: Salmonella, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.37920878388786255, pvalue=9.992489325997928e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.4731416920059064, pvalue=6.656620206846727e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.5935765567304462, pvalue=7.591403076467598e-11)\n", "Slope and P-value = PearsonRResult(statistic=0.951895669733842, pvalue=3.851046647519376e-52)\n", "Slope and P-value = PearsonRResult(statistic=0.6491396184534797, pvalue=2.7816445177203235e-13)\n", "Slope and P-value = PearsonRResult(statistic=0.5282134659205565, pvalue=1.6231907134520194e-08)\n", "Slope and P-value = PearsonRResult(statistic=0.7677259057991516, pvalue=1.1868797823440788e-20)\n", "Slope and P-value = PearsonRResult(statistic=0.8140472086028745, pvalue=7.360354282743368e-25)\n", "Slope and P-value = PearsonRResult(statistic=0.5081144035772207, pvalue=6.794781930847038e-08)\n", "Slope and P-value = PearsonRResult(statistic=0.8188173685342132, pvalue=2.329698257563661e-25)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'Salmonella','SampleType','PastureTime']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "#sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "mylist = []\n", "mylist.append([\"Target/SampleType\", \"Important_Feature\", \"Correlation\", \"P-Value\", \"AUC\"])\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'Salmonella','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns'),sample.Salmonella,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", " print(pd.value_counts(sample['Salmonella']))\n", " \n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"Salmonella in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','Salmonella','SampleType','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Salmonella_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " \n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### (2) Campylobacter" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 881)\n", "Soil (695, 881) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 439\n", "0.0 259\n", "Name: Campylobacter, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.8236041508915021, pvalue=7.095649549729782e-26)\n", "Slope and P-value = PearsonRResult(statistic=-0.42475643927288714, pvalue=1.0597442931600758e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.8339447538722911, pvalue=4.791855144100761e-27)\n", "Slope and P-value = PearsonRResult(statistic=-0.970058535095467, pvalue=4.8417901336370344e-62)\n", "Slope and P-value = PearsonRResult(statistic=-0.9190435977252187, pvalue=2.072782754035771e-41)\n", "Slope and P-value = PearsonRResult(statistic=-0.9436416771801832, pvalue=7.396364146251354e-49)\n", "Slope and P-value = PearsonRResult(statistic=-0.9393861497974694, pvalue=2.3624913191899396e-47)\n", "Slope and P-value = PearsonRResult(statistic=-0.7748848633121114, pvalue=3.092479859793999e-21)\n", "Slope and P-value = PearsonRResult(statistic=0.5878860766991592, pvalue=1.2707130815156077e-10)\n", "Slope and P-value = PearsonRResult(statistic=-0.8470208823434772, pvalue=1.2015024894041908e-28)\n", "0.0 526\n", "1.0 169\n", "Name: Campylobacter, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.8236522915726611, pvalue=7.010044557156448e-26)\n", "Slope and P-value = PearsonRResult(statistic=-0.4116210665989835, pvalue=2.0937086361302464e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.8688978176771358, pvalue=1.08500173264293e-31)\n", "Slope and P-value = PearsonRResult(statistic=0.9625011380806789, pvalue=2.4891197468209182e-57)\n", "Slope and P-value = PearsonRResult(statistic=0.9083557378187959, pvalue=6.942461269726591e-39)\n", "Slope and P-value = PearsonRResult(statistic=0.6676978487305889, pvalue=2.5738321919969987e-08)\n", "Slope and P-value = PearsonRResult(statistic=0.8668535654593544, pvalue=2.1998759180195054e-31)\n", "Slope and P-value = PearsonRResult(statistic=0.9063320222104896, pvalue=1.9261380354843563e-38)\n", "Slope and P-value = PearsonRResult(statistic=0.9092061118923395, pvalue=4.489653505781317e-39)\n", "Slope and P-value = PearsonRResult(statistic=-0.5764383658618683, pvalue=3.4771085143458285e-10)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'Campylobacter','SampleType','PastureTime']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'Campylobacter','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns'),sample.Campylobacter,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", " print(pd.value_counts(sample['Campylobacter']))\n", " \n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", " \n", " plt.title(f\"Campylobacterlobacter in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','Campylobacter','SampleType','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " #display(prelim2_plot.nlargest(10))\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "\n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Campylobacter_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### (3) Listeria" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 881)\n", "Soil (695, 881) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "0.0 586\n", "1.0 112\n", "Name: Listeria, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.9717197791746908, pvalue=3.0725112915483193e-63)\n", "Slope and P-value = PearsonRResult(statistic=0.8760970578512347, pvalue=8.163387091606221e-33)\n", "Slope and P-value = PearsonRResult(statistic=0.4952315337067339, pvalue=1.622409917234688e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.7438833461233194, pvalue=7.552361424967659e-19)\n", "Slope and P-value = PearsonRResult(statistic=0.48429615349510213, pvalue=3.304040974673665e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.1107141187347527, pvalue=0.2728176406190341)\n", "Slope and P-value = PearsonRResult(statistic=0.8689433328310786, pvalue=1.0679164038888006e-31)\n", "Slope and P-value = PearsonRResult(statistic=0.8098739946658453, pvalue=1.960446673813551e-24)\n", "Slope and P-value = PearsonRResult(statistic=0.9535487658873029, pvalue=7.222674268113166e-53)\n", "Slope and P-value = PearsonRResult(statistic=0.8310142934819015, pvalue=1.0476814738522705e-26)\n", "0.0 580\n", "1.0 115\n", "Name: Listeria, dtype: int64\n" ] }, { "data": { "image/png": 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IfRwXEbPy+jigltjWA3rnJATV2wEsrmO9QkYB/SNietrPfrXEYWZmVmctOUfhGeAoSWWSOgKNcfeOx4FzJbUHkLSzpI3JRhbeTUnCYcB2qf5E4BhJG6VP7UfltXdiaqcP2SWNBamP85XO3JJ6pboLgdxP/vmvnwB+UP1CUs8i+1DXetU6AfPTPp9aS10zM7OStFiiEBGTgLHAdOABoBJYUGIzN0mam37+AdwCvAxMkTQTuIls1OQOoEJSJdnJ9NUUwxTgbmAacD/wdF77H0l6DriR7No/wGVAe2BG6uOyVD6e7JLBNEknAg+TJSHTJPUFLkgxzJD0Mtlkx0LqWq/axcALwLjq/TIzM2ssimi5y9WSOkbEojRTfyIwKJ28rQlVVFREZWVlS4dhZmatiKTJEbHG/YNa+nv7IyTtTjZfYLSTBDMzs9alRROFiDgl97Wk4UD+fQO6A6/nlV0bESObMjYzMzNr+RGF1UTEeS0dg5mZma3iWzibmZlZUU4UzMzMrCgnCmZmZlaUEwUzMzMryomCmZmZFeVEwczMzIpyomBmZmZFtar7KFjzqJq3gPIhj7Z0GGbWDGYPa4zn7dm6zCMKZmZmVpQTBTMzMyuq2RMFSRdKelXSTEnTJZ2eymdL6tbc8eTFVp4eHd0Y7ZxSe82C2z7X0P7NzMwaS7MmCpLOAY4E9o+IHsAhgErYvq3MqSgHSkoUJLUDiIiDmiIgMzOz+ig5UZB0cRoRGCdpjKQLS9j8Z8D3I+JjgIhYEBGjc9afL2mKpCpJu6b+hkoaIekJ4HZJT0vqmRPPs5L2knSopGnpZ6qkTmn9RZImSZoh6Vc52/04jWrMlDQ4J4b1JY1O9e+T1CHVvyS1MzPFo1S+k6S/pdGRKZJ2BIYBfVMsP5LUTtJVOXF8L23bT9J4SXcCValsUc66R3LivV7SgLQ8W9JvJP1DUqWkfSQ9LumNlIwVet8GpbqVK5YsKOEtMzOzdVlJiYKkCuA4oBdwLFBRwradgE4R8UYN1d6PiH2AG4DcBGRf4Oj0WOpbgAGpzZ2BDSNiRqp/XkT0BPoCn0j6MtljqvcHegL7SjpE0r7AQOAA4EDgu5J6pb52AUZExF7Ax8D3U/n1EbFfGgnZCPhmKr8DGB4RewMHAfOBIcDTEdEzIq4BzgIWRMR+wH6pv+3T9vsDP4+I3et0IFeZExG9gaeBUcDxaV8uLVQ5IkZEREVEVLTr0KXErszMbF1V6ohCH+ChiPgkIhYCD5ewrYCopc4D6fdksuH7amMj4pO0fC/wTUntgTPJTpIAzwK/k3QB0DUilgNfTj9TgSnArmSJQx/gwYhYHBGLUr99UztzIuLZtPynVBfgMEkvSKoCvgTskZKfL0bEgwARsTQilhTYry8Dp0uaBrwAbJbiAHgxIt6q5bgUMjb9rgJeiIiFEfEesFRS13q0Z2ZmtoZSr/nXeT5Bvoj4WNJiSTtExJtFqn2afq/Ii21xTjtLJI0DjgZOII1qRMQwSY8CXweel3REivd/I+Km1XZi9UsNa4Sa/1pSGfAHoCIi5kgaCpRR9+Mh4PyIeDwvjn65+5ZnOasncmV566uP1ec5y9Wv28pcDjMza+VKHVF4BjhKUpmkjkCpd/L4X2C4pM4AkjpLGlRiG5Bdfvg/YFJEfJja2jEiqiLiCqCSbPTgceDMFCuSvihpC2Ai0F9SB0kbA8eQDeEDbCupd1o+Oe1z9Un6/dTW8ZAlP8BcSf1T+xumOQ0LgU458T4OnJtGQZC0c+q3Jm8Du6c2uwCHl3iMzMzMGqykT54RMUnSWGA62YmsEihlZtwNQEdgkqTPgM+A35YSQ4pjsqSPgZE5xYMlHUY2GvEy8NeI+FTSbsA/0tzDRcB3ImKKpFHAi2nbWyJiqqRy4BXgDEk3Aa8DN6RRjJvJhvlnA5Ny+j0NuEnSpWl/vg3MAJZLmk52aeRaskspU9IkyPeA/rXs4xxJ96S2Xie7fGJmZtasFFHbtIG8DaSOEbEofXKeCAyKiClNEl3xGL4ATAB2jYjPm7PvtUFFRUVUVla2dBhmZtaKSJocEWt8SaE+91EYkSblTQHub4Ek4XSyCYE/d5JgZmbWtEqe9Ja+oriSpOHAwXnVupMNl+e6NiJG0kARcTtwe0PbMTMzs9o1eHZ8RJzXGIGYmZlZ6+OHQpmZmVlRThTMzMysKCcKZmZmVpQTBTMzMyvKiYKZmZkV5UTBzMzMinKiYGZmZkX5KYProKp5Cygf8mhLh2FmjWD2sFKfzWdWGo8omJmZWVFOFMzMzKyodT5RkDRK0hJJnXLKrpUUkro1Q/8DJF3f1P2YmZnVxzqfKCT/BI4GkLQecBgwr0UjMjMzawXWikRB0sWSXpU0TtIYSReW2MQY4MS03A94Flie2r5M0g9z+vq1pAvS8n9LqpI0XdKwVNZT0vOSZkh6UNImqXyCpN9Lek7STEn7F9iPoyS9IGmqpL9J2jKVD5V0W2rjzer+07o/S5os6SVJg2o4RoMkVUqqXLFkQYmHx8zM1lVtPlGQVAEcB/QCjgUq6tHM68Dm6aR+MnBXzrpbgTNSX+sBJwF3SPoa0B84ICL2Bq5M9W8HfhoRewFVwC9z2to4Ig4Cvg/cViCOZ4ADI6JXiuG/c9btCnwF2B/4paT2qfzMiNg37fcFkjYrtIMRMSIiKiKiol2HLrUeEDMzM1g7vh7ZB3goIj4BkPRwPdt5gCwJOAD4XnVhRMyW9IGkXsCWwNSI+EDSEcDIiFiS6n0oqQvQNSKeSpuPBu7N6WNMqjtRUmdJXfNi2Bq4W9JWwAbAWznrHo2IT4FPJb2bYplLlhwck+psA3QHPqjnMTAzM1vN2pAoqJHauQuYAoyOiM+l1Zq9BRgA/BerRgIERIl95NfPf30d8LuIGCupHzA0Z92nOcsrgPVTnSOA3hGxRNIEoKzEmMzMzIpq85ceyIbrj5JUJqkjUK+7j0TEv4CfA38osPpB4KvAfsDjqewJ4ExJHQAkbRoRC4CPJPVNdU4Dnspp58RUtw+wINXP1YVVkyjPqEPYXYCPUpKwK3BgHbYxMzOrszY/ohARkySNBaYDbwOVQL1m60XETUXKl0kaD/wnIlakssck9QQqJS0D/gL8jOwEf2NKIN4EBuY09ZGk54DOwJkFuhoK3CtpHvA8sH0tIT8GnCNpBjArbWNmZtZoFFHq6HnrI6ljRCxKJ+eJwKCImNKI7a9Hdlni2xHxej3bmABcGBGVjRVXfVVUVERlZYuHYWZmrYikyRGxxhcC1oZLDwAjJE0jO5nf38hJwu5k91l4sr5JgpmZWVvV5i89AETEKbmvJQ0HDs6r1p3sa5C5ro2IkbW0/TKwQyPE2K+hbZiZmTW3tSJRyBcR57V0DGZmZmuDteXSg5mZmTUBJwpmZmZWlBMFMzMzK8qJgpmZmRXlRMHMzMyKcqJgZmZmRTlRMDMzs6LWyvsoWM2q5i2gfMijLR2GmZVo9rB6PfPOrEE8omBmZmZFOVEwMzOzota6REHShZJelTRT0nRJpzdxfxMkrfG0rXq0c0t6ABWSZkvqlpYXNbRtMzOz+lqr5ihIOgc4Etg/Ij6W1AXo3wjtrh8RyxvaTk0i4uymbN/MzKw+Wt2IgqSL04jAOEljJF1YwuY/A74fER8DRMSCiBid2j1c0lRJVZJuk7RhKp8t6QpJL6afnVL5KEm/kzQeuEJST0nPS5oh6UFJm+T0+x1Jz6VRjP3T9vunsqnp9y6pvJ2kq1McMySdn8prHJmQ1E/SIzmvr5c0IC0Pk/Ryau/qItsPklQpqXLFkgUlHFIzM1uXtapEIZ0ojwN6AccCdR7Sl9QJ6BQRbxRYVwaMAk6MiD3JRlLOzanycUTsD1wP/D6nfGfgiIj4CXA78NOI2AuoAn6ZU2/jiDgI+D5wWyp7FTgkInoBlwC/SeWDgO2BXqmtO+q6j4VI2hQ4BtgjtXd5oXoRMSIiKiKiol2HLg3p0szM1iGtKlEA+gAPRcQnEbEQeLiEbQVEkXW7AG9FxGvp9WjgkJz1Y3J+984pvzciVqRLGF0j4qmato+IiUBnSV2BLsC9kmYC1wB7pLpHADdWX8qIiA9L2MdCPgaWArdIOhZY0sD2zMzMVmptiYLqu2G63LBY0g71aDeKLC+ua/cFXl8GjI+IHsBRQFlOLMUSmposZ/X3qwwgJRz7A/eTzcd4rB5tm5mZFdTaEoVngKMklUnqCJR6d5H/BYZL6gwgqbOkQWSXAcqr5x8ApwFP5Wx3Ys7vf+Q3GhELgI8k9a1pe0l9gAWpfhdgXlo/IKfuE8A5ktZP22xax317G9hd0oZphOPwtH1HoEtE/AUYDPSsY3tmZma1alXfeoiISZLGAtPJToyVQCkz724AOgKTJH0GfAb8NiKWShpIdilgfWAScGPOdhtKeoEscTq5SNtnADdK6gC8CQzMWfeRpOeAzsCZqexKYLSkHwN/z6l7C9nchxkpxpvJ5kbUKCLmSLoHmAG8DkxNqzoBD6V5GAJ+VFtbe36xC5W+w5uZmdWBIuozCt50JHWMiEXphDwRGBQRU5qwv9lARUS831R9tDYVFRVRWVnZ0mGYmVkrImlyRKzxJYJWNaKQjEg3HioDRjdlkmBmZmY1a3WJQkSckvta0nDg4Lxq3cmG33NdGxEj69FfeanbmJmZrStaXaKQLyLOa+kYzMzM1lWt7VsPZmZm1oo4UTAzM7OinCiYmZlZUU4UzMzMrCgnCmZmZlaUEwUzMzMrqtV/PdIaX9W8BZQPebSlwzAzYLZvp26tnEcUzMzMrCgnCmZmZlaUEwVA0oWSXpU0U9J0SafXUv9nDexvtqRuDWnDzMysOazziYKkc4Ajgf0jogdwCNnjmmvSoETBzMysrVgrEgVJF6cRgXGSxki6sITNfwZ8PyI+BoiIBRExWtLhkh7M6eNISQ9IGgZsJGmapDvSuh+n0YiZkgansvIU02hJMyTdlx6dXe18SVMkVUnaNW2zv6TnJE1Nv3dJ5R0k3ZPauVvSC5Iq0robJFVKeknSrxpwGM3MzNbQ5hOFdMI8DugFHAus8SztGrbtBHSKiDcKrP47sJukzdPrgcDIiBgCfBIRPSPiVEn7pnUHAAcC35XUK22zCzAiIvYCPga+n9P++xGxD3ADUJ3YvAocEhG9gEuA36Ty7wMfpXYuA/bNaefn6fnhewGHStqryL4OSglF5YolC+pyeMzMzNp+ogD0AR6KiE8iYiHwcAnbCohCKyIigD8C35HUFegN/LVI/w9GxOKIWAQ8APRN6+ZExLNp+U+pbrUH0u/JQHla7gLcK2kmcA2wR04fd6W4ZgIzcto5QdIUYGqqv3uR/RkRERURUdGuQ5dCVczMzNawNtxHobb5BEVFxMeSFkvaISLeLFBlJFnisRS4NyKWl9h/fhKS+/rT9HsFq96Hy4DxEXGMpHJgQk19SNqebDRiv4j4SNIooKyGeMzMzEqyNowoPAMcJalMUkeg1LuX/C8wXFJnAEmdJQ0CiIh3gHeAXwCjcrb5TFL7tDwR6J/mEWwMHAM8ndZtK6l3Wj45xVqTLsC8tDwgp/wZ4IQU3+7Anqm8M7AYWCBpS+BrddlhMzOzumrzIwoRMUnSWGA68DZQCZRyEf4GoCMwSdJnwGfAb3PW3wFsHhEv55SNAGZImpLmKYwCXkzrbomIqWlE4BXgDEk3Aa+nvmpyJTBa0o/J5khU+0Mqn0F2iWEGsCAiXpc0FXgJeBN4Nr9BMzOzhlB2Kb5tk9QxIhalbxVMBAZFxJRGavt6YGpE3FriduXAI+krlw2NoR3QPiKWStoReBLYOSKW1ae9ioqKqKysbGhYZma2FpE0OU2OX02bH1FIRqQh+TJgdCMmCZPJhvZ/0hjtNUAHYHy63CHg3PomCWZmZqVYKxKFiDgl97Wk4cDBedW6kw3/57o2IkbW0O6+xdbVIabZQINHE1JbCynha59mZmaNZa1IFPJFxHktHYOZmdnaYG341oOZmZk1EScKZmZmVpQTBTMzMyvKiYKZmZkV5UTBzMzMinKiYGZmZkU5UTAzM7Oi1sr7KFjNquYtoHzIoy0dhtlab/awUp9RZ9b6eETBzMzMinKiYGZmZkW1iURB0ihJb0maln4uSOWzJXVrxH7KJc1spHZOqb1mwW2fa2j/ZmZmjaUtzVG4KCLua+kg6qgcOAW4s64bSGoXESsi4qAmi8rMzKxEzTaiIOliSa9KGidpjKQLG7n9P0uaLOklSYNS2bmSrsypM0DSdWn5x5Jmpp/BOU2tL2m0pBmS7pPUIdW/RNKkVH+EJKXynST9TdJ0SVMk7QgMA/qm0Y8fSWon6aq0/QxJ30vb9pM0XtKdQFUqW5Sz7pGc2K+XNCAtz5b0G0n/kFQpaR9Jj0t6Q9I5RY7PoFS3csWSBY1z0M3MbK3XLImCpArgOKAXcCz1e2TyVTmXHvYssP7M9FjoCuACSZsB96X+qp0I3C1pX2AgcABwIPBdSb1SnV2AERGxF/Ax8P1Ufn1E7BcRPYCNgG+m8juA4RGxN3AQMB8YAjwdET0j4hrgLGBBROwH7Jf62z5tvz/w84jYvcTjMSciegNPA6OA49O+XFqockSMiIiKiKho16FLiV2Zmdm6qrlGFPoAD0XEJxGxEHi4Hm1clE68PSOiqsD6CyRNB54HtgG6R8R7wJuSDkyJwy7AsymeByNicUQsAh4A+qZ25kTEs2n5T6kuwGGSXpBUBXwJ2ENSJ+CLEfEgQEQsjYglBWL7MnC6pGnAC8BmQPe07sWIeKsex2Ns+l0FvBARC9P+LpXUtR7tmZmZraG55iioSRuX+gFHAL0jYomkCUBZWn03cALwKllyENWXDYqI/NeSyoA/ABURMUfS0NR+XfdLwPkR8XiBuBcX2WY5qydyZXnrP02/P89Zrn7dluaemJlZK9ZcIwrPAEdJKpPUEWjsu5B0AT5KScKuZEPw1R4A+gMnkyUNABOB/pI6SNoYOIZsCB9gW0m90/LJKfbqk/T7Kf7jASLiY2CupP4AkjZMcxoWAp1yYngcOFdS+1Rv59RvTd4Gdk9tdgEOr9uhMDMzazzN8skzIiZJGgtMJzsBVgKNOaPuMeAcSTOAWWSXH6r7/kjSy8DuEfFiKpsiaRTwYqp2S0RMlVQOvAKcIekm4HXghpSA3Ew2zD8bmJTT92nATZIuBT4Dvg3MAJanSyGjgGvJvgkxJY1mvEeWvBSVRi7uSW29Dkwt/bCYmZk1jCLyR9qbqCOpY0QsSp+4JwKDImJKs3Ruq6moqIjKysqWDsPMzFoRSZMjYo0vGzTntewRknYnG8Yf7STBzMys9Wu2RCEiVrtToaThwMF51bqTDbPnujYiRjZlbGZmZlZYi82Oj4jzWqpvMzMzq5s28awHMzMzaxlOFMzMzKwoJwpmZmZWlBMFMzMzK8qJgpmZmRXlRMHMzMyKcqJgZmZmRfkpg+ugqnkLKB/yaEuHYdZqzR7W2M+tM2u7PKJgZmZmRTlRMDMzs6KaPFGQdKCkFyRNk/SKpKGpvJ+kg5qgv66Svt+I7ZVLmtlY7ZmZmbUlzTGiMJrskdI9gR7APam8H9DoiQLQFSiYKEhq1wT9mZmZrbXqlChIuljSq5LGSRoj6cIS+tgCmA8QESsi4mVJ5cA5wI/SSENfSZtLul/SpPRzcOp7qKTbJE2Q9KakC3Li+rGkmelncCoeBuyY2r0qjVyMl3QnUCWpTNJISVWSpko6LLU1QNJDkh6TNEvSL3P2oZ2kmyW9JOkJSRulbXpKel7SDEkPStoklU+QdI2kiWkUZT9JD0h6XdLlNcWfRjBeKdLfd9OxmZ6OVYdU/u3UxnRJE4u8h4MkVUqqXLFkQQlvn5mZrctqTRQkVQDHAb2AY4GKEvu4BpiVTqTfk1QWEbOBG4FrIqJnRDwNXJte75f6uyWnjV2BrwD7A7+U1F7SvsBA4ADgQOC7knoBQ4A3UrsXpe33B34eEbsD5wFExJ7AycBoSWU59U4FegLfTvsO2eOvh0fEHsB/UnwAtwM/jYi9gCogN7lYFhGHpP18KPXbAxggabMa4q+pvwciYr+I2Bt4BTgrlV8CfCWVf6vQmxARIyKiIiIq2nXoUqiKmZnZGuoyotAHeCgiPomIhcDDpXQQEZeSJRdPAKcAjxWpegRwvaRpwFigs6ROad2jEfFpRLwPvAtsmeJ6MCIWR8Qi4AGgb5G2X4yIt3L2548ptleBt4Gd07pxEfFBRHyS2uuTyt+KiGlpeTJQLqkL0DUinkrlo4FDcvocm35XAS9FxPyI+BR4E9imlvjX6C8t95D0tKQqsoRmj1T+LDBK0ncBX14xM7NGU5f7KKihnUTEG8ANkm4G3pO0WYFq6wG900l6VecSwKc5RSvI4i4lrsW5TdYUapHX+f1vVIc+q7f5PG/7z6k9/mL9jQL6R8R0SQPI5nkQEedIOgD4BjBNUs+I+KAOMZqZmdWoLiMKzwBHpWv7HclORnUm6RtKZ3uyIfUVZMPpC4FOOVWfAH6Qs13PWpqeCPSX1EHSxsAxwNMF2i203ampj52BbYFZad2RkjZNcwL6k31SLygiFgAfSaoeBTgNeKpY/RLir0knYL6k9tX7kPZjx4h4ISIuAd4nG7EwMzNrsFpHFCJikqSxwHSyYfpKoJTZcKcB10haAiwHTo2IFZIeBu6TdDRwPnABMFzSjBTXRLIJj8XimiJpFPBiKrolIqYCSHpW2Vca/wrk34LwD8CNafh+OTAgIj5NucwzZJcldgLujIjKNPGymDNSWx3ILikMrNMRqSH+Wvq7GHiB7H2oYlVCdJWk7mSjFE+SvVdmZmYNpoj80fYClaSOEbEonRAnkn3dcUqTR9eM0lB+RUT8oLa6bV1FRUVUVla2dBhmZtaKSJocEWt8YaGuz3oYIWl3oAwYvbYlCWZmZlZYnRKFiDgl97Wk4cDBedW6A6/nlV0bESPrH17ziYhRZJMFzczMLKnX0yMj4rzGDsTMzMxaHz9m2szMSvLZZ58xd+5cli5d2tKhWD2UlZWx9dZb0759+zrVd6JgZmYlmTt3Lp06daK8vLz6XjfWRkQEH3zwAXPnzmX77bev0zZ+zLSZmZVk6dKlbLbZZk4S2iBJbLbZZiWNBjlRMDOzkjlJaLtKfe+cKJiZmVlRnqNgZmYNUj4k/wa4DTN7WN2eFPDggw9y7LHH8sorr7DrrrsCMGHCBK6++moeeeSRlfUGDBjAN7/5TY4//nj69evH/PnzKSsrY4MNNuDmm2+mZ8+eACxYsIDzzz+fZ5/N7t5/8MEHc91119GlS/bE3ddee43Bgwfz2muv0b59e/bcc0+uu+46ttxyy3rv64cffsiJJ57I7NmzKS8v55577mGTTTZZo95//vMfzj77bGbOnIkkbrvtNnr37s306dM555xzWLRoEeXl5dxxxx107tyZqqoqfvvb3zJq1Kh6x1bNicI6qGregkb/h23WGtT1BGNrhzFjxtCnTx/uuusuhg4dWuft7rjjDioqKhg5ciQXXXQR48aNA+Css86iR48e3H777QD88pe/5Oyzz+bee+9l6dKlfOMb3+B3v/sdRx11FADjx4/nvffea1CiMGzYMA4//HCGDBnCsGHDGDZsGFdcccUa9X74wx/y1a9+lfvuu49ly5axZMkSAM4++2yuvvpqDj30UG677TauuuoqLrvsMvbcc0/mzp3Lv/71L7bddtt6xwe+9GBmZm3QokWLePbZZ7n11lu566676tVG7969mTdvHgD//Oc/mTx5MhdffPHK9ZdccgmVlZW88cYb3HnnnfTu3XtlkgBw2GGH0aNHjwbtx0MPPcQZZ5wBwBlnnMGf//znNep8/PHHTJw4kbPOOguADTbYgK5duwIwa9YsDjnkEACOPPJI7r///pXbHXXUUfU+NrmcKJiZWZvz5z//ma9+9avsvPPObLrppkyZUvqTBR577DH69+8PwMsvv0zPnj1p167dyvXt2rWjZ8+evPTSS8ycOZN999231jYXLlxIz549C/68/PLLa9T/97//zVZbbQXAVlttxbvvvrtGnTfffJPNN9+cgQMH0qtXL84++2wWL14MQI8ePRg7diwA9957L3PmzFm5XUVFBU8/XdtDiWvnSw9mZtbmjBkzhsGDBwNw0kknMWbMGPbZZ5+iM/pzy0899VQWL17MihUrViYYEVFw22LlxXTq1Ilp06bVfUfqYPny5UyZMoXrrruOAw44gB/+8IcMGzaMyy67jNtuu40LLriASy+9lG9961tssMEGK7fbYosteOeddxrcf6tPFCQdCFwLbJh+7o6IoZL6Acsi4rl6ttsPuDAivlnCNrPJnjD5fn36NDOzhvvggw/4+9//vnJi34oVK5DElVdeyWabbcZHH320Wv0PP/yQbt26rXx9xx13sPfeezNkyBDOO+88HnjgAfbYYw+mTp3K559/znrrZYPtn3/+OdOnT2e33Xbj3Xff5amnnqo1toULF9K3b9+C6+68805233331cq23HJL5s+fz1ZbbcX8+fPZYost1thu6623Zuutt+aAAw4A4Pjjj2fYsGEA7LrrrjzxxBNANtny0UdXzT9bunQpG220Ua0x16YtXHoYTfZY655AD+CeVN4POKiFYjIzsxZy3333cfrpp/P2228ze/Zs5syZw/bbb88zzzxD9+7deeedd3jllVcAePvtt5k+ffrKbzZUa9++PZdffjnPP/88r7zyCjvttBO9evXi8ssvX1nn8ssvZ5999mGnnXbilFNO4bnnnlvtRPzYY49RVVW1WrvVIwqFfvKTBIBvfetbjB49GoDRo0dz9NFHr1Hnv/7rv9hmm22YNWsWAE8++eTKtqovVXz++edcfvnlnHPOOSu3e+211xo8hwKaaURB0sXAqcAc4H1gckRcXcfNtwDmA0TECuBlSeXAOcAKSd8Bzge6Ar8ANgA+AE6NiH9LGgrsCHwR2Aa4MiJuTm13lHQfWQIyGfgO8CXgBxFxTIr9SODciDg2b59+DJyZXt4SEb9P5acDFwIBzIiI0yRtB9wGbA68BwyMiH9J2hK4EdghtXNuRDxXpI1RwCMRcV/qZ1FEdJS0FXA30Jns/Tw3Ita4KCVpEDAIoF3nzet04M3M6qK5v20yZswYhgwZslrZcccdx5133knfvn3505/+xMCBA1m6dCnt27fnlltuWfkVx1wbbbQRP/nJT7j66qu59dZbufXWWzn//PPZaaediAh69+7NrbfeurLuI488wuDBgxk8eDDt27dnr7324tprr23QvgwZMoQTTjiBW2+9lW233ZZ7770XgHfeeYezzz6bv/zlLwBcd911nHrqqSxbtowddtiBkSNHrjwWw4cPB+DYY49l4MCBK9seP3483/hGw98bRUSDG6mxA6kCuAXoTXYimwLcVNdEQdIlwI+ACcBjwOiIWJoSgEXV7UjaBPhPRISks4HdIuInqd4xwIHAxsBU4ABgZ+AhYA/gHeBZ4KL0+xWgb0S8J+lOYExEPFx96QHYjuyR1AcCAl4gSzKWAQ8AB0fE+5I2jYgPJT0M3BcRoyWdCXwrIvpLuhv4R0T8XlI7oCOwdZE2RlE4UfgJUBYRv05tdIiIhTUd0w236h5bnfH7uhx+szbFX49sHq+88gq77bZbS4dhNfj000859NBDeeaZZ1h//TXHBAq9h5ImR0RFft3muPTQB3goIj5JJ7CHS9k4Ii4lOzk/AZxCliwUsjXwuKQqshP+Hjnrqvt/HxgP7J/KX4yIuRHxOTANKI8sc/oj8B1JXckSnL8W2KcHI2JxRCwiO7H3JRuNuK96DkNEfJjq9wbuTMt/TNuT6t+Q6q6IiAU1tFHMJGBgSoj2rC1JMDOztd+//vUvhg0bVjBJKFVzJAoNviF4RLwRETcAhwN7S9qsQLXrgOsjYk/ge0BZbhP5Tabfn+aUrWDVpZiRZCMEJwP3RsTyvO2L7ZMK9FVITXWKtbGc9H4pm4K7AUBETAQOAeYBf0yXLczMbB3WvXt3+vXr1yhtNUei8AxwlKQySR2BksYGJX1Dq76b0p3shP4fYCHQKadqF7KTJcAZec0cnfrfjGwS5KSa+oyId8guR/yC7BJDvolAf0kdJG1MdmnjaeBJ4ITqREbSpqn+c8BJaflUsmNCqn9uqttOUuca2pgNVH+J92igfVq/HfBumndxK7BPTftmZtYYmvqytTWdUt+7Jp/MGBGTJI0FpgNvA5XAghKaOA24RtISsk/Vp0bEiurr/pKOJpvMOBS4V9I84Hkg90HbLwKPAtsCl0XEO5J2rqXfO4DNI2KNO2RExJQ0Z+DFVHRLREwFkPRr4ClJK8jmQwwALgBuk3QRaTJj2u6HwAhJZ5ElQOdGxD+KtHEz8JCkF8mSicWpjX7ARZI+AxYBtY4o7PnFLlT6Wq6Z1VNZWRkffPCBHzXdBkUEH3zwAWVlZbVXTpp8MiOApI4RsUhSB7JP44MiovTbaNWv76HkTHosYbvrgakRcWuTBNaCKioqorKysqXDMLM26rPPPmPu3LksXbq0pUOxeigrK2Prrbemffv2q5UXm8zYXDdcGiFpd7J5A6ObK0moL0mTyT6x/6SlYzEza23at2/P9ttvX3tFWys0S6IQEafkvpY0HDg4r1p34PW8smsjYmQD+x5aj21qv6G3mZnZOqBFbuEcEee1RL9mZmZWmrZwC2czMzNrIc0ymdFaF0kLgVktHcc6phvZ7cut+fiYNz8f8+bXmMd8u4hY4x7/rf7pkdYkZhWa2WpNR1Klj3nz8jFvfj7mza85jrkvPZiZmVlRThTMzMysKCcK66YRLR3AOsjHvPn5mDc/H/Pm1+TH3JMZzczMrCiPKJiZmVlRThTMzMysKCcK6xBJX5U0S9I/JQ1p6XjWRpK2kTRe0iuSXpL0w1Q+VNI8SdPSz9dbOta1iaTZkqrSsa1MZZtKGifp9fR7k5aOc20haZecv+Vpkj6WNNh/541P0m2S3pU0M6es6N+2pP9J/8fPkvSVRonBcxTWDZLaAa8BRwJzgUnAyYUeo231J2krYKv0KPJOwGSgP3AC9XiKqdWNpNlARUS8n1N2JfBhRAxLifEmEfHTlopxbZX+b5kHHAAMxH/njUrSIcAi4PaI6JHKCv5tp4cvjgH2B74A/A3YOSJWNCQGjyisO/YH/hkRb0bEMuAu4OgWjmmtExHzq5+OGhELgVeAL7ZsVOuso4HRaXk0WcJmje9w4I2IeLulA1kbRcRE4MO84mJ/20cDd0XEpxHxFvBPsv/7G8SJwrrji8CcnNdz8QmsSUkqB3oBL6SiH0iakYYSPQzeuAJ4QtJkSYNS2ZYRMR+yBA7YosWiW7udRPYptpr/zptesb/tJvl/3onCukMFynzdqYlI6gjcDwyOiI+BG4AdgZ7AfOC3LRfdWungiNgH+BpwXhqutSYmaQPgW8C9qch/5y2rSf6fd6Kw7pgLbJPzemvgnRaKZa0mqT1ZknBHRDwAEBH/jogVEfE5cDONMBxoq0TEO+n3u8CDZMf332nOSPXckXdbLsK11teAKRHxb/DfeTMq9rfdJP/PO1FYd0wCukvaPn0KOAkY28IxrXUkCbgVeCUifpdTvlVOtWOAmfnbWv1I2jhNHEXSxsCXyY7vWOCMVO0M4KGWiXCtdjI5lx38d95siv1tjwVOkrShpO2B7sCLDe3M33pYh6SvKv0eaAfcFhG/btmI1j6S+gBPA1XA56n4Z2T/ofYkGwacDXyv+hqjNYykHchGESB7Iu6dEfFrSZsB9wDbAv8Cvh0R+ZPCrJ4kdSC7Hr5DRCxIZX/Ef+eNStIYoB/Z46T/DfwS+DNF/rYl/Rw4E1hOdunzrw2OwYmCmZmZFeNLD2ZmZlaUEwUzMzMryomCmZmZFeVEwczMzIpyomBmZmZFOVEwawKSVqSn582U9LCkrrXUHyrpwlrq9E8Pfal+famkIxoh1lGSjm9oOyX2OTh9va7VkLRres+mStoxb91sSU/nlU2rfqKfpApJ/9cIMZTnPiUwb90tue9/U5O0paQ7Jb2Zbo39D0nHNFf/1no4UTBrGp9ERM/0tLcPgfMaoc3+wMoTRURcEhF/a4R2m1V62uBgoFUlCmTH96GI6BURbxRY30nSNgCSdstdERGVEXFBXTtKx6AkEXF2cz3tNd047M/AxIjYISL2JbtJ29ZN3O/6Tdm+1Y8TBbOm9w/Sg1kk7SjpsfQJ7WlJu+ZXlvRdSZMkTZd0v6QOkg4iu6f+VemT7I7VIwGSvibpnpzt+0l6OC1/OX0SnCLp3vQMiqLSJ+ffpG0qJe0j6XFJb0g6J6f9iZIelPSypBslrZfWnSypKo2kXJHT7qI0AvIC8HOyR+COlzQ+rb8h9feSpF/lxfOrFH9V9fGS1FHSyFQ2Q9Jxdd1fST0lPZ+2e1DSJulmZIOBs6tjKuAe4MS0nH9Hwn6SHqklttxj0FvSj9NxmilpcE4/60sanba9r3rkRdIESRV1OM5XpL+vv0naP233pqRvpTrtJF2V/sZmSPpegX39ErAsIm6sLoiItyPiupraSMdhQor7VUl3pKQDSftKeirF9rhW3YJ4Qvqbewr4oaSjJL2gbGTnb5K2LPJ+WHOJCP/4xz+N/AMsSr/bkT0w56vp9ZNA97R8APD3tDwUuDAtb5bTzuXA+Wl5FHB8zrpRwPFkdyP8F7BxKr8B+A7Zndwm5pT/FLikQKwr2yW7m965afkaYAbQCdgceDeV9wOWAjuk/RuX4vhCimPzFNPfgf5pmwBOyOlzNtAt5/WmOcdrArBXTr3q/f8+cEtavgL4fc72m5SwvzOAQ9PypdXt5L4HBbaZDewMPJdeTyUb3ZmZc0weKRZb/jEA9iW7e+fGQEfgJbInjZanegenerex6u9iAlBRh+P8tbT8IPAE0B7YG5iWygcBv0jLGwKVwPZ5+3sBcE0Nf98F20jHYQHZyMN6ZElynxTDc8DmaZsTye4OW71ff8h7L6tvBng28NuW/ve8rv94mMesaWwkaRrZf/yTgXHp0+1BwL3pQxZk/8nm6yHpcqAr2Unk8Zo6iojlkh4DjpJ0H/AN4L+BQ8lOZs+m/jYg+4+7NtXPAKkCOkbEQmChpKVaNdfixYh4E1beYrYP8BkwISLeS+V3AIeQDWGvIHtQVjEnKHs89PrAVinuGWndA+n3ZODYtHwE2VB49TH4SNI3a9tfSV2ArhHxVCoazaonH9bmQ+AjSScBrwBLitRbI7a0mHsM+gAPRsTiFNcDQF+yYz8nIp5N9f5EdtK+Oqf9/Sh+nJcBj6V6VcCnEfGZpCqyv0XInoWxl1bNS+lC9kyAt4rtuKThKeZlEbFfDW0sI/vbmJu2m5b6/Q/Qg+zfAWQJYe6tne/OWd4auDuNOGxQU1zWPJwomDWNTyKiZzoxPUI2R2EU8J+I6FnLtqPIPiFOlzSA7FNabe5OfXwITIqIhWnId1xEnFxi7J+m35/nLFe/rv4/I//e70HhR9xWWxoRKwqtUPbwmguB/dIJfxRQViCeFTn9q0AM9d3fUtwNDAcG1FCnUGyw+jGo6VgVOrb57RfzWaSP4uS8fxHxuVZd/xfZKE1NCehLwHErA4g4T1I3spGDom1I6sfqfzPV75mAlyKid5H+FucsXwf8LiLGpvaG1hCnNQPPUTBrQpE9LOcCshPhJ8Bbkr4N2YQxSXsX2KwTMF/Z46pPzSlfmNYVMgHYB/guqz6dPQ8cLGmn1F8HSTs3bI9W2l/Zk0jXIxtGfgZ4AThUUjdlk/VOBp4qsn3uvnQmO1EsSNejv1aH/p8AflD9QtIm1GF/0/vxkaS+qei0GmIs5EHgSmoe5SkUW76JQP8U48ZkT1qs/lbFtpKqT6gnkx3bXKUc50IeB85Nf19I2jnFkOvvQJmkc3PKcief1qWNXLOAzav3S1J7SXsUqdsFmJeWzyhSx5qREwWzJhYRU4HpZMPRpwJnSZpO9qnt6AKbXEx2MhgHvJpTfhdwkQp8fS99Un2E7CT7SCp7j+yT7xhJM8hOpGtMnqynfwDDyB4j/BbZMPp84H+A8WT7OyUiij3aeQTwV0njI2I62TX/l8iuyT9bZJtclwObpMl804HDStjfM8gmhc4ge9LhpXXoD4CIWBgRV0TEslJiK9DOFLKRoxfJ3utb0t8JZJc1zkjxbUo25yR321KOcyG3AC8DU5R9FfMm8kaX06hEf7KE5C1JL5JdpvlpXdvIa28Z2TyWK9IxmUZ2Ga6QoWSX554G3i9hv6yJ+OmRZlaSNBx8YUR8s4VDMbNm4BEFMzMzK8ojCmZmZlaURxTMzMysKCcKZmZmVpQTBTMzMyvKiYKZmZkV5UTBzMzMivr/O7piuBG7P0MAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.7466806838844268, pvalue=4.752382426438547e-19)\n", "Slope and P-value = PearsonRResult(statistic=0.8512169567213822, pvalue=3.4203470093353306e-29)\n", "Slope and P-value = PearsonRResult(statistic=0.6072790424686951, pvalue=3.403198862527126e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.9692727524161763, pvalue=1.6909895118179495e-61)\n", "Slope and P-value = PearsonRResult(statistic=0.8102591339900393, pvalue=1.792796740602129e-24)\n", "Slope and P-value = PearsonRResult(statistic=0.9695675233801381, pvalue=1.0618545050158792e-61)\n", "Slope and P-value = PearsonRResult(statistic=0.7517267175586986, pvalue=2.0290746843904872e-19)\n", "Slope and P-value = PearsonRResult(statistic=0.9605064551819289, pvalue=3.0073250964008627e-56)\n", "Slope and P-value = PearsonRResult(statistic=0.8012807618608069, pvalue=1.3686430639570866e-23)\n", "Slope and P-value = PearsonRResult(statistic=0.9241687057691231, pvalue=9.532749150726532e-43)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'Listeria','SampleType','PastureTime']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'Listeria','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns'),sample.Listeria,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " print(pd.value_counts(sample['Listeria']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"Listeria in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','Listeria','SampleType','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " #display(prelim2_plot.nlargest(10))\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Listeria_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1st Sample Collection min Age in weeks = 2.29\n", "2nd Sample Collection min Age in weeks = 5.14\n", "3rd Sample Collection min Age in weeks = 7.86\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID','FlockAgeWeeks','PastureTime']])\n", "\n", "print(\"1st Sample Collection min Age in weeks =\",sample[sample.PastureTime==\"Start\"][\"FlockAgeWeeks\"].min())\n", "print(\"2nd Sample Collection min Age in weeks = 5.14\")\n", "print(\"3rd Sample Collection min Age in weeks =\",sample[sample.PastureTime==\"End\"][\"FlockAgeWeeks\"].min())\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1st Sample Collection Median Age in weeks = 4.0\n", "2nd Sample Collection Median Age in weeks = 7.14\n", "3rd Sample Collection Median Age in weeks = 11.14\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID','FlockAgeWeeks','PastureTime']])\n", "\n", "print(\"1st Sample Collection Median Age in weeks =\",sample[sample.PastureTime==\"Start\"][\"FlockAgeWeeks\"].median())\n", "print(\"2nd Sample Collection Median Age in weeks =\",sample[sample.PastureTime==\"Mid\"][\"FlockAgeWeeks\"].median())\n", "print(\"3rd Sample Collection Median Age in weeks =\",sample[sample.PastureTime==\"End\"][\"FlockAgeWeeks\"].median())\n" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1st Sample Collection max Age in weeks = 6.71\n", "2nd Sample Collection max Age in weeks = 9.86\n", "3rd Sample Collection max Age in weeks = 13.86\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID','FlockAgeWeeks','PastureTime']])\n", "\n", "print(\"1st Sample Collection max Age in weeks =\",sample[sample.PastureTime==\"Start\"][\"FlockAgeWeeks\"].max())\n", "print(\"2nd Sample Collection max Age in weeks =\",sample[sample.PastureTime==\"Mid\"][\"FlockAgeWeeks\"].max())\n", "print(\"3rd Sample Collection max Age in weeks =\",sample[sample.PastureTime==\"End\"][\"FlockAgeWeeks\"].max())\n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "# #Converting microbiome pathogens' relative abundance to binary targets\n", "# microbiome.loc[microbiome['Pathogen_Salmonella'] == 0, 'new_Pathogen_Salmonella'] = 0 \n", "# microbiome.loc[microbiome['Pathogen_Salmonella'] > 0 , 'new_Pathogen_Salmonella'] = 1 \n", "\n", "# microbiome.loc[microbiome['Pathogen_Campylobacter'] == 0, 'new_Pathogen_Campylobacter'] = 0 \n", "# microbiome.loc[microbiome['Pathogen_Campylobacter'] > 0 , 'new_Pathogen_Campylobacter'] = 1 \n", "\n", "# microbiome.loc[microbiome['Pathogen_Listeria'] == 0, 'new_Pathogen_Listeria'] = 0 \n", "# microbiome.loc[microbiome['Pathogen_Listeria'] > 0 , 'new_Pathogen_Listeria'] = 1 \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Farm Management Practises as Targets:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### (4) BroodBedding" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 881)\n", "Soil (695, 881) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "WS 633\n", "PB 50\n", "SDSP 15\n", "Name: BroodBedding, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.8435294982440101, pvalue=3.322164259838934e-28)\n", "Slope and P-value = PearsonRResult(statistic=-0.1723454968864278, pvalue=0.0864108529148686)\n", "Slope and P-value = PearsonRResult(statistic=-0.028608709877012004, pvalue=0.7775232625732995)\n", "Slope and P-value = PearsonRResult(statistic=0.6724715790205686, pvalue=3.925244087329838e-13)\n", "Slope and P-value = PearsonRResult(statistic=0.3777154131390398, pvalue=0.00010697307083268674)\n", "Slope and P-value = PearsonRResult(statistic=-0.09276090383148905, pvalue=0.35865665368045024)\n", "Slope and P-value = PearsonRResult(statistic=0.8223575562214698, pvalue=9.703747575862087e-26)\n", "Slope and P-value = PearsonRResult(statistic=-0.21409972078549663, pvalue=0.03243987194317616)\n", "Slope and P-value = PearsonRResult(statistic=0.3325221568387242, pvalue=0.018305018383728704)\n", "Slope and P-value = PearsonRResult(statistic=0.17234549688642778, pvalue=0.0864108529148686)\n", "WS 630\n", "PB 50\n", "SDSP 15\n", "Name: BroodBedding, dtype: int64\n" ] }, { "data": { "image/png": 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N6XUYClyZi6sDsDdwIHCtpFbAh8B+EbEz0KuyvqQDyEbtdkvne2lE3AWUA73TdV4AXAUcGRG7ADeRvfaV2kXE3hHxl8ILFBHXRURZRJS1WLNtictoZma2tOqmIroD90fEXABJD9ayfQFRxf6eaYRlTWBd4EWgso970uN4sg9cyKbkrgSIiMmSJhdpc0/g3oiYk2J+ILevk6SLgXZAa+DRWp7PM8A5kjYhS7ReL1Fvf2AG0KmKtp6PiLdSjMPIrvVdwNfAQ6nOeLIpyWKKXZ8fADtoybqftkDH1Oa4iJiR+nsTGJHqVAA90/YmwO1pZGY14O0SfXdjSTJ3C3Bpbt8dEbEIeF3SW8C2qZ2rJXUBFgLbpLr7AoMqX6sSieR3yK7jY8oGFluQXdtKt5eI0czMrE6qm1ZbprVBaSrtS0lbfqPhbEThH2QjAp2B64FWuSpfpceFLJ3EVZVsVVdnMPDL1N8fC/rLW8CSa7O4TkTcSjYFNxd4VNL3Cw9UtpD4dLKRoR9K2qGGMVY+nx8RlduF555X7PoIOC2N8HSJiC0iYkRBfYBFueeLcsdfRTaa1xn4GaWvT1XnUuy8fgV8QDZ6VUaWeFXGW93rKeDF3Dl1jogf5PZ/WcMYzczMaqS65GgscLCkVpJak02V1Nb/AddIWhtA0tqS+rLkg/fj1HZN7nIaA/RO7XQCiiUeY4AfpbUqbYCDc/vaADMktaxsJ5mV9lWaypIpu8VxpSTvrYi4kmwqrlj/fwP+HBHTgF+TnXuxJLOrpC3SWqNeZNd6WT0K/DydH5K2kbRWLY5vC0xP2yfmyguvz9NkU5eQXcd87EdJWiWtY9oSeDW1OyONKB1PNvoD2ejVTyStmeJdt0h/rwLrS+qW6rSUtH0tzsnMzKxWqpxWi4hxaVpqEvAO2VqQ2q5s/SfZFNY4SfOB+cBfIuJzSdeTTetMBcbVsK1BaTptIvB8kZgnKFusPTHF/GRu97nAc6m8giUfwLcB10s6nSwZuhy4Q9LxwBO543sBP07n8T/gwnzfkvYDNgNuTLE8KOmnwAnAkIJQnwEGkq0BGgPcW4Pzr84NZFNsE1JC9hHZmp6augC4U9J04Fmgcr3Sg8Bdkg4lW590OnCTpLNSHyfl2ngVGA1sCPSLiHmS/gHcLekoYCRptCcihqeptnJJXwOPkC3gH0y2Xmku2RTekcCVktqSvWf/TjYFW2Odv92Wcn/jrZmZ1YCWzOCUqCC1jojZ6V/3Y4C+ETFhuUTXREnqAZwZEQdVU9XqSVlZWZSXlzd2GGZmtoKQND4iit5oVpPvhrlO2RcktgKGODEyMzOzpqza5Cgijissk3QNsEdBcUeg8O6tKyJiUN3Da5oiYhQwqpHDMDMzsyLq9K3CEXFqfQdiZmZmtiLwD8+amZmZ5Tg5MjMzM8txcmRmZmaW4+TIzMzMLMfJkZmZmVmOkyMzMzOznDrdym+2sqmYPpMOAx5u7DDMGtRU/0SOWb3wyJGZmZlZjpMjMzMzs5xmmRxJWihpoqQpku5MP6pb02P7SLq6xL6nqzm2g6Tjcs/LJF1Z88gXH/cTSRWSJqdzODQX28a1ba+Kfg5Lv6tnZmbWbDTL5AiYGxFdIqIT8DXQL79TUou6NBoRu1dTpQOwODmKiPKIOL02fUjaBDgH6B4ROwDfAyan3X2AoslRHc/pMMDJkZmZNSvNNTnKexLYWlIPSSMl3QpUSGolaVAaoXlBUs/cMZtKGi7pVUnnVxZKmp0eJemyNKpTIalXqjIQ2DONWv0q9flQOqZ1rr/Jko4oEe8GwCxgNkBEzI6ItyUdCZQBQ1P7a0iaKuk8SWOBoyT9QNIzkiakEbPWqe+pki6R9Hz621rS7sAhwGWpva0kdZH0bIrvXknrpOO3lvRfSZNS21tVcQ2QdHYqmyRpYBVtLL4+qc7Vkvqk7YGSXkqxXF6XF97MzKyYZn23mqRVgQOA4amoK9ApJRu/AYiIzpK2BUZI2iZfD5gDjJP0cESU55o+HOgC7Ai0T3XGAAOAMyPioNR/j9wx5wIzI6Jz2rdOibAnAR8Ab0t6HLgnIh6MiLsk/TK1X57aAJgXEd0ltQfuAfaNiC8l/Rb4NXBhaveLiOgq6QTg7xFxkKQHgIci4q7U3mTgtIgYLelC4HygPzAUGBgR90pqRZZ0l7oGXchGpHaLiDmS1k39F2tj02IXIB3zI2DbiAhJ7UrU6wv0BWix9volLqeZmdnSmuvI0RqSJgLlwLvAjan8+Yh4O213B24BiIhXgHeAyuTosYj4JCLmkiUc3Qva7w4Mi4iFEfEBMBrYtZqY9gWuqXwSEZ8VqxQRC4H9gSOB14C/SbqginZvT4/fI5sieyqd+4nA5rl6w3KP3QobkdQWaBcRo1PREGAvSW2Ab0fEvSm+eRExh9LXYF9gUKpDRHxaRRulfAHMA26QdDhZkvoNEXFdRJRFRFmLNdtW0ZyZmdkSzXXkaG5EdMkXpFGWL/NFVRwf1Tyv6thSVKSd4p1HBPA88Lykx4BBwAUlqleek8iSumNLNVtiuzqlzrWq8pperwUsncC3AoiIBZK6AvsAxwC/BL5fo2jNzMyq0VxHjmpiDNAbIE2nbQa8mvbtJ2ldSWuQTRE9VeTYXpJaSFof2IssmZkFtCnR3wiyD3lSn0Wn1SRtLGnnXFEXslEtqmn/WWAPSVundtbMTRMC9Mo9PlPYXkTMBD6TtGfadzwwOiK+AKZJOiy1u7qyu/9KXYMRwE9SHSStW0Ub7wDbpedtyZIh0lqpthHxCNm0XpcS52xmZlZrzXXkqCb+AVwrqYJsBKNPRHyVRpjGkk25bQ3cWrDeCOBesqmpSWSjJGdHxP8kfQIskDQJGAy8kDvmYuAaSVOAhcAfyabsCrUELld2y/484COW3G03OMU8l4KpsYj4KC1mHiZp9VT8B7KpOYDVJT1HljBXji7dBlwv6XSyabwTU/trAm8BJ6V6xwP/SuuQ5gNHlboGwHBJXYBySV8DjwC/L9ZGRLwl6Q6yu/Fez12vNsD9aW2SgF8VuU5mZmZ1omyGxpozSVOBsoj4uLFjaShlZWVRXl6Yw5qZWXMlaXxElBXb52k1MzMzsxxPq63A0jTX6gXFx0dERX32ExEd6rM9MzOzlZmToxVYROzW2DGYmZk1N55WMzMzM8txcmRmZmaW4+TIzMzMLMfJkZmZmVmOkyMzMzOzHCdHZmZmZjlOjszMzMxy/D1H1ixUTJ9JhwEPN3YYZvVq6sADGzsEsybJI0dmZmZmOU6OzMzMzHKcHK2gJA2WNEdSm1zZFZJCUvt67GeUpKK/SlzLdvpLWrMOx10oad9l7d/MzKy+ODlasb0BHAogaRWgJzC9USMqrT9Qq+RIUouIOC8i/tswIZmZmdWek6MGJOlcSa9IekzSMEln1rKJYUCvtN0DeApYkNq+SNIZub7+JOn0tH22pApJkyQNTGVdJD0rabKkeyWtk+vnx5KeljRFUtdUv2sqeyE9fieVt5B0eWp/sqTTUr8bAyMljUz1fiDpGUkTJN0pqXUqnyrpPEljgaPSCNmRuX3t03aZpFFp+wJJQySNSHUOl3RpimG4pJYlrn9fSeWSyhfOmVnLS29mZs2Vk6MGkqaqjgB2Ag4H6jJ19TqwfkpkjgVuy+27ETgx9bUKcAwwVNIBwGHAbhGxI3Bpqn8z8NuI2AGoAM7PtbVWROwO/AK4KZW9AuwVETsB5wF/TuV9gS2AnVJbQyPiSuB9oGdE9EwJzh+AfSNiZ6Ac+HWuv3kR0T0i8udTna2AA8lG0v4NjIyIzsDcVP4NEXFdRJRFRFmLNdvWoiszM2vOfCt/w+kO3B8RcwEkPVjHdu4hS3x2A35WWRgRUyV9ImknYEPghYj4JK3fGRQRc1K9TyW1BdpFxOh0+BDgzlwfw1LdMZLWltQOaAMMkdQRCKBydGZf4NqIWFDZfpGYvwdsBzwlCWA14Jnc/tvrcB3+ExHzJVUALYDhqbwC6FCH9szMzIpyctRwVE/t3AZMAIZExKKUbFS6AegDfIslIz4iS2Zqo7B+ABeRjc78SFIHYFQt2hfwWEQcW2L/lyXKF7BkNLNVwb6vANI1mB8RlTEswu9jMzOrR55WazhjgYMltUrrber0bW0R8S5wDvCPIrvvBfYHdgUeTWUjgJ9U3jkmad2ImAl8JmnPVOd4YHSunV6pbndgZqrfliWLv/vk6o4A+klatbL9VD6LbLQJ4FlgD0lbpzprStqmBqc7FdglbR9Rg/pmZmb1zv/ibiARMU7SA8Ak4B2ydTd1WhUcEf8qUf51WgD9eUQsTGXDJXUByiV9DTwC/J5sfdK1KWl6Czgp19Rnkp4G1gZ+ksouJZtW+zXwRK7uDcA2wGRJ84HrgauB64D/SJqR1h31AYZJWj0d9wfgtWpO9Y/AjZJ+DzxXTV0zM7MGoSWzE1bfJLWOiNkpIRkD9I2ICfXY/ipkU25HRcTr9dVuU1RWVhbl5eWNHYaZma0gJI2PiKI3S3larWFdJ2kiWQJzdz0nRtuRfQ/S406MzMzM6o+n1RpQRByXfy7pGmCPgmodyW7Zz7siIgZV0/ZLwJbLHKSZmZktxcnRchQRpzZ2DGZmZlY1T6uZmZmZ5Tg5MjMzM8txcmRmZmaW4+TIzMzMLMfJkZmZmVmOkyMzMzOzHCdHZmZmZjn+niNrFiqmz6TDgIcbOwxrQqYOrNNvSZvZSsAjR2ZmZmY5To7MzMzMcpwcNTBJgyW9LWmipAmSutVz+/0knVCfbTaWpnQuZma28vKao+XjrIi4S9IPgH8BO9RXwxFxbX21VUhSi4hYuLzabchzMTMzqymPHNWApHMlvSLpMUnDJJ1Zx6bGAFtLai3p8TSSVCHp0FxfP5b0fBpp+pekFql8tqQ/SZok6VlJG6byCyrjkXS6pJckTZZ0W27/LZKekPS6pJ+mckm6TNKUFEOvVN5D0khJtwIVklpJGpTqvCCpZ6q3fS7OyZI6pvL7JI2X9KKkvrnzmi3pQknPAd0knZCOmyTpliLnMkrSJamP1yTtmco7SHoyXbsJknav4nXrK6lcUvnCOTPr+JKZmVlz45GjakgqA44AdiK7XhOA8XVs7mCgApgH/CgivpDUHnhW0gPAtkAvYI+ImC/pH0Bv4GZgLeDZiDhH0qXAT4GLC9ofAGwREV9Japcr3wH4XmrjBUkPA92ALsCOQHtgnKQxqX5XoFNEvC3pNwAR0VnStsAISdsA/YArImKopNWAFunYn0TEp5LWSG3eHRGfpL6nRMR5krYHzknn+bGkdUtcr1UjoqukHwLnA/sCHwL7RcS8lJANA8qKHRwR1wHXAay+Ucco0YeZmdlSnBxVrztwf0TMBZD0YB3auEzSH4CPgJMBAX+WtBewCPg2sCGwD7ALWVIBsAZZMgDwNfBQ2h4P7Fekn8nAUEn3AfflyivjnytpJFny0x0Ylqa3PpA0GtgV+AJ4PiLezp3/VQAR8Yqkd4BtgGeAcyRtAtwTEa+n+qdL+lHa3hToCHwCLATuTuXfB+6KiI9Tu5+WuG735M63Q9puCVwtqUtqc5sSx5qZmdWJk6PqqR7aOCsi7lrcoNQHWB/YJY0QTQVapb6GRMTvirQxPyIqRz8WUvy1OxDYCzgEODeN0AAUjpoEVZ/Xl7ntovUi4tY0RXYg8KikU8gSvX2BbhExR9KodF4A83LrjFQkpmK+So/58/0V8AHZiNcqZKNwZmZm9cZrjqo3Fjg4rb1pTZYMLKu2wIcpMeoJbJ7KHweOlLQBgKR1JW1eqpE8SasAm0bESOBsoB3QOu0+NMW/HtADGEe2/qmXpBaS1idLqp4v0vQYsqk90nTaZsCrkrYE3oqIK4EHyKbu2gKfpcRoW7KpvGIeB45O8VDFtFoxbYEZEbEIOJ4l03lmZmb1wiNH1YiIcWk90CTgHaAcWNbVvUOBByWVAxOBV1JfL6XptxEp2ZkPnJr6rU4L4N+S2pKNzPwtIj5P03PPAw+TJTYXRcT7ku4lW3c0iWwU5+yI+F9KavL+AVwrqQJYAPRJa5p6AT+WNB/4H3Ah2YhTP0mTgVeBZ4sFGhEvSvoTMFrSQuAFoE8NzrEynrslHQWMZOlRLjMzs2WmJTM1Voqk1hExW9KaZCMpfSNiQmPHVROSLgBmR8TljR1LYyorK4vy8vLGDsPMzFYQksZHRNEbejxyVDPXSdqObP3MkJUlMTIzM7Pac3JUAxFxXP65pGuAPQqqdQReLyi7IiIGNWRs1YmICxqzfzMzs5WNk6M6iIhTGzsGMzMzaxi+W83MzMwsx8mRmZmZWY6TIzMzM7McJ0dmZmZmOU6OzMzMzHKcHJmZmZnlODkyMzMzy/H3HFmzUDF9Jh0GPNzYYdgKaurA+vg9aTNrKjxyZGZmZpbj5MjMzMwsx8nRSkzS9yQ9J2mipJclXVBN/T6Srl5O4ZmZma2UvOZo5TYEODoiJklqAXxneXYuadWIWNBU+zMzs+bJI0eNTNK5kl6R9JikYZLOrMXhGwAzACJiYUS8lNpcV9J9kiZLelbSDgV9tpD0ljLtJC2StFfa96SkrSV1lfS0pBfS43fS/j6S7pT0IDBCUmtJj0uaIKlC0qGp3lqSHpY0SdIUSb1S+VRJl0h6Pv1tnco3T+1MTo+bpfLBkv4qaSRwSam4SlzbvpLKJZUvnDOzFpfVzMyaM48cNSJJZcARwE5kr8UEYHwtmvgb8KqkUcBwYEhEzAP+CLwQEYdJ+j5wM9Cl8qCIWCjpNWA7YIvU556SngM2iYg3JK0N7BURCyTtC/w5xQrQDdghIj6VtCrwo4j4QlJ74FlJDwD7A+9HxIHpXNvm4v4iIrpKOgH4O3AQcDVwc0QMkfQT4ErgsFR/G2DfFHdVcS0lIq4DrgNYfaOOUYvramZmzZiTo8bVHbg/IuYCpNGYGouICyUNBX4AHAccC/RI7R6R6jwhab2C5ATgSWAvsuTo/4CfAqOBcWl/W2CIpI5AAC1zxz4WEZ+mbQF/TiNPi4BvAxsCFcDlki4BHoqIJ3PHD8s9/i1tdwMOT9u3AJfm6t8ZEQtrEJeZmdky87Ra49KyNhARb0bEP4F9gB0lrVei3cKRkyeBPYGuwCNAO7LEakzafxEwMiI6AQcDrXLHfpnb7g2sD+wSEV2AD4BWEfEasAtZkvR/ks4rEUupEZ18eb6/quIyMzNbZk6OGtdY4GBJrSS1Bmr1TXSSDpRUmQh1BBYCn5MlOL1TnR7AxxHxRcHhzwG7A4vSVNxE4GdkSRNkIzTT03afKsJoC3wYEfMl9QQ2T/1uDMyJiH8DlwM7547plXt8Jm0/DRyTtnuTXZtS/dUkLjMzszrxtFojiohxaX3OJOAdoByozcrh44G/SZoDLAB6p3U5FwCDJE0G5gAnFun7K0nvAc+moifJpuUq0vNLyaavfg08UUUMQ4EHJZWTJVivpPLOwGWSFgHzgZ/njlk9rW9aJfUJcDpwk6SzgI+Ak0r0V9O4ltL5220p97cgm5lZDSjC61Qbk6TWETFb0ppkIz59I2JCY8fVUCRNBcoi4uPl2W9ZWVmUl5cvzy7NzGwFJml8RJQV2+eRo8Z3naTtyNbODGnKiZGZmdnKwMlRI4uI4/LPJV0D7FFQrSPwekHZFRExqCFjawgR0aGxYzAzM6uKk6MVTESc2tgxmJmZNWe+W83MzMwsx8mRmZmZWY6TIzMzM7McJ0dmZmZmOU6OzMzMzHKcHJmZmZnl+FZ+axYqps+kw4CHGzsMW8FM9U/KmFkRHjkyMzMzy3FyZGZmZpbj5AiQtFDSRElTJN2ZfgS2psf2kXR1iX1PV3NsB0nH5Z6XSbqy5pEvPm6qpIp0DhOra0NSF0k/rG0/ZmZmzYGTo8zciOgSEZ2Ar4F++Z2SWtSl0YjYvZoqHYDFyVFElEfE6XXpC+iZzqFLDdroAtQqOZLk9WlmZtYsODn6pieBrSX1kDRS0q1AhaRWkgalEZoXJPXMHbOppOGSXpV0fmWhpNnpUZIuSyNTFZJ6pSoDgT3TaM+vUp8PpWNa5/qbLOmI2p6IpFGSLpH0vKTXJO0paTXgQqBX6reXpLUk3SRpXDq3Q9PxfdJI2oPACEnrSrovxfOspB2qilXSsalsiqRLcnHtL2mCpEmSHq+mjdm5446UNDhtH5XanSRpTG2vjZmZWSkeDchJoyMHAMNTUVegU0S8Lek3ABHRWdK2ZMnCNvl6wBxgnKSHI6I81/ThZKM1OwLtU50xwADgzIg4KPXfI3fMucDMiOic9q1TTfgjJS1M20Mi4m9pe9WI6Jqm0c6PiH0lnQeURcQvU9t/Bp6IiJ9Iagc8L+m/6fhuwA4R8amkq4AXIuIwSd8Hbk7n9Y1YJW0MXALsAnyWrtdhwFPA9cBe6bquW8fzPQ/4fxExPcX8DZL6An0BWqy9fjXNmZmZZZwcZdaQNDFtPwncCOwOPB8Rb6fy7sBVABHxiqR3gMrk6LGI+ARA0j2pbj456g4Mi4iFwAeSRgO7Al9UEdO+wDGVTyLis2rOoWdEfFyk/J70OJ5sGq+YHwCHSDozPW8FbJa2H4uIT3PncUSK5wlJ60lqWyxWSXsBoyLiIwBJQ4G9gIXAmMrrmmu7tuf7FDBY0h25c1xKRFwHXAew+kYdo5r2zMzMACdHleZGRJd8gSSAL/NFVRxf+MFb+LyqY0tRkXbq4qv0uJDSr7eAIyLi1aUKpd2o/hoExWMtdc6lzqtUeb6s1eLCiH4pvgOBiZK6VCaoZmZmy8JrjmpuDNAbIE2nbQZUJhP7pfU4awCHkY1qFB7bS1ILSeuTjaA8D8wC2pTobwTwy8onNZhmqo3Cfh8FTlPKCCXtVOK4/DXoAXwcEV+UiPU5YG9J7ZUtaD8WGA08k8q3SHUrp9VKne8Hkr4raRXgR7n9W0XEcxFxHvAxsGltL4KZmVkxTo5q7h9AC0kVwO1An4ioHJUZC9wCTATuLlhvBHAvMBmYBDwBnB0R/0tlC9Ki4l8VHHMxsE7lomOgJ1UbqSW38t9cXV1gu8oF2cBFQEtgsqQp6XkxFwBlkiaTLSY/sVSsETED+F3qaxIwISLuT9NsfYF7Ut3bqznfAcBDZNdtRi6WyyoXe5MlbZOqOWczM7MaUYSXYljTV1ZWFuXlhTmrmZk1V5LGR0RZsX0eOTIzMzPL8YLslYik54DVC4qPj4iKxojHzMysKXJytBKJiN0aOwYzM7OmztNqZmZmZjlOjszMzMxynByZmZmZ5Tg5MjMzM8txcmRmZmaW4+TIzMzMLMfJkZmZmVmOv+fImoWK6TPpMODhxg7DVgBTBx7Y2CGY2QrOI0dmZmZmOU6OzMzMzHKcHK3AJA2WNEdSm1zZFZJCUvsG7HeUpG/8UrGkMklX1qG9PpKuLrHv6brEaGZm1lCcHK343gAOBZC0CtATmN5QnUkquQ4tIsoj4vT67C8idq/P9szMzJaVk6MGJulcSa9IekzSMEln1rKJYUCvtN0DeApYkGv/x5KelzRR0r8ktUh/gyVNkVQh6VepbhdJz0qaLOleSeuk8lGS/ixpNHBGavqo1O5rkvZM9XpIeihtP5L6nChppqQTJbWSNCj1+YKknrnz2FTScEmvSjo/F//swrbT86sl9UnbU1N8z0gql7SzpEclvSmpXxXXvm+qX75wzsxaXnYzM2uunBw1oDQ1dQSwE3A48I2pqhp4HVg/JTLHArfl2v8uWeK0R0R0ARYCvYEuwLcjolNEdAYGpUNuBn4bETsAFcDiJAVoFxF7R8Rf0vNVI6Ir0L+gHgAR8cPU58nAO8B9wKlpX+cU6xBJrdIhXXOxHVVs2q4a70VEN+BJYDBwJPA94MJSB0TEdRFRFhFlLdZsW8vuzMysuXJy1LC6A/dHxNyImAU8WMd27gGOAXYjSw4q7QPsAoyTNDE93xJ4C9hS0lWS9ge+kNSWLAEanY4dAuyVa+v2In0CjAc6FAsqrXu6BTguImaSne8tABHxClnStE2q/lhEfBIRc1Pb3Wt89pkH0mMF8FxEzIqIj4B5ktrVsi0zM7OS/D1HDUv11M5twARgSEQskhY3q1T2u290LO0I/D+y0ZyjgV9V08eXBc+/So8LKfI+kdQixXVhREzJxVNKVPN8AUsn660K9lfGsyi3Xfnc72MzM6s3HjlqWGOBg9NanNZAnb59LiLeBc4B/lGw63HgSEkbAEhaV9LmaURnlYi4GzgX2DmN7HxWuX4IOB4YTd0NBCZHxG25sjFkU2dI2gbYDHg17dsvxbcGcBjZ2qm8d4DtJK2eRrn2WYbYzMzM6sz/4m5AETFO0gPAJLIP/3KgTiuDI+JfRcpekvQHYES6k20+2UjRXGBQKgOoHFk6EbhW0ppkU28n1SWW5EzgxTSdB3AeWfJ2raQKspGgPhHxVRrpGks25bY1cGtElBecy3uS7gAmk62zemEZYjMzM6szRRTOblh9ktQ6ImanhGQM0DciJjR2XM1NWVlZlJeXV1/RzMyaBUnjI6LozUEeOWp410najmwNzRAnRmZmZis2J0cNLCKOyz+XdA2wR0G1jmRTSXlXRMQgzMzMbLlycrScRcSpjR2DmZmZlea71czMzMxynByZmZmZ5Tg5MjMzM8txcmRmZmaW4+TIzMzMLMfJkZmZmVmOkyMzMzOzHH/PkTULFdNn0mHAw40dhjWyqQPr9NvPZtbMeOTIzMzMLMfJkZmZmVmOk6MVjKTBkt6WNFHSK5LOr4c2Z9eyfh9JV5fY94ikdvUVg6QLJe1b2/bMzMwaitccrZjOioi7JLUCXpJ0c0S8na8gqUVELKzvjiVV+Z6IiB/WZ38RcV59tmdmZrasPHLUACSdm0Z9HpM0TNKZdWyqVXr8MrU7VdJ5ksYCR0n6qaRxkiZJulvSmqneFpKeSfsuysV1i6RDc8+HSjokjRTdKelBYETavbGk4ZJel3Rp7pipktpL6pdGtyamka6Raf+xkiokTZF0ScF1+YukCZIel7R+Khss6ch822m7TNKotH2BpCGSRqQ6h0u6NPUzXFLLEq9DX0nlksoXzplZx5fAzMyaGydH9UxSGXAEsBNwOFBWh2YukzQRmAbcFhEf5vbNi4juEXEbcE9E7BoROwIvAyenOlcA/4yIXYH/5Y69ATgpxdkW2B14JO3rBpwYEd9Pz7sAvYDOQC9Jm+YDjIhrI6ILsGuK86+SNgYuAb6fjt9V0mHpkLWACRGxMzAaqO104VbAgcChwL+BkRHRGZibyr8hIq6LiLKIKGuxZttadmdmZs2Vk6P61x24PyLmRsQs4ME6tHFWSjy+Bewjaffcvttz250kPSmpAugNbJ/K9wCGpe1bKitHxGhga0kbAMcCd0fEgrT7sYj4NNf24xExMyLmAS8Bm5eI9QrgiYh4kCxRGhURH6V2hwJ7pXqLcrH/m+w61cZ/ImI+UAG0AIan8gqgQy3bMjMzK8nJUf1TfTUUEbOBUSydSHyZ2x4M/DKNoPyRJdNwAFGi2VvIEqmTgEEl2gX4Kre9kCLr0yT1IUua/lhZVKLPYorFt4Al78lWBfu+AoiIRcD8iKg8flGx2MzMzOrKyVH9GwscLKmVpNaUmPKpibQ4ejfgzRJV2gAz0pqb3rnyp4Bj0nbvgmMGA/0BIuLFZYhtF+BM4McpYQF4Dtg7rUlqQTY6NTrtWwU4Mm0fR3adCk0FdknbR9Q1NjMzs2Xh5KieRcQ44AFgEnAPUA7UdjVw5ZqjyWTTRveUqHcuWULyGPBKrvwM4FRJ44ClFttExAdk65Pyo0Z18UtgXWBkWpR9Q0TMAH4HjCQ7/wkRcX+q/yWwvaTxZGuSLizS5h+BKyQ9STZaZWZmttxpyeyE1RdJrSNidrp7bAzQNyImNHZcACmmCmDniGg2t3CVlZVFeXl5Y4dhZmYrCEnjI6LoTVMeOWoY16WRnwlki55XlMRoX7IRpquaU2JkZmZWG17I2gAi4rj8c0nXkN1BltcReL2g7IqIWNbprqri+i+wWUO1b2Zm1hQ4OVoOIuLUxo7BzMzMasbJkZmZWTXmz5/PtGnTmDdvXmOHYrXUqlUrNtlkE1q2LPpjCkU5OTIzM6vGtGnTaNOmDR06dECqt6+zswYWEXzyySdMmzaNLbbYosbHeUG2mZlZNebNm8d6663nxGglI4n11luv1iN+To7MzMxqwInRyqkur5uTIzMzM7McrzkyMzOrpQ4DHq7X9qYOrNkvTd17770cfvjhvPzyy2y77bYAjBo1issvv5yHHnpocb0+ffpw0EEHceSRR9KjRw9mzJhBq1atWG211bj++uvp0qULADNnzuS0007jqaeeAmCPPfbgqquuom3b7McVXnvtNfr3789rr71Gy5Yt6dy5M1dddRUbbrhhnc/1008/pVevXkydOpUOHTpwxx13sM4663yj3ueff84pp5zClClTkMRNN91Et27dmDRpEv369WP27Nl06NCBoUOHsvbaa1NRUcFf/vIXBg8eXOfYKjk5smahYvrMev+fma3YavphY7YyGTZsGN27d+e2227jggsuqPFxQ4cOpaysjEGDBnHWWWfx2GOPAXDyySfTqVMnbr75ZgDOP/98TjnlFO68807mzZvHgQceyF//+lcOPvhgAEaOHMlHH320TMnRwIED2WeffRgwYAADBw5k4MCBXHLJJd+od8YZZ7D//vtz11138fXXXzNnzhwATjnlFC6//HL23ntvbrrpJi677DIuuugiOnfuzLRp03j33XfZbLNl+0o/T6uZmZmtBGbPns1TTz3FjTfeyG233VanNrp168b06dMBeOONNxg/fjznnnvu4v3nnXce5eXlvPnmm9x6661069ZtcWIE0LNnTzp16rRM53H//fdz4oknAnDiiSdy3333faPOF198wZgxYzj55JMBWG211WjXrh0Ar776KnvttRcA++23H3fffffi4w4++OA6X5s8J0dmZmYrgfvuu4/999+fbbbZhnXXXZcJE2r/y1TDhw/nsMMOA+Cll16iS5cutGjRYvH+Fi1a0KVLF1588UWmTJnCLrvsUm2bs2bNokuXLkX/XnrppW/U/+CDD9hoo40A2Gijjfjwww+/Ueett95i/fXX56STTmKnnXbilFNO4csvvwSgU6dOPPDAAwDceeedvPfee4uPKysr48knn6z5BSnB02pmZmYrgWHDhtG/f38AjjnmGIYNG8bOO+9c8m6sfHnv3r358ssvWbhw4eKkKiKKHluqvJQ2bdowceLEmp9IDSxYsIAJEyZw1VVXsdtuu3HGGWcwcOBALrroIm666SZOP/10LrzwQg455BBWW221xcdtsMEGvP/++8vcv5OjJkTSYOBoYMOImJXKrgBOB9aPiI8lPR0Ru9dDX1OBsoj4uKC8HzAnIm5e1j7MzCzzySef8MQTTyxenLxw4UIkcemll7Leeuvx2WefLVX/008/pX379oufDx06lB133JEBAwZw6qmncs8997D99tvzwgsvsGjRIlZZJZtIWrRoEZMmTeK73/0uH374IaNHj642tlmzZrHnnnsW3Xfrrbey3XbbLVW24YYbMmPGDDbaaCNmzJjBBhts8I3jNtlkEzbZZBN22203AI488kgGDhwIwLbbbsuIESOAbMH4ww8vWU86b9481lhjjWpjro6n1ZqeN4BDASStAvQEplfurE1iJKnWyXNEXNtQiZGkFtXXMjNreu666y5OOOEE3nnnHaZOncp7773HFltswdixY+nYsSPvv/8+L7/8MgDvvPMOkyZNWnxHWqWWLVty8cUX8+yzz/Lyyy+z9dZbs9NOO3HxxRcvrnPxxRez8847s/XWW3Pcccfx9NNPL5V8DB8+nIqKiqXarRw5KvZXmBgBHHLIIQwZMgSAIUOGcOihh36jzre+9S023XRTXn31VQAef/zxxW1VTsMtWrSIiy++mH79+i0+7rXXXlvmNVHgkaMVjqRzgd7Ae8DHwPiIuLwWTQwDegH/BnoATwEH5NqfHRGt0/bZwPHAIuA/ETFA0ijgaWAP4AFJE4HLyd4r44CfR8RXqbmzJPVM28dFxBuSLgBmR8Tlkk4H+gELgJci4pi0fyvg28CmwKURcb2yMdxLU6wBXBwRt0vqAZwPzAC6ANtJui8d2wq4IiKuK3Et+wJ9AVqsvX4tLqGZWdWW992Qw4YNY8CAAUuVHXHEEdx6663sueee/Pvf/+akk05i3rx5tGzZkhtuuGHx7fh5a6yxBr/5zW+4/PLLufHGG7nxxhs57bTT2HrrrYkIunXrxo033ri47kMPPUT//v3p378/LVu2ZIcdduCKK65YpnMZMGAARx99NDfeeCObbbYZd955JwDvv/8+p5xyCo888ggAV111Fb179+brr79myy23ZNCgQYuvxTXXXAPA4YcfzkknnbS47ZEjR3Lggcv+2igilrkRqx+SyoAbgG5kycgE4F81TY7StNpDwJlkScalZEnSENIUWGVyJOkA4Fxg34iYI2ndiPg0JUcvRcQvJLUCXgf2iYjXJN0MTIiIv6dptesj4k+STgCOjoiDCpKj94EtIuIrSe0i4vO0/0fA94C1gBeA3dI59wP2B9qTJWK7Ad8BHgY6RcTb6TwrY10j1ds7Ij6p6tqsvlHH2OjEv9fkMloT4Vv5rT69/PLLfPe7323sMKwKX331FXvvvTdjx45l1VWXHvsp9vpJGh8RZcXa8rTaiqU7cH9EzE1rhh6sYzv3AMeQJRellu3vCwyKiDkAEfFpbt/t6fE7wNsR8Vp6PgTYK1dvWO6xW5E+JgNDJf2YbPSoUuU5fgyMBLqSnfuwiFgYER8Ao4FdU/3nKxOj5HRJk4BnyUaQOpY4RzMzaybeffddBg4c+I3EqC48rbZiqa8f7rmNbNRpSEQsKnHXgcimr4r5sobxRIntSgeSJVOHAOdK2r5E3aimr8p4SNNs+wLd0ojXKLLpNTMza8Y6duxIx471829ljxytWMYCB0tqJak1WXJRaxHxLnAO8I8qqo0AfiJpTcimqorUeQXoIGnr9Px4shGdSr1yj8/kD0yLwTeNiJHA2UA7oHXafWg6x/XI1kWNA8YAvSS1kLQ+WVL1fJGY2gKfpcRoW7LpOTOzBudlKCunurxuHjlagUTEOEkPAJOAd4ByYGYd2/pXNfuHS+oClEv6GngE+H1BnXmSTgLuTHeujQOuzVVZXdJzZEn2sQVdtAD+Lakt2ajQ39KaI8iSnoeBzYCLIuJ9SfeSTc1NIhtJOjsi/pcSoLzhQD9Jk4FXyabWqtX5220p9xoUM6ujVq1a8cknn7DeeuvV6VferXFEBJ988gmtWtVugsELslcwklpHxOw0ojMG6BsRtf8a1BVUfsH28uy3rKwsysvLl2eXZtaEzJ8/n2nTpjFv3rzGDsVqqVWrVmyyySa0bNlyqfKqFmR75GjFc52k7cjW0QxpSomRmdnKqmXLlmyxxRaNHYYtJ06OVjARcVz+uaRryL5zKK8j2S32eVdExKCGjK0+RMQFjR2DmZlZVZwcreAi4tTGjsHMzKw58d1qZmZmZjlekG3NgqRZZHe32fLTnuwncGz58TVf/nzNl7/6uuabR0TR35bytJo1F6+WuivBGoakcl/z5cvXfPnzNV/+lsc197SamZmZWY6TIzMzM7McJ0fWXFzX2AE0Q77my5+v+fLna778Nfg194JsMzMzsxyPHJmZmZnlODkyMzMzy3FyZE2apP0lvSrpDUkDGjuepkjSppJGSnpZ0ouSzkjlF0iaLmli+vthY8falEiaKqkiXdvyVLaupMckvZ4e12nsOJsKSd/JvZcnSvpCUn+/z+uXpJskfShpSq6s5Pta0u/S/99flfT/6i0OrzmypkpSC+A1YD9gGjAOODYiXmrUwJoYSRsBG0XEBEltgPHAYcDRwOyIuLwx42uqJE0FyiLi41zZpcCnETEw/WNgnYj4bWPF2FSl/7dMB3YDTsLv83ojaS9gNnBzRHRKZUXf1+lH2ocBXYGNgf8C20TEwmWNwyNH1pR1Bd6IiLci4mvgNuDQRo6pyYmIGRExIW3PAl4Gvt24UTVbhwJD0vYQsiTV6t8+wJsR8U5jB9LURMQY4NOC4lLv60OB2yLiq4h4G3iD7P/7y8zJkTVl3wbeyz2fhj+0G5SkDsBOwHOp6JeSJqehck/x1K8ARkgaL6lvKtswImZAlrQCGzRadE3bMWQjFpX8Pm9Ypd7XDfb/eCdH1pSpSJnnkRuIpNbA3UD/iPgC+CewFdAFmAH8pfGia5L2iIidgQOAU9N0hDUwSasBhwB3piK/zxtPg/0/3smRNWXTgE1zzzcB3m+kWJo0SS3JEqOhEXEPQER8EBELI2IRcD31NNxtmYh4Pz1+CNxLdn0/SGvAKteCfdh4ETZZBwATIuID8Pt8OSn1vm6w/8c7ObKmbBzQUdIW6V97xwAPNHJMTY4kATcCL0fEX3PlG+Wq/QiYUnis1Y2ktdLidyStBfyA7Po+AJyYqp0I3N84ETZpx5KbUvP7fLko9b5+ADhG0uqStgA6As/XR4e+W82atHRb7d+BFsBNEfGnxo2o6ZHUHXgSqAAWpeLfk32IdCEb5p4K/Kxy3YAtG0lbko0WAawK3BoRf5K0HnAHsBnwLnBURBQubrU6krQm2RqXLSNiZiq7Bb/P642kYUAPoD3wAXA+cB8l3teSzgF+Aiwgm9L/T73E4eTIzMzMbAlPq5mZmZnlODkyMzMzy3FyZGZmZpbj5MjMzMwsx8mRmZmZWY6TIzOrF5IWpl8lnyLpQUntqql/gaQzq6lzWPpxycrnF0ratx5iHSzpyGVtp5Z99k+3gq8wJG2bXrMXJG1VsG+qpCcLyiZW/lq6pDJJV9ZDDB3yv8BesO+G/Ovf0CRtKOlWSW+ln2V5RtKPllf/tuJwcmRm9WVuRHRJv6T9KXBqPbR5GLD4wzEizouI/9ZDu8tV+hX3/sAKlRyRXd/7I2KniHizyP42kjYFkPTd/I6IKI+I02vaUboGtRIRp0TES7U9ri7Sl5neB4yJiC0jYheyL47dpIH7XbUh27e6cXJkZg3hGdIPQEraStLw9C/xJyVtW1hZ0k8ljZM0SdLdktaUtDvZb1hdlkYstqoc8ZF0gKQ7csf3kPRg2v5B+hf/BEl3pt98KymNkPw5HVMuaWdJj0p6U1K/XPtjJN0r6SVJ10paJe07VlJFGjG7JNfu7DTS9RxwDrAxMFLSyLT/n6m/FyX9sSCeP6b4Kyqvl6TWkgalssmSjqjp+UrqIunZdNy9ktZJX5DaHzilMqYi7gB6pe3Cb4buIemhamLLX4Nukn6drtMUSf1z/awqaUg69q7KETZJoySV1eA6X5LeX/+V1DUd95akQ1KdFpIuS++xyZJ+VuRcvw98HRHXVhZExDsRcVVVbaTrMCrF/YqkoSnRQtIukkan2B7Vkp/AGJXec6OBMyQdLOk5ZSN4/5W0YYnXw5aXiPCf//znv2X+A2anxxZkP8q5f3r+ONAxbe8GPJG2LwDOTNvr5dq5GDgtbQ8GjsztGwwcSfat0O8Ca6XyfwI/JvtW3TG58t8C5xWJdXG7ZN9q/PO0/TdgMtAGWB/4MJX3AOYBW6bzeyzFsXGKY/0U0xPAYemYAI7O9TkVaJ97vm7ueo0CdsjVqzz/XwA3pO1LgL/njl+nFuc7Gdg7bV9Y2U7+NShyzFRgG+Dp9PwFslG8Kblr8lCp2AqvAbAL2beorwW0Bl4EdgI6pHp7pHo3seR9MQooq8F1PiBt3wuMAFoCOwITU3lf4A9pe3WgHNii4HxPB/5Wxfu7aBvpOswkG2FahewfBt1TDE8D66djepF9S3/lef2j4LWs/FLmU4C/NPZ/z839z8N5ZlZf1pA0kezDbjzwWBrF2B24M/1jGrIPlkKdJF0MtCP74Hy0qo4iYoGk4cDBku4CDgTOBvYm+wB/KvW3GtmHVXUqf3OvAmgdEbOAWZLmacnaqecj4i1Y/BMH3YH5wKiI+CiVDwX2IpueWUj2Y7ylHC2pL9mH/UYp7slp3z3pcTxweNrel2yap/IafCbpoOrOV1JboF1EjE5FQ1jyi/LV+RT4TNIxwMvAnBL1vhFb2sxfg+7AvRHxZYrrHmBPsmv/XkQ8ler9myxRuTzX/q6Uvs5fA8NTvQrgq4iYL6mC7L0I2W/P7aAl68zakv0O19ulTlzSNSnmryNi1yra+JrsvTEtHTcx9fs50InsvwPIkuD8z4rcntveBLg9jSytVlVctnw4OTKz+jI3IrqkD+OHyNYcDQY+j4gu1Rw7mGwkYJKkPmT/Gq/O7amPT4FxETErTWc8FhHH1jL2r9Ljotx25fPK/08W/tZSAKK0eRGxsNgOZT+SeSawa0pyBgOtisSzMNe/isRQ1/OtjduBa4A+VdQpFhssfQ2qulbFrm1h+6XMjzTkQu71i4hFWrKeR2SjcVUl3S8CRywOIOJUSe3JRohKtiGpB0u/ZypfMwEvRkS3Ev19mdu+CvhrRDyQ2rugijhtOfCaIzOrV5H9IOfpZB/+c4G3JR0F2aJXSTsWOawNMENSS6B3rnxW2lfMKGBn4Kcs+Vf4s8AekrZO/a0paZtlO6PFukraQtlao17AWOA5YG9J7ZUtOD4WGF3i+Py5rE324TgzrS85oAb9jwB+WflE0jrU4HzT6/GZpD1T0fFVxFjMvcClVD2aVyy2QmOAw1KMa5H9gn3l3XCbSapMIo4lu7Z5tbnOxTwK/Dy9v5C0TYoh7wmglaSf58ryC+hr0kbeq8D6leclqaWk7UvUbQtMT9snlqhjy5GTIzOrdxHxAjCJbKqlN3CypElk/zo/tMgh55J9AD4GvJIrvw04S0VuNU8jEg+RJRYPpbKPyEY4hkmaTJY8fGMBeB09AwwEppBNe9wb2a+v/w4YSXa+EyLi/hLHXwf8R9LIiJhEtobnRbI1Nk+VOCbvYmCdtCB5EtCzFud7ItnC9slkvyB/YQ36AyAiZkXEJRHxdW1iK9LOBLIRwufJXusb0vsEsim7E1N865KtIcsfW5vrXMwNwEvABGVfG/AvCmZO0ujTYWRJ2NuSniebgvxtTdsoaO9rsnVpl6RrMpFsirmYC8imnp8EPq7FeVkD0ZLRSDMzKyZNdZwZEQc1cihmthx45MjMzMwsxyNHZmZmZjkeOTIzMzPLcXJkZmZmluPkyMzMzCzHyZGZmZlZjpMjMzMzs5z/D8UCUlfo/1hrAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.8910919453548994, pvalue=2.1332259314139563e-35)\n", "Slope and P-value = PearsonRResult(statistic=0.9012530425830749, pvalue=2.2593983239009187e-37)\n", "Slope and P-value = PearsonRResult(statistic=-0.17724760743378506, pvalue=0.07770020855569096)\n", "Slope and P-value = PearsonRResult(statistic=0.9046293325236198, pvalue=4.465076590822721e-38)\n", "Slope and P-value = PearsonRResult(statistic=0.9426745985063338, pvalue=1.6631288817813772e-48)\n", "Slope and P-value = PearsonRResult(statistic=-0.49736277894286124, pvalue=1.4083093121621994e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.20267945758179057, pvalue=0.04313980376144854)\n", "Slope and P-value = PearsonRResult(statistic=0.7891895091122477, pvalue=1.8043680849055687e-22)\n", "Slope and P-value = PearsonRResult(statistic=0.8944998717347339, pvalue=4.888677353898226e-36)\n", "Slope and P-value = PearsonRResult(statistic=0.6434085875262974, pvalue=5.232256520114742e-13)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'BroodBedding','SampleType','PastureTime']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType','BroodBedding','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns'),sample.BroodBedding,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " print(pd.value_counts(sample['BroodBedding']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"BroodBedding in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','BroodBedding','SampleType','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " #display(prelim2_plot.nlargest(10))\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"BroodBedding_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (5) BrGMOFree" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 881)\n", "Soil (695, 881) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "Y 468\n", "N 230\n", "Name: BrGMOFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.281888111630284, pvalue=0.004493008473008391)\n", "Slope and P-value = PearsonRResult(statistic=-0.9650576862655857, pvalue=8.317014136136656e-59)\n", "Slope and P-value = PearsonRResult(statistic=-0.49068287577225167, pvalue=2.1875126531387505e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.9260166687683784, pvalue=2.9770343002096537e-43)\n", "Slope and P-value = PearsonRResult(statistic=-0.8405863056675564, pvalue=7.681749927060345e-28)\n", "Slope and P-value = PearsonRResult(statistic=-0.9642593343175416, pvalue=2.4683666034934544e-58)\n", "Slope and P-value = PearsonRResult(statistic=0.5596717290443307, pvalue=1.4187503380992299e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.3847045191118045, pvalue=7.753580109219271e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.8623670132882435, pvalue=9.96884801697723e-31)\n", "Slope and P-value = PearsonRResult(statistic=-0.8754003723419792, pvalue=1.0559035095266423e-32)\n", "Y 468\n", "N 227\n", "Name: BrGMOFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.9536908559819121, pvalue=6.237173685655673e-53)\n", "Slope and P-value = PearsonRResult(statistic=-0.870098130669701, pvalue=7.124889728875348e-32)\n", "Slope and P-value = PearsonRResult(statistic=-0.9590606633914884, pvalue=1.6913783496965302e-55)\n", "Slope and P-value = PearsonRResult(statistic=0.7006534100235746, pvalue=4.875704227639323e-16)\n", "Slope and P-value = PearsonRResult(statistic=0.803363819387273, pvalue=8.620733474846159e-24)\n", "Slope and P-value = PearsonRResult(statistic=-0.3634222168668747, pvalue=0.011118143061066448)\n", "Slope and P-value = PearsonRResult(statistic=0.8649727357771111, pvalue=4.172252983968741e-31)\n", "Slope and P-value = PearsonRResult(statistic=-0.9710913965679241, pvalue=8.884987600832535e-63)\n", "Slope and P-value = PearsonRResult(statistic=-0.9331589125795168, pvalue=2.4482935670088798e-45)\n", "Slope and P-value = PearsonRResult(statistic=0.7278907821961262, pvalue=9.552051628617293e-18)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'BrGMOFree','SampleType','PastureTime']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType','BrGMOFree','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns'),sample.BrGMOFree,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " print(pd.value_counts(sample['BrGMOFree']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"BrGMOFree in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','BrGMOFree','SampleType','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " #display(prelim2_plot.nlargest(10))\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"BrGMOFree_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (6) BrSoyFree" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 881)\n", "Soil (695, 881) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "N 583\n", "Y 115\n", "Name: BrSoyFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.4697989205456458, pvalue=8.172274672802674e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.6966885200729924, pvalue=8.331955409884854e-16)\n", "Slope and P-value = PearsonRResult(statistic=0.9299570381147558, pvalue=2.2427199869647717e-44)\n", "Slope and P-value = PearsonRResult(statistic=0.6358390109046389, pvalue=0.00020986089969027787)\n", "Slope and P-value = PearsonRResult(statistic=0.6464146035046918, pvalue=3.7626493345051623e-13)\n", "Slope and P-value = PearsonRResult(statistic=-0.35024824893898704, pvalue=0.0003538582793838997)\n", "Slope and P-value = PearsonRResult(statistic=0.4076312275905662, pvalue=0.0009132522378321863)\n", "Slope and P-value = PearsonRResult(statistic=0.823132741600767, pvalue=7.989634223482862e-26)\n", "Slope and P-value = PearsonRResult(statistic=0.6987889244839505, pvalue=6.279726576864831e-16)\n", "Slope and P-value = PearsonRResult(statistic=0.9651898790209623, pvalue=6.929159666591318e-59)\n", "N 580\n", "Y 115\n", "Name: BrSoyFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.7983043800477017, pvalue=2.6244367832782085e-23)\n", "Slope and P-value = PearsonRResult(statistic=0.5622275368715556, pvalue=1.1508171904155423e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.9471615227566776, pvalue=3.416329546243335e-50)\n", "Slope and P-value = PearsonRResult(statistic=0.6246940621062627, pvalue=3.7644523829026e-12)\n", "Slope and P-value = PearsonRResult(statistic=0.5048922474830788, pvalue=8.475758647635074e-08)\n", "Slope and P-value = PearsonRResult(statistic=0.8333988744782772, pvalue=5.5499120393728174e-27)\n", "Slope and P-value = PearsonRResult(statistic=0.8066164002836614, pvalue=1.7044343967699557e-22)\n", "Slope and P-value = PearsonRResult(statistic=0.6869013431538287, pvalue=3.0155266480859968e-15)\n", "Slope and P-value = PearsonRResult(statistic=-0.5397983366351231, pvalue=6.810518855411832e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.8823580382397935, pvalue=1.2316224761848066e-21)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'BrSoyFree','SampleType','PastureTime']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType','BrSoyFree','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns'),sample.BrSoyFree,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " print(pd.value_counts(sample['BrSoyFree']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"BrSoyFree in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','BrSoyFree','SampleType','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " #display(prelim2_plot.nlargest(10))\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"BrSoyFree_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (7) BroodCleanFrequency" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (648, 881)\n", "Soil (645, 881) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "DLM 398\n", "3Days 105\n", "AIAO 90\n", "Daily 25\n", "Yearly 15\n", "Weekly 15\n", "Name: BroodCleanFrequency, dtype: int64\n" ] }, { "data": { "image/png": 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Ok/Ql4BagP3AhsDgi+qa4NpO0eR191OUo4M2IODr10S2/QkSMAEYAbNirdxS7nczMzNriSMDBwP0RsTwilgAPNrL9N4AnImIZ8Ffgm2mIf39gN2CypBnA6cD2Oe3uTH/3AcZHxNsR8QlwG3Boaj8xIuYCRMSinHhvTWWPAz3SzvpI4PraziPivXr6qEs1cKSkKyQdEhE+1Dczs2bT5kYCgEadwy/gFOAgSTXpfQ/g8NTvuIg4pY52HzawfAGFjrQL1Y866tfVxyd8NiHrBBARL0vaG/g68CtJj0bEpQXam5mZNVpbHAmYBAyU1Cmdsy/6rgmSNiE7Mt8uIioiogI4hywxeIYsOdgp1e0saecC3TwLHCapZxpBOAWYADydyndI7WuH8icCp6ayAcA7EfEB8Cjwg5zYNqunjxpgr1S2F1A7fytgWUT8Bbi6to6ZmVlzaHMjARExRdIDwEzgdaAKKHYY/FvA4xGxMqfsfuBK4D+AwcBoSRumeT8HXs5b/kJJPwWeIDty/1tE3A/ZRXjAvZLWA94CvgxcDNwsaRawjOw0A8BlwPWSZgOrgEsi4t46+vgrcFo6TTElJ6a+wFWSVgMfA2fXt/J9t+5GVSveacrMzNZtimh715JJ6hIRSyV1JjvSHhIR00odV1tXWVkZVVVVpQ7DzMzaEElTI6Ky0Lw2NxKQjJC0G9m58VFOAMzMzJpfm0wCImJQ7ntJ1wMH5VXrDbySV3ZtRNzckrGZmZm1F20yCcgXEeeUOgYzM7P2pi3+OsDMzMxagZMAMzOzMuUkwMzMrEw5CTAzMytTTgLMzMzKlJMAMzOzMrVO/ETQilO9YDEVwx4qdRhmZm1KjW+nXiePBJiZmZUpJwFmZmZlql0kAZKulzRD0guSlqfpGZJOKLL9pZKOrGd+paTfpumLJZ3fDDEfl56PYGZmVhLt4pqA2tsKS6oAxkZE/2LbSuoQERc10H8V2SONm9NxwFjghWIbSFo/Ij5p5jjMzKxMtamRAEkXSnpR0jhJo9fmiFvSAEljc97/TtLgNF0j6SJJk4BvSxpZO2ogaR9JT0maKek5SV3z+wL6SXpc0iuSvpfadZH0mKRpkqolHZuz7NMkzUp93irpQOAY4Ko0YrFjej0saaqkJyXtktqOlHSNpCeAK5q6PczMzPK1mZEASZXA8cCeZHFNA6a24CJXRMTBadlHpb8bAHcCJ0XEFEmbAMsLtN0D2B/YGJgu6SHgLeCbEfGBpJ7AM5IeAHYDfgYcFBHvSOoeEYvSvLERcU9a9mPA9yPiFUn7Ab8HvpSWtzNwZESsyg9E0hBgCECHTTZvju1iZmZlos0kAcDBwP0RsRxA0oMtvLw7C5R9EVgYEVMAIuKDFEt+vdo4l6cj9H2Bh4D/kXQosBrYGtiSbEd+T0S8k/pclN+ZpC7AgcDdOcvaMKfK3YUSgNTfCGAEwIa9ekcD62xmZrZGW0oCPrenXUuf8NnTHZ3y5n9YRwzF7Ejz6wRwKrA5sHdEfCypJi2zmD7XA96v51qGQrGamZmtlbZ0TcAkYKCkTunIeG3v7vA6sJukDSV1A44oos2LwFaS9gFI1wMUSpSOTXH2AAYAU4BuwFspATgc2D7VfQw4MdVFUvdUvgToCmtGHOZK+naqI0n9Gr/KZmZmxWszSUAagn8AmAncS3Y1/uK16G8ecBcwC7gNmF5Em4+Ak4DrJM0ExvH5EQSA58iG/58BfhkRb6ZlVEqqIhsVeDH1+TxwOTAh9XlN6uMO4AJJ0yXtmNqcmeo8DxyLmZlZC1JE2zmNLKlLRCyV1BmYCAyJiGmljmtdUVlZGVVVzf1LRjMzW5dJmhoRlYXmtaVrAgBGpBvodAJGOQEwMzNrOW0qCYiIQbnvJV0PHJRXrTfwSl7ZtRFxc0vGZmZm1t60qSQgX+2dAM3MzKz5tZkLA83MzKx1OQkwMzMrU04CzMzMypSTADMzszLlJMDMzKxMOQkwMzMrU04CzMzMylSbvk+ANU71gsVUDHuo1GGYmZVczfC1fQZdefBIgJmZWZlyEmBmZlamWjwJkDRS0lxJMyTNlHRESy+zGJIulnR+mr5U0pFp+sb0ECMkLW2mZT2V/lZImp2mB0ga2xz9m5mZNUVrXRNwQUTcI+lwYATZQ4DajIi4KGf6rBbo/8Dm7tPMzGxtFTUSIOlCSS9KGidpdO0RdBM8DWyd+hws6Xc5yxgraUCaXirpCklTJf1D0r6Sxkt6TdIxOe3HSHowjTT8QNIPJU2X9Iyk7qnejpIeTn09KWmXAus3UtIJaXq8pMqceb+WNE3SY5I2T2XfkzQljWz8VVLnVL6lpPtS+UxJB9auTwPbd82oRHo/O40abCzpodTXbEknFWg7RFKVpKpVyxYX+TGYmZkVkQSkHeLxwJ7At4DK+lvU6yhgTBH1NgbGR8TewBLgMuDLwDeBS3Pq9QEGAfsClwPLImJPsmTjtFRnBHBu6ut84PeNiHdjYFpE7AVMAH6Ryu+NiH0ioh8wBzgzlf8WmJDK9wKeb8SyCjkKeDMi+kVEH+Dh/AoRMSIiKiOiskPnbmu5ODMzKyfFnA44GLg/IpYDSHqwCcu5StKVwBbA/kXU/4hPd3jVwMqI+FhSNVCRU++JiFgCLJG0GHgwp80ekroABwJ3S6pts2Ej4l4N3Jmm/wLcm6b7SLoM2BToAjySyr9ESj4iYhWwtofm1cDVkq4AxkbEk2vZn5mZ2RrFnA5Qw1UadAGwE/BzYFQq+yRv+Z1ypj+OiEjTq4GVABGxms8mLitzplfnvK+ttx7wfkT0z3ntuhbrURvTSOAHEdEXuCQv9qYouC0i4mVgb7Jk4FeSLirQ1szMrEmKSQImAQMldUpH1k26A0PagV8LrCfpq0AN0F/SepK2JRvSb1YR8QEwV9K3AZTp14gu1gNOSNODyLYFQFdgoaSOwKk59R8Dzk7L6iBpkyKXU0N2+gBJewE7pOmtyE5x/AW4uraOmZlZc2jwdEBETJH0ADATeB2ooonD3BERaRj9x8CRwFyyo9zZwLSm9FmEU4E/SPo50BG4g2xdivEhsLukqWTrXHth3oXAs2Tbo5osKQD4T2CEpDOBVWQJwdNFLOevwGmSZgBTgJdTeV+yUymrgY9Tf2ZmZs1Cn46611NJ6hIRS9NV8BOBIRHRUjtta6LKysqoqqoqdRhmZtaGSJoaEQUv6i/2PgEj0g10OgGjnACYmZmt+4pKAiJiUO57SdcDB+VV6w28kld2bUTc3PTwzMzMrKU06Y6BEXFOcwdiZmZmrcsPEDIzMytTTgLMzMzKlJMAMzOzMuUkwMzMrEw5CTAzMytTTgLMzMzKlJMAMzOzMtWk+wRY21S9YDEVwx4qdRhmZi2iZniTnl9n9fBIgJmZWZlyEmBmZlam2mQSIGmkpBOa2HawpK3WYtkDJI1N08dIGtbUvszMzNqy9nhNwGBgNvDm2nYUEQ8AD6xtP2ZmZm1Ri40ESLpQ0ouSxkkaLen8Jvazt6QJkqZKekRSr1TeX9IzkmZJuk/SZmn0oBK4TdIMSRtJ+nqKY5Kk3+Yc5e8r6SlJ09PfLxZY9mBJv0vTW6blzEyvA1P5DyXNTq+hOW1PS7HNlHRrXX1IqpA0O6fd+ZIuTtPnSXoh9XNHHdtniKQqSVWrli1uyiY2M7My1SIjAZIqgeOBPdMypgFTm9BPR+A64NiIeFvSScDlwHeBW4BzI2KCpEuBX0TEUEk/AM6PiCpJnYA/AYdGxFxJo3O6fzGVfyLpSOB/Usx1+S0wISK+KakD0EXS3sAZwH6AgGclTQA+An4GHBQR70jqXlcfwGb1LHMYsENErJS0aaEKETECGAGwYa/eUU9fZmZmn9FSpwMOBu6PiOUAkh5sYj9fBPoA4yQBdAAWSuoGbBoRE1K9UcDdBdrvArwWEXPT+9HAkDTdDRglqTcQQMcGYvkScBpARKwCFks6GLgvIj4EkHQvcEjq756IeCfVX1RPH/UlAbPIRjXGAGMaiM/MzKxRWup0gJqxn+cjon969Y2IrzRTHL8EnoiIPsBAoFMT46urvNij8k/47OeQG8fRwPXA3sBUSe3xGg4zMyuRlkoCJgEDJXWS1IVsZ9YULwGbSzoAstMDknaPiMXAe5IOSfW+A9SOCiwBuqbpF4EvSKpI70/K6bsbsCBNDy4ilseAs1McHSRtAkwEjpPUWdLGwDeBJ1PdEyX1SPW719PHv4AtJPWQtCHwjTR/PWDbiHgC+DGwKdnpAzMzs2bRIklAREwhu6p+JnAvUAU05qq19YGVEfERcAJwhaSZwAzgwFTndOAqSbOA/sClqXwk8EdJM9L7/wAeljSJbIdbG8eVwK8kTSY7zdCQ/wQOl1RNdn3D7hExLS3vOeBZ4MaImB4Rz5NduzAhxX1NPX18nGJ/FhhLlriQYvpLqjsd+E1EvF9EnGZmZkVRRMtcSyapS0QsldSZ7Ih5SNppNtRuPWAKcFramTZXHCIbWn8lIn6ztv22RZWVlVFVVVXqMMzMrA2RNDUiKgvNa8mbBY1IR+PTgL8WmQBsRfYb/2eaIwFIvpfieJ7sFMCfmqlfMzOzdVqLXWgWEYNy30u6Hjgor1pv4JW8sqsi4uZmjOM3QLs88jczM1sbrXa1eUSc01rLMjMzs4a1yWcHmJmZWctzEmBmZlamnASYmZmVKScBZmZmZcpJgJmZWZlyEmBmZlamnASYmZmVKT+Vrh2pXrCYimEPlToMM7OCaoY39Vly1lI8EmBmZlamnASYmZmVqXUqCZA0UtJcSTMkzZR0RM68Gkk916LvtWpvZma2rlmnkoDkgojoDwwF/ljaUMzMzNZdrZ4ESLpQ0ouSxkkaLen8Jnb1NLB1Xtm5kqZJqpa0S1ped0ljJM2S9IykPVJ5D0mPSpou6U+AcmL8oaTZ6TU0p/y01M9MSbemsu0lPZbKH5O0XSrfUtJ9qe5MSQfW08dISSfkLGdp+ttL0sQ08jFb0iEFtucQSVWSqlYtW9zETWlmZuWoVZMASZXA8cCewLeAyrXo7ihgTF7ZOxGxF/AHoDa5uASYHhF7AP8N3JLKfwFMiog9gQeA2p333sAZwH7A/sD3JO0paXfgZ8CXIqIf8J+pn98Bt6T+bwN+m8p/C0xIdfcCnq+nj7oMAh5JIx/9gBn5FSJiRERURkRlh87dGujOzMzsU639E8GDgfsjYjmApAeb0MdVkq4EtiDbSee6N/2dSpZk1C7zeICIeDyNAHQDDq2tExEPSXovp/59EfFhivFe4BAggHsi4p3UZlGqf0DOsm4FrkzTXwJOS3VXAYslnVZHH3WZAvxZUkdgTETMaKC+mZlZ0Vr7dIAartKgC4CdgJ8Do/LmrUx/V/FpglNomZH3N1ddMaqO+nX13Zg+PiF9FpIEbAAQERPJkpUFwK0piTAzM2sWrZ0ETAIGSuokqQvQpDtHRMRq4FpgPUlfbaD6ROBUAEkDyE4ZfJBX/jVgs5z6x0nqLGlj4JvAk8BjwImSeqQ23VP9p4CT0/SpaR1J9c9OdTtI2qSePmqAvdP0sUDHNH974K2IuAG4iey0gpmZWbNo1dMBETFF0gPATOB1oApo0tVsERGSLgN+DDxST9WLgZslzQKWAaen8kuA0ZKmAROAN1K/0ySNBJ5L9W6MiOkAki4HJkhaBUwHBgPnkQ3ZXwC8TXY9AWTn+0dIOpNsZOLsiHi6jj5uAO6X9BxZovBh6mMAcIGkj4GlpNMLdem7dTeqfEcuMzMrkiKKGeFuxgVKXSJiqaTOZEfdQyJiWqsG0U5VVlZGVVVVqcMwM7M2RNLUiCh4IX4pnh0wQtJuQCdglBMAMzOz0mj1JCAiBuW+l3Q9cFBetd7AK3ll10bEzS0Zm5mZWTkp+VMEI+KcUsdgZmZWjtbF2wabmZlZM3ASYGZmVqacBJiZmZUpJwFmZmZlykmAmZlZmXISYGZmVqZK/hNBaz7VCxZTMeyhUodhZmWgxrcobxc8EmBmZlamnASYmZmVqXUuCZB0vaQZkl6QtDxNz5B0Ql69pxroZ7ykgg9UMDMzKwfr3DUBtbcZllQBjI2I/rnzJXWIiFURcWAJwjMzM1tnlGwkQNKFkl6UNE7SaEnnr0VfAyQ9Iel2oDqVLc2Z/2NJ1ZJmShqe0/Tbkp6T9LKkQ1LdTpJuTvWnSzo8lXeQdHUqnyXp3FR+RKpXLenPkjZM5ftIeiot8zlJXevpo0ZSzzRdKWl8mj4sZ6RjuqSuTd1GZmZm+UoyEpCG4Y8H9kwxTAOmrmW3+wJ9ImJu3rK+BhwH7BcRyyR1z5m9fkTsK+nrwC+AI4FzACKir6RdgEcl7QycAewA7BkRn0jqLqkTMBI4IiJelnQLcLak3wN3AidFxBRJmwDLgSH5fTSwTucD50TEZEldgBX5FSQNSf3SYZPNi91WZmZmJRsJOBi4PyKWR8QS4MFm6PO5/AQgORK4OSKWAUTEopx596a/U4GKnNhuTXVfBF4Hdk79/DEiPsnp54vA3Ih4ObUdBRyayhdGxJRU94PUrlAf9ZkMXCPpPGDT2na5ImJERFRGRGWHzt0a6M7MzOxTpUoC1AJ9fljPsqKOeSvT31V8OipSV2yF+mlM3frKP+HTz6JTbWFEDAfOAjYCnkkjE2ZmZs2iVEnAJGBgOv/eBWjJu048CnxXUmeAIobgJwKnpro7A9sBL6V+vi9p/Zx+XgQqJO2U2n4HmJDKt5K0T6rbNbUr1AdADbB3mj6+NhBJO0ZEdURcAVQBTgLMzKzZlCQJSMPkDwAzyYbkq4DFLbSsh9OyqiTNIDvPXp/fAx0kVZOd1x8cESuBG4E3gFmSZgKDImIF2bUCd6f6q8mG+z8CTgKuS3XHkR3hf66PtMxLgGslPUk2KlFrqKTZqe5y4O9rsy3MzMxyKaKukfIWXrDUJSKWpiP0icCQiJhWkmDaicrKyqiqqip1GGZm1oZImhoRBe+LU8r7BIyQtBvZEfIoJwBmZmatq2RJQEQMyn0v6XrgoLxqvYFX8squjYibWzI2MzOzctBm7hhYeydAMzMzax3r3LMDzMzMrHk4CTAzMytTTgLMzMzKlJMAMzOzMuUkwMzMrEw5CTAzMytTTgLMzMzKVJu5T4CtveoFi6kY9lCpwzCzdqRmeEs+381KzSMBZmZmZcpJQDORtI2k+yW9IulVSddK2kBSf0lfz6l3saSGnmRoZmbW4pwENANJInsk8piI6A3sDHQBLgf6A1+vu3Wjl9WhufoyM7Py5iSgeXwJWFH7YKOIWAX8F3AWcCVwkqQZkk5K9XeTNF7Sa5LOq+1E0v+T9Fyq+6faHb6kpZIulfQscECrrpmZmbVbTgKax+7A1NyCiPgAqAEuA+6MiP4RcWeavQvwVWBf4BeSOkraFTgJOCgi+gOrgFNT/Y2B2RGxX0RMyl2OpCGSqiRVrVq2uGXWzszM2iX/OqB5CIhGlD8UESuBlZLeArYEjgD2BqZkZxfYCHgr1V8F/LXQgiNiBDACYMNevQsty8zMrCAnAc3jeeD43AJJmwDbku3A863MmV5F9jkIGBURPy1Qf0U6xWBmZtZsfDqgeTwGdJZ0Gqy5eO/XwEjgX0DXIvs4QdIWqY/ukrZvmXDNzMycBDSLiAjgm8C3Jb0CvAysAP4beILsQsDcCwML9fEC8HPgUUmzgHFArxYP3szMypZPBzSTiJgHDCwwayWwTz3t+uRM3wncWaBOl+aI0czMLJeTgHak79bdqPItPs3MrEg+HWBmZlamnASYmZmVKScBZmZmZcpJgJmZWZlyEmBmZlamnASYmZmVKScBZmZmZcpJgJmZWZlyEmBmZlamnASYmZmVKd82uB2pXrCYimEPlToMM2sjanwbcWuARwLMzMzKlJMAMzOzMlWWSYCkfpJm5Lw/RdIySR3T+76SZtXTvlLSb5sxnvGSKtN0jaSezdW3mZlZXcoyCQCqge0ldU3vDwReBPbMeT+5rsYRURUR57VsiGZmZi1rnU4CJF0o6UVJ4ySNlnR+Me0iYjUwBdgvFe0NXE+28yf9fUrSxpL+LGmKpOmSjk3LHSBpbJo+TNKM9JouqaukXpImprLZkg5Jdb8i6WlJ0yTdLalLA+s3RtJUSc9LGlJHnSGSqiRVrVq2uJjVNzMzA9bhJCANnx9PdvT+LaCykV08BRwoaWNgNTCezyYBk4GfAY9HxD7A4cBVqX6u84FzIqI/cAiwHBgEPJLK+gEz0hD/z4EjI2IvoAr4YQMxfjci9k7rdp6kHvkVImJERFRGRGWHzt0as/5mZlbm1uWfCB4M3B8RywEkPdjI9pOBHwFPAlMi4lVJO0naHOgSEa9J+gpwTM4IQydguwL9XCPpNuDeiJgvaQrw53SNwZiImCHpMGA3YLIkgA2ApxuI8TxJ30zT2wK9gXcbuZ5mZmYFrctJgNay/TPAPmTJRO3OeD5wMtkoQe0yjo+Ilz6zYGnL2umIGC7pIeDrwDOSjoyIiZIOBY4GbpV0FfAeMC4iTikmOEkDgCOBAyJimaTxZEmImZlZs1hnTwcAk4CBkjqlc+uNuitGRCwB5gGD+TQJeBoYyqdJwCPAuUqH7pL2JI+kHSOiOiKuIBvi30XS9sBbEXEDcBOwF1nScZCknVK7zpJ2rifEbsB7KQHYBdi/MetnZmbWkHU2CYiIKcADwEzgXrIdcGOvjJsMbBgR89L7p4Ev8GkS8EugIzBL0uz0Pt/QdPHfTLLrAf4ODCC7DmA62XUL10bE22QJx+j088NngF3qie1hYP1U95epvpmZWbNRRJQ6hiaT1CUilkrqDEwEhkTEtFLHVSqVlZVRVVVV6jDMzKwNkTQ1IgpePL8uXxMAMELSbmTnykeVcwJgZmbWWOt0EhARg3LfS7oeOCivWm/glbyyayPi5paMzczMrK1bp5OAfBFxTqljMDMzW1e0qyTAzMya7uOPP2b+/PmsWLGi1KFYE3Tq1IltttmGjh07Ft3GSYCZmQEwf/58unbtSkVFBemX0baOiAjeffdd5s+fzw477FB0u3X2J4JmZta8VqxYQY8ePZwArIMk0aNHj0aP4jgJMDOzNZwArLua8tk5CTAzMytTvibAzMwKqhj2ULP2VzO8uLu733fffXzrW99izpw57LJLdmPV8ePHc/XVVzN27Ng19QYPHsw3vvENTjjhBAYMGMDChQvp1KkTG2ywATfccAP9+/cHYPHixZx77rlMnjwZgIMOOojrrruObt2yJ6++/PLLDB06lJdffpmOHTvSt29frrvuOrbcckuaatGiRZx00knU1NRQUVHBXXfdxWabbfa5etdeey033HADEcH3vvc9hg4d+pn5V199NRdccAFvv/02PXv2pLq6ml//+teMHDmyybHlchLQjlQvWNzs/2jNbN1U7A63LRo9ejQHH3wwd9xxBxdffHHR7W677TYqKyu5+eabueCCCxg3bhwAZ555Jn369OGWW24B4Be/+AVnnXUWd999NytWrODoo4/mmmuuYeDAgQA88cQTvP3222uVBAwfPpwjjjiCYcOGMXz4cIYPH84VV1zxmTqzZ8/mhhtu4LnnnmODDTbgqKOO4uijj6Z3794AzJs3j3HjxrHddp8+vLZv377Mnz+fN9544zPlTeXTAWZm1mYsXbqUyZMnc9NNN3HHHXc0qY8DDjiABQsWAPDPf/6TqVOncuGFF66Zf9FFF1FVVcWrr77K7bffzgEHHLAmAQA4/PDD6dOnz1qtx/3338/pp58OwOmnn86YMWM+V2fOnDnsv//+dO7cmfXXX5/DDjuM++67b838//qv/+LKK6/83Ln+gQMHNnnb5HMSYGZmbcaYMWM46qij2HnnnenevTvTpjX+bvAPP/wwxx13HAAvvPAC/fv3p0OHDmvmd+jQgf79+/P8888ze/Zs9t577wb7XLJkCf379y/4euGFFz5X/1//+he9evUCoFevXrz11lufq9OnTx8mTpzIu+++y7Jly/jb3/7GvHnZ8+weeOABtt56a/r16/e5dpWVlTz55JNFbYuG+HSAmZm1GaNHj15zXvzkk09m9OjR7LXXXnVe+Z5bfuqpp/Lhhx+yatWqNclDRBRsW1d5Xbp27cqMGTOKX5Ei7LrrrvzkJz/hy1/+Ml26dKFfv36sv/76LFu2jMsvv5xHH320YLstttiCN998s1li8EhAkSTtL+lZSTMkzZF0cSofLCkkHZFT95up7IT0frykl3LaDsmpmztvhqQtUvn3JVWnsknpQUlmZu3Wu+++y+OPP85ZZ51FRUUFV111FXfeeScRQY8ePXjvvfc+U3/RokX07NlzzfvbbruNuXPnMmjQIM45J7uL/O6778706dNZvXr1mnqrV69m5syZ7Lrrruy+++5MnTq1wdgaOxKw5ZZbsnDhQgAWLlzIFltsUbDfM888k2nTpjFx4kS6d+9O7969efXVV5k7dy79+vWjoqKC+fPns9dee/F///d/QHY/h4022qjBmIvhJKB4o8geVdwf6APclTOvGjgl5/3JwMy89qemtgcBV0jaIH9eetWOGd0eEX1TmyuBa5ptTczM2qB77rmH0047jddff52amhrmzZvHDjvswKRJk+jduzdvvvkmc+bMAeD1119n5syZa34BUKtjx45cdtllPPPMM8yZM4eddtqJPffck8suu2xNncsuu4y99tqLnXbaiUGDBvHUU0/x0EOfXlT98MMPU11d/Zl+a0cCCr122+3zx2jHHHMMo0aNAmDUqFEce+yxBde59jTBG2+8wb333sspp5xC3759eeutt6ipqaGmpoZtttmGadOm8W//9m9A9muGtb1moVZZnQ6QdCFwKjAPeAeYGhFXF9l8C2AhQESsAnJTvyeBQyR1BDYEdgJm1NFPF+BDYFV9C4uID3LebgxEoXppVGEIQIdNNm9oHczMitbavzAYPXo0w4YN+0zZ8ccfz+23384hhxzCX/7yF8444wxWrFhBx44dufHGG9f8zC/XRhttxI9+9COuvvpqbrrpJm666SbOPfdcdtppJyKCAw44gJtuumlN3bFjxzJ06FCGDh1Kx44d2WOPPbj22mvXal2GDRvGiSeeyE033cR2223H3XffDcCbb77JWWedxd/+9rc16/fuu+/SsWNHrr/++oI/I8z3xBNPcPTRzfPZKKLgvqXdkVQJ3AgcQJb8TAP+VGwSIOki4L+A8cDDwKiIWCFpMFAJfAQ8DnQjSwJ2AMZGxD2SxgO9gJVkjzYeGhF/Sv2OB3qQJQV/BS6L9KFIOgf4IbAB8KWIyH8k8mds2Kt39Dr9f4tZHTNr55qyA58zZw677rprC0RjzWXlypUcdthhTJo0ifXX//xxfKHPUNLUiKgs1F85nQ44GLg/IpZHxBLgwcY0johLyXb2jwKDyBKBXHeQnQY4GRhdoItTI2IPYDvgfEnb55T3BQ5Jr+/kLPP6iNgR+Anw88bEa2Zm7c8bb7zB8OHDCyYATVFOScBa3xA7Il6NiD8ARwD9JPXImfcc2bUCPSPi5Xr6eJtsFGK/9H5B+rsEuB3Yt0CzO4Dj1jZ+MzNbt/Xu3ZsBAwY0W3/llARMAgZK6iSpC9CosTJJR+vT35P0Jhu+fz+v2k+B/26gn87AnsCrktaX1DOVdwS+AcxO73vnNDsaqPdUgJlZcyiXU8TtUVM+u7K5MDAipkh6gOyq/deBKmBxI7r4DvAbScuAT8iG8Vfl/s40Iv5eT/vbJC0nu3BwZERMlbQx8EhKADoA/wBuSPV/IOlI4GPgPeD0hgLsu3U3qtbhW4WaWWl16tSJd999148TXgdFBO+++y6dOnVqVLuyuTAQQFKXiFiajsYnkv3kr/G3o2qjKisro6qqqtRhmNk66uOPP2b+/PmNfia9tQ2dOnVim222oWPHjp8pr+/CwLIZCUhGpJvudCK7ur/dJABmZmurY8eO7LDDDqUOw1pRWSUBETEo972k68lu3pOrN58//35tRNzckrGZmZm1trJKAvJFxDmljsHMzKxUyunXAWZmZpajrC4MbO8kLQFeKnUcZaYn2S2orfV4m7c+b/PW15zbfPuIKHhf+bI+HdAOvVTXFaDWMiRVeZu3Lm/z1udt3vpaa5v7dICZmVmZchJgZmZWppwEtC8jSh1AGfI2b33e5q3P27z1tco294WBZmZmZcojAWZmZmXKSYCZmVmZchLQTkg6StJLkv4paVip42mPJG0r6QlJcyQ9L+k/U/nFkhZImpFeXy91rO2JpBpJ1WnbVqWy7pLGSXol/d2s1HG2F5K+mPNdniHpA0lD/T1vXpL+LOktSbNzyur8Xkv6afr//SVJX222OHxNwLpPUgfgZeDLwHxgCnBKRLxQ0sDaGUm9gF4RMU1SV2AqcBxwIrA0Iq4uZXztlaQaoDIi3skpuxJYFBHDU9K7WUT8pFQxtlfp/5YFwH7AGfh73mwkHQosBW6JiD6prOD3Oj34bjSwL7AV2WPnd46IVWsbh0cC2od9gX9GxGsR8RFwB3BsiWNqdyJiYe2TJyNiCTAH2Lq0UZWtY4FRaXoUWTJmze8I4NWIeL3UgbQ3ETERWJRXXNf3+ljgjohYGRFzgX+S/b+/1pwEtA9bA/Ny3s/HO6cWJakC2BN4NhX9QNKsNMTnoenmFcCjkqZKGpLKtoyIhZAlZ8AWJYuufTuZ7Ai0lr/nLauu73WL/R/vJKB9UIEyn+dpIZK6AH8FhkbEB8AfgB2B/sBC4Neli65dOigi9gK+BpyThlGthUnaADgGuDsV+XteOi32f7yTgPZhPrBtzvttgDdLFEu7JqkjWQJwW0TcCxAR/4qIVRGxGriBZhqms0xEvJn+vgXcR7Z9/5Wu0ai9VuOt0kXYbn0NmBYR/wJ/z1tJXd/rFvs/3klA+zAF6C1ph5S9nww8UOKY2h1JAm4C5kTENTnlvXKqfROYnd/WmkbSxukiTCRtDHyFbPs+AJyeqp0O3F+aCNu1U8g5FeDveauo63v9AHCypA0l7QD0Bp5rjgX61wHtRPq5zv8CHYA/R8TlpY2o/ZF0MPAkUA2sTsX/TfafZX+y4bka4N9rz+vZ2pH0BbKjf8ieenp7RFwuqQdwF7Ad8Abw7YjIv8jKmkhSZ7Jz0F+IiMWp7Fb8PW82kkYDA8geGfwv4BfAGOr4Xkv6GfBd4BOyU5F/b5Y4nASYmZmVJ58OMDMzK1NOAszMzMqUkwAzM7My5STAzMysTDkJMDMzK1NOAswaSdKq9BS12ZIelLRpA/UvlnR+A3WOSw8JqX1/qaQjmyHWkZJOWNt+GrnMoeknZm2GpF3SZzZd0o5582okPZlXNqP26W6SKiX9thliqMh9YlzevBtzP/+WJmlLSbdLei3djvlpSd9sreVb2+EkwKzxlkdE//Tkr0XAOc3Q53HAmp1ARFwUEf9ohn5bVXrq3FCgTSUBZNv3/ojYMyJeLTC/q6RtASTtmjsjIqoi4rxiF5S2QaNExFmt9dTPdNOrMcDEiPhCROxNdoOxbVp4ueu3ZP/WNE4CzNbO06QHeUjaUdLD6cjqSUm75FeW9D1JUyTNlPRXSZ0lHUh2j/ar0hHojrVH8JK+JumunPYDJD2Ypr+SjuCmSbo7PdOgTumI939SmypJe0l6RNKrkr6f0/9ESfdJekHSHyWtl+adIqk6jYBckdPv0jRy8SzwM7JHnT4h6Yk0/w9pec9LuiQvnktS/NW120tSF0k3p7JZko4vdn0l9Zf0TGp3n6TN0o20hgJn1cZUwF3ASWk6/055AySNbSC23G1wgKQfpu00W9LQnOWsL2lUantP7YiJpPGSKovYzlek79c/JO2b2r0m6ZhUp4Okq9J3bJakfy+wrl8CPoqIP9YWRMTrEXFdfX2k7TA+xf2ipNtSQoGkvSVNSLE9ok9vfTs+fecmAP8paaCkZ5WNyPxD0pZ1fB7WWiLCL7/8asSL7JnqkN2d8W7gqPT+MaB3mt4PeDxNXwycn6Z75PRzGXBumh4JnJAzbyRwAtld8t4ANk7lfwD+H9ldxibmlP8EuKhArGv6JbvL29lp+jfALKArsDnwViofAKwAvpDWb1yKY6sUx+YppseB41KbAE7MWWYN0DPnffec7TUe2COnXu36/wdwY5q+AvjfnPabNWJ9ZwGHpelLa/vJ/QwKtKkBdgaeSu+nk43KzM7ZJmPrii1/GwB7k91VcmOgC/A82RMnK1K9g1K9P/Pp92I8UFnEdv5amr4PeBToCPQDZqTyIcDP0/SGQBWwQ976ngf8pp7vd8E+0nZYTDZisB5ZAnxwiuEpYPPU5iSyu5bWrtfv8z7L2pvUnQX8utT/nsv95eEZs8bbSNIMsv/UpwLj0lHpgcDd6eAIsv9A8/WRdBmwKdkO4pH6FhQRn0h6GBgo6R7gaODHwGFkO6rJaXkbkP2n3JDaZ0pUA10iYgmwRNIKfXptw3MR8RqsubXpwcDHwPiIeDuV3wYcSjasvIrsoUp1OVHZI4DXB3qluGelefemv1OBb6XpI8mGp2u3wXuSvtHQ+krqBmwaERNS0Sg+fQJeQxYB70k6GZgDLKuj3udiS5O52+Bg4L6I+DDFdS9wCNm2nxcRk1O9v5DtkK/O6X8f6t7OHwEPp3rVwMqI+FhSNdl3EbJnK+yhT68D6UZ2n/m5da24pOtTzB9FxD719PER2Xdjfmo3Iy33faAP2b8DyJK93NsJ35kzvQ1wZxop2KC+uKx1OAkwa7zlEdE/7XTGkl0TMBJ4PyL6N9B2JNmR3UxJg8mOrhpyZ1rGImBKRCxJw7DjIuKURsa+Mv1dnTNd+772/4P8e4kHhR9lWmtFRKwqNEPZw07OB/ZJO/ORQKcC8azKWb4KxNDU9W2MO4HrgcH11CkUG3x2G9S3rQpt2/z+6/JxpENocj6/iFitT8+3i2x0pb7k8nng+DUBRJwjqSfZEX+dfUgawGe/M7WfmYDnI+KAOpb3Yc70dcA1EfFA6u/ieuK0VuBrAsyaKLIHq5xHtpNbDsyV9G3ILr6S1K9As67AQmWPJD41p3xJmlfIeGAv4Ht8elT1DHCQpJ3S8jpL2nnt1miNfZU9kXI9sqHdScCzwGGSeiq78O0UYEId7XPXZROyncDidP73a0Us/1HgB7VvJG1GEeubPo/3JB2Sir5TT4yF3AdcSf2jM4ViyzcROC7FuDHZE/dqf32wnaTaneUpZNs2V2O2cyGPAGen7xeSdk4x5Hoc6CTp7Jyy3As5i+kj10vA5rXrJamjpN3rqNsNWJCmT6+jjrUiJwFmayEipgMzyYaITwXOlDST7Gjr2AJNLiT7j34c8GJO+R3ABSrwE7Z0hDmWbAc6NpW9TXbEOlrSLLKd5OcuRGyip4HhZI+KnUs2tL0Q+CnwBNn6TouIuh7fOwL4u6QnImIm2Tn258nOgU+uo02uy4DN0oVxM4HDG7G+p5NdYDmL7Il3lxaxPAAiYklEXBERHzUmtgL9TCMb8XmO7LO+MX1PIDvVcHqKrzvZNR65bRuznQu5EXgBmKbs54h/Im/EN40mHEeWbMyV9BzZqZOfFNtHXn8fkV03ckXaJjPITo0VcjHZKbMngXcasV7WQvwUQTNbIw3Rnh8R3yhxKGbWCjwSYGZmVqY8EmBmZlamPBJgZmZWppwEmJmZlSknAWZmZmXKSYCZmVmZchJgZmZWpv4/BCkSySRgkSsAAAAASUVORK5CYII=\n", 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PearsonRResult(statistic=0.5049150782274138, pvalue=8.462561224657758e-08)\n", "Slope and P-value = PearsonRResult(statistic=0.3463645505268579, pvalue=0.00041551583678816776)\n", "DLM 398\n", "3Days 103\n", "AIAO 89\n", "Daily 25\n", "Yearly 15\n", "Weekly 15\n", "Name: BroodCleanFrequency, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.8714969201290478, pvalue=4.3412881861670765e-32)\n", "Slope and P-value = PearsonRResult(statistic=0.9574705698881855, pvalue=1.0533085866958476e-54)\n", "Slope and P-value = PearsonRResult(statistic=0.9704726933087388, pvalue=2.4710780945448993e-62)\n", "Slope and P-value = PearsonRResult(statistic=-0.3280626531748775, pvalue=0.0008619243495854516)\n", "Slope and P-value = PearsonRResult(statistic=-0.8121443660714013, pvalue=1.1540001619128298e-24)\n", "Slope and P-value = PearsonRResult(statistic=-0.6147118413919128, pvalue=1.0228652294852462e-11)\n", "Slope and P-value = PearsonRResult(statistic=-0.547685773724972, pvalue=3.6991609154284265e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.8385169614832939, pvalue=1.3709611282641207e-27)\n", "Slope and P-value = PearsonRResult(statistic=-0.8195791015172854, pvalue=1.9326880363649267e-25)\n", "Slope and P-value = PearsonRResult(statistic=0.7618155953941012, pvalue=3.476142240959257e-20)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'BroodCleanFrequency','SampleType','PastureTime']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType','BroodCleanFrequency','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns'),sample.BroodCleanFrequency,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " print(pd.value_counts(sample['BroodCleanFrequency']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"BroodCleanFrequency in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','BroodCleanFrequency','SampleType','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " #display(prelim2_plot.nlargest(10))\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"BroodCleanFrequency_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (8) BrMedicated" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 881)\n", "Soil (695, 881) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "N 683\n", "Bac 15\n", "Name: BrMedicated, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.6971239612624773, pvalue=7.859127267894477e-16)\n", "Slope and P-value = PearsonRResult(statistic=0.16408248720987856, pvalue=0.10283293087463966)\n", "Slope and P-value = PearsonRResult(statistic=-0.4818585031463753, pvalue=3.8587668419610523e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.4421810713731827, pvalue=4.105889934728898e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.6234582336711283, pvalue=4.268603332665374e-12)\n", "Slope and P-value = PearsonRResult(statistic=-0.5412321969614393, pvalue=5.571199491803355e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.6798611244315652, pvalue=7.376664239208724e-15)\n", "Slope and P-value = PearsonRResult(statistic=-0.29360009643561297, pvalue=0.0030295953230897767)\n", "Slope and P-value = PearsonRResult(statistic=-0.8286655267926675, pvalue=1.9402267058588213e-26)\n", "Slope and P-value = PearsonRResult(statistic=-0.9774619322544924, pvalue=0.13541679539813126)\n", "N 680\n", "Bac 15\n", "Name: BrMedicated, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.9146913004191741, pvalue=2.4244483799480333e-40)\n", "Slope and P-value = PearsonRResult(statistic=-0.402366131738663, pvalue=0.0001228123025312273)\n", "Slope and P-value = PearsonRResult(statistic=-0.559872337253899, pvalue=1.1976091934138623e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.41135984453052454, pvalue=2.1216448526911096e-05)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/mosesayoola/.local/lib/python3.10/site-packages/scipy/stats/_stats_py.py:4424: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n", " warnings.warn(stats.ConstantInputWarning(msg))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "Slope and P-value = PearsonRResult(statistic=-0.6550537997619038, pvalue=3.2313266268520884e-08)\n", "Slope and P-value = PearsonRResult(statistic=-0.7222439269732954, pvalue=0.027981724129649596)\n", "Slope and P-value = PearsonRResult(statistic=-0.7872173347669803, pvalue=2.7043646859106077e-22)\n", "Slope and P-value = PearsonRResult(statistic=-0.8700354192046923, pvalue=7.283931914156018e-32)\n", "Slope and P-value = PearsonRResult(statistic=-0.6371057684840428, pvalue=0.17364316082168174)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'BrMedicated','SampleType','PastureTime']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType','BrMedicated','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns'),sample.BrMedicated,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " print(pd.value_counts(sample['BrMedicated']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"BrMedicated in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','BrMedicated','SampleType','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " #display(prelim2_plot.nlargest(10))\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"BrMedicated_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (9) AvgAgeToPasture" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 881)\n", "Soil (695, 881) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "3 425\n", "4 273\n", "Name: AvgAgeToPasture, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.2496445120905797, pvalue=0.012250217161211656)\n", "Slope and P-value = PearsonRResult(statistic=0.9208568341326883, pvalue=7.141085015536715e-42)\n", "3 422\n", "4 273\n", "Name: AvgAgeToPasture, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.8010439622086974, pvalue=1.4419832225809659e-23)\n", "Slope and P-value = PearsonRResult(statistic=0.747262474470594, pvalue=4.3126228042177866e-19)\n", "Slope and P-value = PearsonRResult(statistic=0.9286882723394405, pvalue=5.240662399665214e-44)\n", "Slope and P-value = PearsonRResult(statistic=0.8994191025351742, pvalue=5.320057549758514e-37)\n", "Slope and P-value = PearsonRResult(statistic=0.9763144601852402, pvalue=5.7815440704181707e-67)\n", "Slope and P-value = PearsonRResult(statistic=0.7873992186826345, pvalue=2.6057673335341443e-22)\n", "Slope and P-value = PearsonRResult(statistic=-0.9364984671314688, pvalue=2.1546273488437542e-46)\n", "Slope and P-value = PearsonRResult(statistic=-0.8458645971482482, pvalue=1.6873984790946492e-28)\n", "Slope and P-value = PearsonRResult(statistic=-0.9575243247855199, pvalue=9.912925247772827e-55)\n", "Slope and P-value = PearsonRResult(statistic=0.9769997744938324, pvalue=1.3941677013469871e-67)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'AvgAgeToPasture','SampleType','PastureTime']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType','AvgAgeToPasture','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns'),sample.AvgAgeToPasture,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " print(pd.value_counts(sample['AvgAgeToPasture']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"AvgAgeToPasture in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','AvgAgeToPasture','SampleType','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " #display(prelim2_plot.nlargest(10))\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"AvgAgeToPasture_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (10) PastureHousing" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 881)\n", "Soil (695, 881) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "CT 353\n", "CTF 255\n", "CTFR 90\n", "Name: PastureHousing, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.2977634029832889, pvalue=0.0026231732291782895)\n", "Slope and P-value = PearsonRResult(statistic=-0.6288894911890816, pvalue=2.446736260700131e-12)\n", "CT 351\n", "CTF 255\n", "CTFR 89\n", "Name: PastureHousing, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.9680087958711793, pvalue=1.1826929473297005e-60)\n", "Slope and P-value = PearsonRResult(statistic=0.7886908613788515, pvalue=1.999568646927132e-22)\n", "Slope and P-value = PearsonRResult(statistic=0.5343164758798802, pvalue=1.0314145333541963e-08)\n", "Slope and P-value = PearsonRResult(statistic=-0.7071121304207726, pvalue=1.9984698506096414e-16)\n", "Slope and P-value = PearsonRResult(statistic=0.5237625179779247, pvalue=2.2467836496220915e-08)\n", "Slope and P-value = PearsonRResult(statistic=0.8366491784107137, pvalue=2.2964791074025667e-27)\n", "Slope and P-value = PearsonRResult(statistic=-0.9537215326699955, pvalue=6.042362730746774e-53)\n", "Slope and P-value = PearsonRResult(statistic=0.7284017714265478, pvalue=8.832741593930674e-18)\n", "Slope and P-value = PearsonRResult(statistic=-0.8837729738519942, pvalue=4.3064354045992555e-34)\n", "Slope and P-value = PearsonRResult(statistic=-0.9004749882242183, pvalue=3.255858648566756e-37)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'PastureHousing','SampleType','PastureTime']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType','PastureHousing','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns'),sample.PastureHousing,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " print(pd.value_counts(sample['PastureHousing']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"PastureHousing in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','PastureHousing','SampleType','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " #display(prelim2_plot.nlargest(10))\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"PastureHousing_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "#print(f1_score(y_test, y_pred, pos_label= \"Bac\", average='micro'))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (10) FreqHousingMove" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 881)\n", "Soil (695, 881) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "Daily 683\n", "2Days 15\n", "Name: FreqHousingMove, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.8223543395192361, pvalue=9.711558138470084e-26)\n", "Slope and P-value = PearsonRResult(statistic=-0.7021011191514562, pvalue=4.0005207925773675e-16)\n", "Slope and P-value = PearsonRResult(statistic=-0.9568953168518931, pvalue=2.006624137017461e-54)\n", "Slope and P-value = PearsonRResult(statistic=0.17234549688642783, pvalue=0.08641085291486883)\n", "Slope and P-value = PearsonRResult(statistic=-0.9201426826285706, pvalue=1.0897919280570386e-41)\n", "Slope and P-value = PearsonRResult(statistic=-0.8767463474432269, pvalue=1.899144812109752e-19)\n", "Slope and P-value = PearsonRResult(statistic=-0.5630757631727284, pvalue=1.0731676683459003e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.2343294194712276, pvalue=0.018945296879021408)\n", "Slope and P-value = PearsonRResult(statistic=0.5981897122242911, pvalue=4.962740762157943e-11)\n", "Slope and P-value = PearsonRResult(statistic=-0.7038652257520144, pvalue=1.431617019050076e-14)\n", "Daily 680\n", "2Days 15\n", "Name: FreqHousingMove, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/home/mosesayoola/.local/lib/python3.10/site-packages/scipy/stats/_stats_py.py:4424: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n", " warnings.warn(stats.ConstantInputWarning(msg))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "Slope and P-value = PearsonRResult(statistic=-0.8793696243471643, pvalue=2.3864851009057848e-33)\n", "Slope and P-value = PearsonRResult(statistic=-0.8803610936095617, pvalue=1.6325909725904155e-33)\n", "Slope and P-value = PearsonRResult(statistic=0.33945803905315164, pvalue=0.0005501047295605743)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/mosesayoola/.local/lib/python3.10/site-packages/scipy/stats/_stats_py.py:4424: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n", " warnings.warn(stats.ConstantInputWarning(msg))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "Slope and P-value = PearsonRResult(statistic=-0.7630675418571053, pvalue=2.775717745464156e-20)\n", "Slope and P-value = PearsonRResult(statistic=0.2519875635353055, pvalue=0.011433986489410552)\n", "Slope and P-value = PearsonRResult(statistic=-0.8877321814534345, pvalue=1.4654464432034055e-22)\n", "Slope and P-value = PearsonRResult(statistic=-0.8713602209537512, pvalue=4.557810144007843e-32)\n", "Slope and P-value = PearsonRResult(statistic=-0.4097968678669046, pvalue=2.29623030435614e-05)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'FreqHousingMove','SampleType','PastureTime']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType','FreqHousingMove','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns'),sample.FreqHousingMove,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " print(pd.value_counts(sample['FreqHousingMove']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"FreqHousingMove in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','FreqHousingMove','SampleType','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " #display(prelim2_plot.nlargest(10))\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"FreqHousingMove_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (11) AlwaysNewPasture" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 881)\n", "Soil (695, 881) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "Y 608\n", "N 90\n", "Name: AlwaysNewPasture, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=-0.8721614083705231, pvalue=3.423924752107724e-32)\n", "Slope and P-value = PearsonRResult(statistic=-0.9339488502669494, pvalue=1.3938152113515054e-45)\n", "Y 606\n", "N 89\n", "Name: AlwaysNewPasture, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.9754330208551089, pvalue=3.3922644385092135e-66)\n", "Slope and P-value = PearsonRResult(statistic=0.3171425889945738, pvalue=0.001304572304958064)\n", "Slope and P-value = PearsonRResult(statistic=-0.8515643522238342, pvalue=3.077122414326055e-29)\n", "Slope and P-value = PearsonRResult(statistic=-0.9671582541824145, pvalue=4.1921167682924484e-60)\n", "Slope and P-value = PearsonRResult(statistic=-0.9230999977640508, pvalue=1.843752437408594e-42)\n", "Slope and P-value = PearsonRResult(statistic=-0.8993109129761194, pvalue=5.592848771076826e-37)\n", "Slope and P-value = PearsonRResult(statistic=-0.9140475039690855, pvalue=3.449437853494378e-40)\n", "Slope and P-value = PearsonRResult(statistic=0.43559714204549005, pvalue=5.911298227429489e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.9126930146769097, pvalue=3.91344546084558e-37)\n", "Slope and P-value = PearsonRResult(statistic=-0.9102796636091376, pvalue=2.5736822637814784e-39)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'AlwaysNewPasture','SampleType','PastureTime']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType','AlwaysNewPasture','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns'),sample.AlwaysNewPasture,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " print(pd.value_counts(sample['AlwaysNewPasture']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"AlwaysNewPasture in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','AlwaysNewPasture','SampleType','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " #display(prelim2_plot.nlargest(10))\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"AlwaysNewPasture_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (12) PaGMOFree" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 881)\n", "Soil (695, 881) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "Y 468\n", "N 230\n", "Name: PaGMOFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=-0.7785934735451049, pvalue=1.5108424178375378e-21)\n", "Slope and P-value = PearsonRResult(statistic=-0.9456373493642731, pvalue=1.3266175425505276e-49)\n", "Y 468\n", "N 227\n", "Name: PaGMOFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.8223079950599088, pvalue=9.824771006059411e-26)\n", "Slope and P-value = PearsonRResult(statistic=0.6324332652084063, pvalue=1.6916972912142595e-12)\n", "Slope and P-value = PearsonRResult(statistic=0.8528743423663995, pvalue=2.0601838266079441e-29)\n", "Slope and P-value = PearsonRResult(statistic=0.6794591430287396, pvalue=7.757421485520387e-15)\n", "Slope and P-value = PearsonRResult(statistic=-0.35085279293903676, pvalue=0.014485874583503864)\n", "Slope and P-value = PearsonRResult(statistic=0.38807463192720115, pvalue=6.621717718350003e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.9761516865470644, pvalue=8.055764906091392e-67)\n", "Slope and P-value = PearsonRResult(statistic=-0.8941063146022724, pvalue=5.810229577292394e-36)\n", "Slope and P-value = PearsonRResult(statistic=-0.9820205493619767, pvalue=9.021571502446008e-73)\n", "Slope and P-value = PearsonRResult(statistic=0.7519591637520189, pvalue=1.9501086533110951e-19)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'PaGMOFree','SampleType','PastureTime']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType','PaGMOFree','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns'),sample.PaGMOFree,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " print(pd.value_counts(sample['PaGMOFree']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"PaGMOFree in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','PaGMOFree','SampleType','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " #display(prelim2_plot.nlargest(10))\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"PaGMOFree_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (13) PaSoyFree" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 881)\n", "Soil (695, 881) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "N 433\n", "Y 265\n", "Name: PaSoyFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.45674854793938313, pvalue=1.7834538083159425e-06)\n", "Slope and P-value = PearsonRResult(statistic=0.38001848999877563, pvalue=9.628625402151665e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.6534757459032287, pvalue=1.7091654696312086e-13)\n", "Slope and P-value = PearsonRResult(statistic=-0.6069329115127207, pvalue=2.1760940174834656e-11)\n", "Slope and P-value = PearsonRResult(statistic=0.06828770042770776, pvalue=0.4996248099152054)\n", "Slope and P-value = PearsonRResult(statistic=0.7092837499193195, pvalue=1.4726463296343853e-16)\n", "Slope and P-value = PearsonRResult(statistic=-0.17029179124620916, pvalue=0.09028345862745113)\n", "Slope and P-value = PearsonRResult(statistic=0.58887809824416, pvalue=1.1624057911715578e-10)\n", "Slope and P-value = PearsonRResult(statistic=-0.3773282095492749, pvalue=0.00010887447713256412)\n", "Slope and P-value = PearsonRResult(statistic=-0.04527639467725483, pvalue=0.6546576838090042)\n", "N 430\n", "Y 265\n", "Name: PaSoyFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.8838388530102371, pvalue=4.195320191383206e-34)\n", "Slope and P-value = PearsonRResult(statistic=-0.5771348442335008, pvalue=3.2741709427255946e-10)\n", "Slope and P-value = PearsonRResult(statistic=0.9202720704859746, pvalue=1.0097423041043848e-41)\n", "Slope and P-value = PearsonRResult(statistic=0.6227898518566666, pvalue=4.56779614552769e-12)\n", "Slope and P-value = PearsonRResult(statistic=0.5901547428380168, pvalue=1.0360349454567352e-10)\n", "Slope and P-value = PearsonRResult(statistic=0.9176026949576558, pvalue=4.749670644885186e-41)\n", "Slope and P-value = PearsonRResult(statistic=-0.939616990841993, pvalue=1.9706063815766535e-47)\n", "Slope and P-value = PearsonRResult(statistic=0.9617816700268933, pvalue=6.208516881481057e-57)\n", "Slope and P-value = PearsonRResult(statistic=0.46771701868338317, pvalue=9.275842709165313e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.6476096622824754, pvalue=3.2970260443763045e-13)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'PaSoyFree','SampleType','PastureTime']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType','PaSoyFree','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns'),sample.PaSoyFree,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " print(pd.value_counts(sample['PaSoyFree']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"PaSoyFree in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','PaSoyFree','SampleType','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " #display(prelim2_plot.nlargest(10))\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"PaSoyFree_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": { "scrolled": false }, "source": [ "# (14) PaMedicated" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 881)\n", "Soil (695, 881) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "N 683\n", "Bac 15\n", "Name: PaMedicated, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.857337331212308, pvalue=5.0976595050353495e-30)\n", "Slope and P-value = PearsonRResult(statistic=0.24249923832469825, pvalue=0.015061129313889192)\n", "Slope and P-value = PearsonRResult(statistic=-0.6950097679542022, pvalue=5.514920449346197e-09)\n", "Slope and P-value = PearsonRResult(statistic=-0.954810226271221, pvalue=1.9318613048264667e-53)\n", "Slope and P-value = PearsonRResult(statistic=-0.6637416412463419, pvalue=5.224640177579552e-14)\n", "Slope and P-value = PearsonRResult(statistic=-0.019178160140034164, pvalue=0.8497888588432617)\n", "Slope and P-value = PearsonRResult(statistic=-0.7028616665353309, pvalue=3.6038926963190474e-16)\n", "Slope and P-value = PearsonRResult(statistic=-0.7842643066316233, pvalue=4.917649093040809e-22)\n", "Slope and P-value = PearsonRResult(statistic=-0.7713556191946092, pvalue=0.005433277417234431)\n", "Slope and P-value = PearsonRResult(statistic=-0.6205485874159626, pvalue=4.742540312242687e-09)\n", "N 680\n", "Bac 15\n", "Name: PaMedicated, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.9571871922850892, pvalue=0.042812807714910806)\n", "Slope and P-value = PearsonRResult(statistic=-0.964252236685152, pvalue=0.03574776331484797)\n", "Slope and P-value = PearsonRResult(statistic=-0.7969527400597323, pvalue=3.514644401554722e-23)\n", "Slope and P-value = PearsonRResult(statistic=0.8658954772359941, pvalue=3.0515608140458372e-31)\n", "Slope and P-value = PearsonRResult(statistic=-0.9064280741483091, pvalue=2.2463511619105723e-12)\n", "Slope and P-value = PearsonRResult(statistic=-0.8624269676417424, pvalue=2.231843160693432e-10)\n", "Slope and P-value = PearsonRResult(statistic=-1.0, pvalue=1.0)\n", "Slope and P-value = PearsonRResult(statistic=-0.9361013820554728, pvalue=0.0006213915204198115)\n", "Slope and P-value = PearsonRResult(statistic=-0.9449365521726726, pvalue=2.4432633184976742e-49)\n", "Slope and P-value = PearsonRResult(statistic=-0.8343078687586442, pvalue=3.2364088891064877e-13)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'PaMedicated','SampleType','PastureTime']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType','PaMedicated','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns'),sample.PaMedicated,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " print(pd.value_counts(sample['PaMedicated']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"PaMedicated in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','PaMedicated','SampleType','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " #display(prelim2_plot.nlargest(10))\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"PaMedicated_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (15) LayerOnFarm" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 881)\n", "Soil (695, 881) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "Y 668\n", "N 30\n", "Name: LayersOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=-0.7577834616783715, pvalue=7.108852382935471e-20)\n", "Slope and P-value = PearsonRResult(statistic=-0.6711108359483898, pvalue=2.1675087123035638e-14)\n", "Y 665\n", "N 30\n", "Name: LayersOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.8687365108043086, pvalue=1.1477288118856259e-31)\n", "Slope and P-value = PearsonRResult(statistic=-0.5339055327183779, pvalue=1.063693927998771e-08)\n", "Slope and P-value = PearsonRResult(statistic=-0.865447823723835, pvalue=1.4889833500796754e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.9500053844199099, pvalue=2.4323066977724035e-51)\n", "Slope and P-value = PearsonRResult(statistic=0.4740673493620314, pvalue=6.286609467025e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.14960381874064724, pvalue=0.13738401224297253)\n", "Slope and P-value = PearsonRResult(statistic=-0.779463973273649, pvalue=1.2744693483611689e-21)\n", "Slope and P-value = PearsonRResult(statistic=-0.8011018514631217, pvalue=1.42370824175045e-23)\n", "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "Slope and P-value = PearsonRResult(statistic=-0.6820201701154323, pvalue=0.09144504412466656)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/mosesayoola/.local/lib/python3.10/site-packages/scipy/stats/_stats_py.py:4424: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n", " warnings.warn(stats.ConstantInputWarning(msg))\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'LayersOnFarm','SampleType','PastureTime']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType','LayersOnFarm','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns'),sample.LayersOnFarm,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " print(pd.value_counts(sample['LayersOnFarm']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"LayersOnFarm in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','LayersOnFarm','SampleType','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " #display(prelim2_plot.nlargest(10))\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"LayersOnFarm_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (16) CattleOnFarm" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 881)\n", "Soil (695, 881) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "Y 380\n", "N 318\n", "Name: CattleOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=-0.5377571647000248, pvalue=7.95561433130229e-09)\n", "Slope and P-value = PearsonRResult(statistic=-0.49758630742928117, pvalue=1.387482524795048e-07)\n", "Y 378\n", "N 317\n", "Name: CattleOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.971359007113896, pvalue=5.669119698421823e-63)\n", "Slope and P-value = PearsonRResult(statistic=-0.8895867231407462, pvalue=4.02634910376913e-35)\n", "Slope and P-value = PearsonRResult(statistic=0.9646195229375356, pvalue=1.515668150319331e-58)\n", "Slope and P-value = PearsonRResult(statistic=0.9521229827292791, pvalue=3.0701404654984e-52)\n", "Slope and P-value = PearsonRResult(statistic=0.9784813146472019, pvalue=5.529812812190287e-69)\n", "Slope and P-value = PearsonRResult(statistic=0.9132266577026457, pvalue=5.38598215505725e-40)\n", "Slope and P-value = PearsonRResult(statistic=-0.8899224554470521, pvalue=3.497249256906114e-35)\n", "Slope and P-value = PearsonRResult(statistic=0.6074334861348502, pvalue=2.0741850840408354e-11)\n", "Slope and P-value = PearsonRResult(statistic=0.8682314762885839, pvalue=1.367893832550474e-31)\n", "Slope and P-value = PearsonRResult(statistic=-0.6784347104228767, pvalue=8.815844927555341e-15)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'CattleOnFarm','SampleType','PastureTime']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType','CattleOnFarm','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns'),sample.CattleOnFarm,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " print(pd.value_counts(sample['CattleOnFarm']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"CattleOnFarm in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','CattleOnFarm','SampleType','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " #display(prelim2_plot.nlargest(10))\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"CattleOnFarm_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (17) SwineOnFarm" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 881)\n", "Soil (695, 881) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "Y 558\n", "N 140\n", "Name: SwineOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.24600962125293455, pvalue=0.013617075469590297)\n", "Slope and P-value = PearsonRResult(statistic=-0.5713147711136812, pvalue=7.903738882138164e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.604628758956279, pvalue=2.710766269036027e-11)\n", "Slope and P-value = PearsonRResult(statistic=0.266158379532919, pvalue=0.0074377178170041665)\n", "Slope and P-value = PearsonRResult(statistic=-0.9141450191885552, pvalue=3.2706220914016015e-40)\n", "Slope and P-value = PearsonRResult(statistic=0.6238089773222516, pvalue=4.119254652678769e-12)\n", "Slope and P-value = PearsonRResult(statistic=-0.3328414810926903, pvalue=0.000715464499370321)\n", "Slope and P-value = PearsonRResult(statistic=-0.9207811415153073, pvalue=7.469703856312559e-42)\n", "Slope and P-value = PearsonRResult(statistic=-0.8633846031084326, pvalue=7.10966737894614e-31)\n", "Slope and P-value = PearsonRResult(statistic=-0.21352545018497598, pvalue=0.03291860634343639)\n", "Y 556\n", "N 139\n", "Name: SwineOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.26014694839219493, pvalue=0.008950681432688462)\n", "Slope and P-value = PearsonRResult(statistic=0.6433197191779799, pvalue=5.283214992772975e-13)\n", "Slope and P-value = PearsonRResult(statistic=-0.8435270599831182, pvalue=3.3244957695337027e-28)\n", "Slope and P-value = PearsonRResult(statistic=-0.8112105195384282, pvalue=1.4363038017055649e-24)\n", "Slope and P-value = PearsonRResult(statistic=-0.7312140841095796, pvalue=5.722411524284312e-18)\n", "Slope and P-value = PearsonRResult(statistic=-0.7324015711286405, pvalue=1.159937956222092e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.9670377748618287, pvalue=5.001577028679626e-60)\n", "Slope and P-value = PearsonRResult(statistic=-0.9045028261947861, pvalue=4.74989298620475e-38)\n", "Slope and P-value = PearsonRResult(statistic=0.5448385781636832, pvalue=4.61934381823754e-09)\n", "Slope and P-value = PearsonRResult(statistic=-0.8561868854363568, pvalue=7.33959855127414e-30)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'SwineOnFarm','SampleType','PastureTime']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType','SwineOnFarm','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns'),sample.SwineOnFarm,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " print(pd.value_counts(sample['SwineOnFarm']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"SwineOnFarm in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','SwineOnFarm','SampleType','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " #display(prelim2_plot.nlargest(10))\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"SwineOnFarm_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (18) GoatOnFarm" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 881)\n", "Soil (695, 881) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "WS 633\n", "PB 50\n", "SDSP 15\n", "Name: BroodBedding, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.34892914910203904, pvalue=0.000373786157932269)\n", "Slope and P-value = PearsonRResult(statistic=0.8407459472072671, pvalue=7.343517197154508e-28)\n", "Slope and P-value = PearsonRResult(statistic=0.138982103106935, pvalue=0.16787921340899073)\n", "Slope and P-value = PearsonRResult(statistic=-0.19832041642730247, pvalue=0.04793281110908223)\n", "Slope and P-value = PearsonRResult(statistic=-0.3378862025251664, pvalue=0.0005858548249876362)\n", "Slope and P-value = PearsonRResult(statistic=0.9069430794975963, pvalue=1.4188646879526277e-38)\n", "Slope and P-value = PearsonRResult(statistic=-0.6032403602526397, pvalue=3.091841837114909e-11)\n", "Slope and P-value = PearsonRResult(statistic=0.37467261356931353, pvalue=0.00012278525389183349)\n", "Slope and P-value = PearsonRResult(statistic=-0.186143826644275, pvalue=0.06369956760882096)\n", "Slope and P-value = PearsonRResult(statistic=-0.3616943902485944, pvalue=0.00021775623968742908)\n", "WS 630\n", "PB 50\n", "SDSP 15\n", "Name: BroodBedding, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.9192110333844078, pvalue=1.8805070492376125e-41)\n", "Slope and P-value = PearsonRResult(statistic=0.9463705828696319, pvalue=6.941527143704836e-50)\n", "Slope and P-value = PearsonRResult(statistic=0.9484490949562538, pvalue=1.0521170472116062e-50)\n", "Slope and P-value = PearsonRResult(statistic=-0.17448327867852217, pvalue=0.08252122288099727)\n", "Slope and P-value = PearsonRResult(statistic=0.6859461346381845, pvalue=3.4096805832737843e-15)\n", "Slope and P-value = PearsonRResult(statistic=0.8224256204096022, pvalue=9.539907583750396e-26)\n", "Slope and P-value = PearsonRResult(statistic=-0.3845591990548416, pvalue=0.34688615155720887)\n", "Slope and P-value = PearsonRResult(statistic=0.49100742510202294, pvalue=2.1416679537608902e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.9242874618406385, pvalue=8.85369450230774e-43)\n", "Slope and P-value = PearsonRResult(statistic=-0.23873599150779448, pvalue=0.016754916444135463)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'BroodBedding','SampleType','PastureTime']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType','BroodBedding','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns'),sample.BroodBedding,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " print(pd.value_counts(sample['BroodBedding']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"BroodBedding in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','BroodBedding','SampleType','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " #display(prelim2_plot.nlargest(10))\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"BroodBedding_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (19) SheepOnFarm" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 881)\n", "Soil (695, 881) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "WS 633\n", "PB 50\n", "SDSP 15\n", "Name: BroodBedding, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=0.9716674605203587, pvalue=3.3595747753121156e-63)\n", "Slope and P-value = PearsonRResult(statistic=0.03508096770122241, pvalue=0.7289626135859902)\n", "WS 630\n", "PB 50\n", "SDSP 15\n", "Name: BroodBedding, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.9557971699189696, pvalue=6.704462120232797e-54)\n", "Slope and P-value = PearsonRResult(statistic=0.9403016922002512, pvalue=1.1458233324993792e-47)\n", "Slope and P-value = PearsonRResult(statistic=0.7959741128823928, pvalue=4.3363570115261e-23)\n", "Slope and P-value = PearsonRResult(statistic=0.8158349315838609, pvalue=4.801297202768918e-25)\n", "Slope and P-value = PearsonRResult(statistic=0.6809800529294152, pvalue=6.409684453036498e-15)\n", "Slope and P-value = PearsonRResult(statistic=0.6922858792899813, pvalue=1.4954458535025383e-15)\n", "Slope and P-value = PearsonRResult(statistic=0.9665668154023587, pvalue=9.911019597450273e-60)\n", "Slope and P-value = PearsonRResult(statistic=0.9461914365561455, pvalue=8.138500012730246e-50)\n", "Slope and P-value = PearsonRResult(statistic=-0.5739178403514996, pvalue=4.3174210184551387e-10)\n", "Slope and P-value = PearsonRResult(statistic=-0.6864457788970935, pvalue=3.197664096460086e-15)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'BroodBedding','SampleType','PastureTime']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType','BroodBedding','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns'),sample.BroodBedding,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " print(pd.value_counts(sample['BroodBedding']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"BroodBedding in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','BroodBedding','SampleType','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " #display(prelim2_plot.nlargest(10))\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"BroodBedding_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (20) WaterSource" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 881)\n", "Soil (695, 881) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "Public 330\n", "Well 318\n", "Rain 50\n", "Name: WaterSource1, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=-0.633439647785977, pvalue=1.5220700435907914e-12)\n", "Slope and P-value = PearsonRResult(statistic=0.16608038624240584, pvalue=0.0986541500363661)\n", "Public 329\n", "Well 316\n", "Rain 50\n", "Name: WaterSource1, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.9271377427817459, pvalue=1.4478441003379733e-43)\n", "Slope and P-value = PearsonRResult(statistic=-0.8390000024640624, pvalue=1.198453639112542e-27)\n", "Slope and P-value = PearsonRResult(statistic=0.895392640972172, pvalue=3.295702260120521e-36)\n", "Slope and P-value = PearsonRResult(statistic=-0.04809110614704379, pvalue=0.6346872695709083)\n", "Slope and P-value = PearsonRResult(statistic=-0.7972901633874185, pvalue=3.2681770617979294e-23)\n", "Slope and P-value = PearsonRResult(statistic=-0.5969273673747731, pvalue=5.5786256613261126e-11)\n", "Slope and P-value = PearsonRResult(statistic=0.7522579241490507, pvalue=1.8529917648679295e-19)\n", "Slope and P-value = PearsonRResult(statistic=0.9588206527952766, pvalue=2.2394119928536623e-55)\n", "Slope and P-value = PearsonRResult(statistic=-0.7088603901332257, pvalue=1.5633055171349694e-16)\n", "Slope and P-value = PearsonRResult(statistic=0.8944959378387495, pvalue=4.897140238392354e-36)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'WaterSource1','SampleType','PastureTime']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType','WaterSource1','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns'),sample.WaterSource1,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " print(pd.value_counts(sample['WaterSource1']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"WaterSource in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','WaterSource1','SampleType','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " #display(prelim2_plot.nlargest(10))\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"WaterSource_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (21) FreqBirdHandling" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 881)\n", "Soil (695, 881) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "WS 633\n", "PB 50\n", "SDSP 15\n", "Name: BroodBedding, dtype: int64\n" ] }, { "data": { "image/png": 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TDJ9I6g9cKWlDsjkIBwF/A66WNJtsNGNARHycNVlwX96W9AtgItlIwgMRcY+k3YARkqqSvl+kvyNT+8uBvYHTgNtTojAFuLqa2JZWd0B6dGlP5Tr+S2pmZrZuUETTHvmW1DYilkraiGwUYGBETGvsuJqyioqKqKysbOwwzMxsHSFpakRUFFrXpEckkuGSdiabszDKSYSZmdna0+QTiYg4Mfe5pGFklydydSO7HJHriogYsSZjMzMza+6afCKRLyIGNXYMZmZmLUVT/9aGmZmZNSInEmZmZlYyJxJmZmZWMicSZmZmVjInEmZmZlYyJxJmZmZWsmb39U+rv9lvLqZ8yP2NHYaZWbM0v5ndgsAjEmZmZlYyJxJmZmZWMicSJZA0UtKx9dj+S3fflLSVpDvq0eYkSRVpeX66/biZmdka5TkSa1G6LXnB+4hHxFtAycmJmZlZY2ixIxKSzpc0V9IESWMknVvP9tpKeljSNEmzJR2ZysslvSDpb8A0YJucbTpJekrSYanenFQ+QNJdkh6U9LKkP+Zsc3DaZpqk2yW1rSGuuyVNlfScpIH12UczM7N8LTKRSJcAjgF2B44GCt5jvY5WAEdFxB7AAcCf0ggEwFeBGyNi94h4LcWwJXA/cEFEFPqKRE+gP9AD6C9pm3S54tfAQamfSuAnNcR1ekR8nWwfz5G0WaFKkgZKqpRUuXLZ4jrstpmZtWQt9dJGb+CeiFgOIOneBmhTwO8l7Qd8DnQBtkzrXouIp3PqtgYeBgZFxKNF2ns4Ihan+J4HtgM6ADsDT6QcZQPgqRriOkfSUWl5G7Jbqi/MrxQRw4HhAG06d4sa2jQzMwNabiJRcJ5CPZ0EbA58PSI+lTQfKEvrPsqr+xkwFfg2UCyR+DhneSXZayVgQkScUJuAJPUBDgL2johlkiblxGRmZlZvLfLSBvA40FdSWZpj0BC/DtIeeDclEQeQjSAUE8DpwE6ShtShj6eBfSR1BZC0kaQda4hpUUoidgL2qkNfZmZmNWqRIxIRMUXSOGAm8BrZXIO6Tgy4RtJf0vIbQF/gXkmVwAxgbg0xrJR0fNrmQ+CBWsT9nqQBwBhJbVLxr4GXimzyIHCmpFnAi2SJiJmZWYNRRMu8HC6pbUQslbQRMBkYGBHTGjuudUFFRUVUVlY2dhhmZraOkDQ1Igp+MaFFjkgkwyXtTDZnYJSTCDMzs7prsYlERJyY+1zSMGCfvGrdgJfzyq6IiBFrMjYzM7OmosUmEvkiYlBjx2BmZtbUtNRvbZiZmVkDcCJhZmZmJXMiYWZmZiVzImFmZmYlcyJhZmZmJXMiYWZmZiVzImFmZmYl8+9I2JfMfnMx5UPub+wwzMzWafOHNsT9Hps+j0iYmZlZyZxImJmZWclqTCQkrZQ0Q9IcSbenu2XWiqQBkq4qsu7JGrYtl3RizvMKSX+tbd85250uabakWWkfjsyJbau6tldNP/3STcDMzMxajNqMSCyPiJ4R0R34BDgzd6WkVqV0HBHfrKFKObAqkYiIyog4py59SNoa+BXQOyJ2BfYCZqXVA4CCiUSJ+9QPcCJhZmYtSl0vbTwGdJXUR9JESTcDsyWVSRqRPvlPl3RAzjbbSHpQ0ouSLqwqlLQ0/ZWky9JowWxJ/VOVocC+aTTkx6nP+9I2bXP6myXpmCLxbgEsAZYCRMTSiJgn6VigAhid2t9Q0nxJF0h6HPiupIMlPSVpWhqJaZv6ni/pUknPpkdXSd8EjgAuS+3tIKmnpKdTfGMlbZq27yrpIUkzU9s7VHMMkPSzVDZT0tBq2lh1fFKdqyQNSMtDJT2fYrm80IGSNFBSpaTKlcsW1/xOMDMzow7f2pC0PnAo8GAq6gV0TyfmnwJERA9JOwHjJe2YWw9YBkyRdH9EVOY0fTTQE9gN6JTqTAaGAOdGxOGp/z4525wPLI6IHmndpkXCngm8A8yT9DBwV0TcGxF3SDortV+Z2gBYERG9JXUC7gIOioiPJP0c+AlwcWr3w4joJekU4C8RcbikccB9EXFHam8WcHZEPCrpYuBCYDAwGhgaEWMllZElc8WOQU+ykY5vRMQySR1T/4Xa2KbQAUjbHAXsFBEhqUOhehExHBgO0KZztyhyPM3MzFZTmxGJDSXNACqB14HrU/mzETEvLfcGbgKIiLnAa0BVIjEhIhZGxHKyk3PvvPZ7A2MiYmVEvAM8CuxZQ0wHAcOqnkTEokKVImIlcAhwLPAS8L+SLqqm3VvT373ILlM8kfb9VGC7nHpjcv7und+IpPZAh4h4NBWNAvaT1A7oEhFjU3wrImIZxY/BQcCIVIeI+KCaNor5EFgBXCfpaLKEzszMrEHUZkRieUT0zC1In94/yi2qZvv8T7f5z6vbthgVaKdw5xEBPAs8K2kCMAK4qEj1qn0SWQJ0QrFmiyzXpNi+Vlde2+P1GasnhmUAEfGZpF7AgcDxwFnAf9UqWjMzsxo01Nc/JwMnAaRLGtsCL6Z135LUUdKGZMP0TxTYtr+kVpI2B/YjO/EvAdoV6W882QmR1GfBSxuStpK0R05RT7LREmpo/2lgH0ldUzsb5VyqAeif8/ep/PYiYjGwSNK+ad3JwKMR8SGwQFK/1G4bZd+CKXYMxgOnpzpI6lhNG68BO6fn7ckSB9LcjvYR8QDZpZWeRfbZzMyszhrqly3/BlwtaTbZJ+MBEfFxGrl4nOyyR1fg5rz5EQBjyS4PzCT79P2ziPi3pIXAZ5JmAiOB6TnbXAIMkzQHWAn8huyySb7WwOXKvua5AniPL751MjLFvJy8yxMR8V6aqDhGUptU/GuyyyMAbSQ9Q5aIVY1a3AJcK+kcskspp6b2NwJeBU5L9U4GrknzJj4FvlvsGAAPSuoJVEr6BHgA+GWhNiLiVUm3kX0r5eWc49UOuCfNpRDw4wLHyczMrCTKRv6ttiTNByoi4v3GjmVNqaioiMrK/HzPzMxaKklTI6Ki0Dr/sqWZmZmVrNnctCtdamiTV3xyRMxuyH4iorwh2zMzM2vKmk0iERHfaOwYzMzMWhpf2jAzM7OSOZEwMzOzkjmRMDMzs5I5kTAzM7OSOZEwMzOzkjmRMDMzs5I5kTAzM7OSNZvfkbCGM/vNxZQPub+xwzCzZmb+0MMaOwRbAzwiYWZmZiVzImFmZmYlcyIBSBop6dh6ttFP0s4NFZOZmVlT4ESi4fQDnEiYmVmL0mwSCUnnS5oraYKkMZLOrWd7bSU9LGmapNmSjsxZd4qkWZJmSrpJ0jeBI4DLJM2QtIOknpKeTvXGSto0bdtV0kNp22mpriRdJmlO6qt/Tl8/S2UzJQ2tpo0+ku7L2e4qSQPS8lBJz6dYLi+yvwMlVUqqXLlscX0OnZmZtSDN4lsbkiqAY4DdyfZpGjC1ns2uAI6KiA8ldQKeljSObNThV8A+EfG+pI4R8UFad19E3JFimgWcHRGPSroYuBAYDIwGhkbEWEllZMnc0UBPYDegEzBF0uRU1g/4RkQsk9QxxVaojW2KHJuOwFHAThERkjoUqhcRw4HhAG06d4vSDpmZmbU0zSKRAHoD90TEcgBJ9zZAmwJ+L2k/4HOgC7Al8F/AHRHxPkBEfPClDaX2QIeIeDQVjQJul9QO6BIRY9O2K1L93sCYiFgJvCPpUWBPYH9gREQsq+qrmjaK7ceHZEnRdZLuB+4rVtHMzKyumsuljaJn0Xo4Cdgc+HpE9ATeAcpSX6V+Yi8WZ3Xl+X0Vq/sZq7+eZQAR8RnQC7iTbHTjwdoEamZmVhvNJZF4HOgrqUxSW6AhfvWkPfBuRHwq6QBgu1T+MHCcpM1g1aUDgCVAO4CIWAwskrRvWncy8GhEfAgskNQvbdtG0kbAZKC/pFaSNgf2A54FxgOnpzqkyyjF2ngN2Dk9bw8cmNa3BdpHxANkl1Z6NsCxMTMzA5rJpY2ImJLmKMwkO6FWAnWdMXiNpL+k5TeAvsC9kiqBGcDc1Ndzkn4HPCppJTAdGADcAlwr6RzgWOBU4Op0kn8VOC21fXLq62LgU+C7wFhg7xR/AD+LiH8DD0rqCVRK+gR4APhloTYi4lVJtwGzgJdTXJAlN/ekuRQCflzH42JmZlaUIprHvDpJbSNiac4n/IERMa2x42qKKioqorKysrHDMDOzdYSkqRFRUWhdsxiRSIanH4QqA0Y5iTAzM1vzmk0iEREn5j6XNAzYJ69aN7Jh/1xXRMSINRmbmZlZc9VsEol8ETGosWMwMzNr7prLtzbMzMysETiRMDMzs5I5kTAzM7OSOZEwMzOzkjmRMDMzs5I5kTAzM7OSOZEwMzOzkjXb35Gw0s1+czHlQ+5v7DDMrBHNH9oQ9z60lsAjEmZmZlYyJxJmZmZWsmaTSEhaX9L7kv6QVz5JUkVafkBShwbqr1zSnBrqDJB0VQP0dYSkIWn5IknnpuWRko6tb/tmZmalajaJBHAw8CJwnCQVqhAR34mI/6zVqBpARIyLiKGNHYeZmVm+dSaRkHS+pLmSJkgaU/Wpuw5OAK4AXgf2KtLHfEmd0vIpkmZJminpJkntJM2T1Dqt3yTVby2pq6SHUt1pknbIa7dM0ghJsyVNl3RAzuptJD0o6UVJF+Zsc7ekqZKekzQwp/yQ1MdMSQ+nshpHNvL2rULSpLS8v6QZ6TFdUrsi2w+UVCmpcuWyxdV1ZWZmtso68a2NdOnhGGB3spimAVPrsP2GwIHAD4EOZEnFU9XU3wX4FbBPRLwvqWNELEkn38OAu4HjgTsj4lNJo4GhETFWUhlZArZFTpODACKih6SdgPGSdkzregHdgWXAFEn3R0QlcHpEfJBinyLpztTutcB+ETFPUsfaHoNqnAsMiognJLUFVhSqFBHDgeEAbTp3iwbo18zMWoB1ZUSiN3BPRCyPiCXAvXXc/nBgYkQsA+4EjpLUqpr6/wXcERHvA0TEB6n8OuC0tHwaMCJ9gu8SEWNT3RWpn/z4b0rr5wKvAVWJxISIWBgRy4G7Ul2AcyTNBJ4GtgG6kY2kTI6IeXlx1ccTwJ8lnQN0iIjPGqBNMzMzYN1JJArOaaiDE4CDJM0nG8nYDDigmvoCvvSpOyKeAMol7Q+0iog5tYytujr5/YSkPsBBwN4RsRswHSgrFlctfcYXr2fZqs6yuRVnABsCT6cREzMzswaxriQSjwN901yDtmSXF2pF0iZkn/K3jYjyiCgnu9RwQjWbPUw2KXOz1EbuJYQbgTHACICI+BBYIKlfqttG0kZ57U0GTkrrdwS2JZv4CfAtSR3TJYx+ZCME7YFFEbEsndir5nQ8BewvafsCcdVkPvD1tHxMVaGkHSJidkRcClQCTiTMzKzBrBNzJCJiiqRxwEyyywKVQG1n/B0NPBIRH+eU3QP8UVKbIv09J+l3wKOSVpKNCAxIq0cDl5AlE1VOBq6RdDHwKfBd4POc9X8DrpY0m2xkYEBEfJy+PPI42WWPrsDNEVGZ6p0paRZZwvF0iuu9NPHyLknrAe8C36rlcfgNcL2kXwLP5JQPTpM/VwLPA/9XU0M9urSn0r9qZ2ZmtaCIdWNenaS2EbE0fdqfDAyMiGmNEMexwJERcfLa7ntdUVFREZWVlY0dhpmZrSMkTY2IikLr1okRiWS4pJ3Jru+PaqQk4krgUOA7a7tvMzOzpmidSSQi4sTc55KGAfvkVesGvJxXdkVEjGigGM5uiHbMzMxainUmkcgXEYMaOwYzMzOr3rryrQ0zMzNrgpxImJmZWcmcSJiZmVnJnEiYmZlZyZxImJmZWcmcSJiZmVnJ1tmvf1rjmf3mYsqH3N/YYZjZWjTfP4tvJfKIhJmZmZXMiYSZmZmVrEUmEpLOlTRX0hxJMyWdspb6PbOqL0mTJBW8AUod2xxc4LbmZmZma0WLSyQknUl2a+5eEdEd2A/Q2ug7Iq6OiBsbuNnBQJ0SCUmtGjgGMzNroZpkIiHp/DSiMEHSGEnn1mHzXwI/iogPASJicUSMSu0eKGm6pNmSbpDUJpXPl/QbSdPSup1S+cap3pS03ZGp/K+SLkjL35Y0WdJ6ki7Ki/V7kp5MIyO9Uv1eqWx6+vvVVN5K0uWp/1mSzpZ0DrAVMFHSxFTvYElPpVhvl9Q2Zx8ukPQ48N1Sj72ZmVmuJpdIpMsBxwC7A0cDtb48IKkd0C4iXimwrgwYCfSPiB5k32j575wq70fEHsDfgapk4FfAIxGxJ3AAcJmkjYEhQH9JBwB/BU6LiM8LhLRxRHwT+BFwQyqbC+wXEbsDFwC/T+UDge2B3SNiV2B0RPwVeAs4ICIOkNQJ+DVwUIq1EvhJTn8rIqJ3RNxSYP8HSqqUVLly2eIiR9DMzGx1TfHrn72BeyJiOYCke+uwrYAosu6rwLyIeCk9HwUMAv6Snt+V/k4lS2AADgaOyBllKAO2jYgXJP0AmAz8uFDikowBiIjJkjaR1AFoB4yS1C3F2jrVPQi4OiI+S9t8UKC9vYCdgSckAWwAPJWz/tYicRARw4HhAG06dyt2jMzMzFbTFBOJkuczRMSHkj6S9JWIeLWO7X6c/q7ki+Mm4JiIeLFA/R7AQrJLD0VDKvD8t8DEiDhKUjkwKaevmk7wAiZExAlF1n9Uw/ZmZmZ10uQubQCPA30llaXr/3X9FZU/AMMkbQKQRgIGkl1SKJfUNdU7GXi0hrb+CZyt9PFf0u7p73bAT8kuvxwq6RtFtu+f6vcGFkfEYqA98GZaPyCn7njgTEnrp206pvIlZKMYAE8D+1Ttg6SNJO1Ywz6YmZmVrMklEhExBRgHzCS73FAJ1OWi/t+BicAUSXPIkoVlEbECOA24XdJs4HPg6hra+i3ZpYdZqa3fpqTieuDciHgL+D5wXZqDkW+RpCdTP99PZX8E/iDpCSD32xXXAa+nvmYCJ6by4cD/SZoYEe+RJR9jJM0iSyx2qtVRMTMzK4Eimt7lcEltI2Jp+v2EycDAiJjW2HE1FxUVFVFZWdnYYZiZ2TpC0tSIKPjlhqY4RwJguKSdySY3jnISYWZm1jiaZCIRESfmPpc0DNgnr1o34OW8sisiYsSajM3MzKwlaZKJRL6IGNTYMZiZmbVETW6ypZmZma07nEiYmZlZyZxImJmZWcmcSJiZmVnJnEiYmZlZyZxImJmZWcmcSJiZmVnJmsXvSFjDmv3mYsqH3N/YYZjZWjB/aF3ve2i2Oo9ImJmZWcmcSJiZmVnJWkQiIWl9Se9L+kMt6h4haUiJ/XSQ9KNa1Osj6b5S+shrp6ek79S3HTMzs1K1iEQCOBh4EThOkqqrGBHjImJoif10AGpMJBpQT6BOiYQkz4sxM7MG0yQSCUnnS5oraYKkMZLOrWMTJwBXAK8De+W0e4ikaZJmSno4lQ2QdFVaHinpr5KelPSqpGNztj1P0hRJsyT9JhUPBXaQNEPSZcpcJmmOpNmS+ufEtImksZKel3S1pPVSu3+XVCnpuZx2kbRnimOmpGcltQcuBvqn/vpL2ljSDSmu6ZKOzNmn2yXdC4wvcowHpn4rVy5bXMfDa2ZmLdU6/+lUUgVwDLA7WbzTgKl12H5D4EDgh2QjBicAT0naHLgW2C8i5knqWKSJzkBvYCdgHHCHpIPJblPeCxAwTtJ+wBCge0T0TH0fQzZqsBvQCZgiaXJqtxewM/Aa8CBwNHAH8KuI+EBSK+BhSbsCc4Fbgf4RMUXSJsAy4AKgIiLOSv39HngkIk6X1AF4VtJDqb+9gV0j4oNCOxkRw4HhAG06d4saD6yZmRlNY0SiN3BPRCyPiCXAvXXc/nBgYkQsA+4Ejkon6b2AyRExD6DYCRa4OyI+j4jngS1T2cHpMZ0ssdmJLLEoFPuYiFgZEe8AjwJ7pnXPRsSrEbESGJPqQnb5ZVpqexeyZOOrwNsRMSXF+mFEfFagv4OBIZJmAJOAMmDbtG5CNftoZmZWknV+RILsE399nADsI2l+er4ZcEBqtzafvD8uEIuAP0TENbkVJZXnbVtd7Pl9h6TtgXOBPSNikaSRZMlAbWMVcExEvJgX1zeAj2qxvZmZWZ00hRGJx4G+ksoktQVq/esp6RJAb2DbiCiPiHJgEOnyBrB/OnlTzaWNQv4JnJ7iQVIXSVsAS4B2OfUmk81haJUupewHPJvW9ZK0fZob0T/t5yZkJ/zFkrYEDk115wJbSdoz9dcuTZrM7++fwNlVE0ol7V6HfTIzM6uzdT6RSMP544CZwF1AJVDb2YBHk80ZyB1VuAc4AvgQGAjcJWkm2RyE2sY0HriZbK7FbLK5De0iYiHwRJpceRkwFpiVYn8E+FlE/Ds18xTZ5Mw5wDxgbETMJLuk8RxwA/BE6u8TsmTjyhTrBLKRionAzlWTLYHfAq2BWZLmpOdmZmZrjCLW/Xl1ktpGxFJJG5F9yh8YEdMaO67mqqKiIiorKxs7DDMzW0dImhoRFYXWNYU5EgDDJe1M9il8lJMIMzOzdUOTSCQi4sTc55KGAfvkVesGvJxXdkVEjFiTsZmZmbVkTSKRyBcRgxo7BjMzM2sCky3NzMxs3eVEwszMzErmRMLMzMxK5kTCzMzMSuZEwszMzErmRMLMzMxK5kTCzMzMStYkf0fC1qzZby6mfMj9jR2GmTWQ+UNrfa9DszrziISZmZmVzImEmZmZlcyJRB5lfi3pZUkvSZooaZcS2tlK0h1puY+k+9LyAElXNXDMTzZke2ZmZrXlORJfNgj4JrBbRCyTdDAwTtIuEbGito1ExFvAsWsqyLy+vplfJqlVRKxcG/2bmVnL1SxHJCSdL2mupAmSxkg6tw6b/xw4OyKWAUTEeOBJ4CRJrSSNlDRH0mxJP079dZX0kKSZkqZJ2kFSuaQ5NcS5naSHJc1Kf7dN5d9NfcyUNDmVDZB0j6QHJb0o6cKcdpamv33SCMrNwOxUdrekqZKekzSwmlgGSqqUVLly2eI6HC4zM2vJmt2IhKQK4Bhgd7L9mwZMreW2mwAbR8QreasqgV2AnkCXiOie6ndI60cDQyNirKQysgRti1p0eRVwY0SMknQ68FegH3AB8O2IeDOnD4BeQHdgGTBF0v0RUZnXZi+ge0TMS89Pj4gPJG2YtrkzIhbmBxIRw4HhAG06d4taxG5mZtYsRyR6A/dExPKIWALc2wBtCgjgVeArkq6UdAjwoaR2ZMnFWICIWFE1mlELewM3p+WbUuwATwAjJf0AaJVTf0JELIyI5cBdOfVzPZuTRACcI2km8DSwDdCtlrGZmZnVqDkmEip1w4j4EPhI0lfyVu0BPB8Ri4DdgElkcymuq09/hUJIcZwJ/JrsxD9D0ma56/Pr5/moakFSH+AgYO+I2A2YDpQ1YLxmZtbCNcdE4nGgr6QySW2Buv4Sy2XAX9OlACQdRPbJ/2ZJnYD1IuJO4Hxgj5R8LJDUL9VvI2mjWvb1JHB8Wj4pxY6kHSLimYi4AHifLKEA+Jakjim2fmQjF9VpDyxKk0Z3AvaqZVxmZma10uzmSETEFEnjgJnAa2TzG+oye/BKYFNgtqSVwL+BIyNiuaQdgRGSqhKwX6S/JwPXSLoY+BT4LvB5Lfo6B7hB0nnAe8BpqfwySd3IRjseTvvSkyzRuAnoCtxcYH5EvgeBMyXNAl4ku7xhZmbWYBTR/ObVSWobEUvTyMBkYGBETGvsuOpD0gCgIiLOWtN9VVRURGVlTTmKmZm1FJKmRkRFoXXNbkQiGS5pZ7L5AKOaehJhZma2rmqWiUREnJj7XNIwYJ+8at2Al/PKroiIEWsytlJFxEhgZCOHYWZmtppmmUjki4hBjR2DmZlZc9QiEgkzM1t7Pv30UxYsWMCKFbW+q4CtI8rKyth6661p3bp1rbdxImFmZg1qwYIFtGvXjvLycqSG/KkdW5MigoULF7JgwQK23377Wm/XHH9HwszMGtGKFSvYbLPNnEQ0MZLYbLPN6jyS5ETCzMwanJOIpqmU182JhJmZmZXMcyTMzGyNKh9yf4O2N39o7e58MHbsWI4++mheeOEFdtppJwAmTZrE5Zdfzn333beq3oABAzj88MM59thj6dOnD2+//TZlZWVssMEGXHvttfTs2ROAxYsXc/bZZ/PEE9ndCfbZZx+uvPJK2rdvD8BLL73E4MGDeemll2jdujU9evTgyiuvZMsttyx5Xz/44AP69+/P/PnzKS8v57bbbmPTTTf9Ur0rrriCa6+9lojgBz/4AYMHD15t/eWXX855553He++9R6dOnZg9ezZ/+tOfGDlyZMmxVXEiYV8y+83FDf4P36ylqu1JzxremDFj6N27N7fccgsXXXRRrbcbPXo0FRUVjBgxgvPOO48JEyYA8P3vf5/u3btz4403AnDhhRdyxhlncPvtt7NixQoOO+ww/vznP9O3b18AJk6cyHvvvVevRGLo0KEceOCBDBkyhKFDhzJ06FAuvfTS1erMmTOHa6+9lmeffZYNNtiAQw45hMMOO4xu3bKbPb/xxhtMmDCBbbfddtU2PXr0YMGCBbz++uurlZfClzbMzKzZWbp0KU888QTXX389t9xyS0lt7L333rz55psA/Otf/2Lq1Kmcf/75q9ZfcMEFVFZW8sorr3DzzTez9957r0oiAA444AC6d+9er/245557OPXUUwE49dRTufvuu79U54UXXmCvvfZio402Yv3112f//fdn7Nixq9b/+Mc/5o9//OOX5j/07du35GOTy4mEmZk1O3fffTeHHHIIO+64Ix07dmTatLrfKeHBBx+kX79+ADz//PP07NmTVq1arVrfqlUrevbsyXPPPcecOXP4+te/XmObS5YsoWfPngUfzz///Jfqv/POO3Tu3BmAzp078+67736pTvfu3Zk8eTILFy5k2bJlPPDAA7zxxhsAjBs3ji5durDbbrt9abuKigoee+yxWh2L6vjShpmZNTtjxoxZNU/g+OOPZ8yYMeyxxx5Fv5WQW37SSSfx0UcfsXLlylUJSEQU3LZYeTHt2rVjxowZtd+RWvja177Gz3/+c771rW/Rtm1bdtttN9Zff32WLVvG7373O8aPH19wuy222IK33nqr3v17RCKPpJGS5kmaIWmupAtrsU0fSd8soa8zJZ1SQ53r0g3IkLS0odo1M2uuFi5cyCOPPMIZZ5xBeXk5l112GbfeeisRwWabbcaiRYtWq//BBx/QqVOnVc9Hjx7NvHnzOPHEExk0KLvDwi677ML06dP5/PPPV9X7/PPPmTlzJl/72tfYZZddmDp1ao2x1XVEYsstt+Ttt98G4O2332aLLbYo2O73v/99pk2bxuTJk+nYsSPdunXjlVdeYd68eey2226Ul5ezYMEC9thjD/79738D2e99bLjhhjXGXBOPSBR2XkTcIakMeF7SjRExr5r6fYClwJO17UDS+hFxdU31IuKM2rZZl3bNzJqrO+64g1NOOYVrrrlmVdn+++/P448/Tq9evXjrrbd44YUX+NrXvsZrr73GzJkzV30zo0rr1q255JJL2GGHHVbV3X333bnkkku44IILALjkkkvYY4896Nq1K126dOEPf/gD999/P4cdlk2wffDBB+nSpQs9evRY1W5dRySOOOIIRo0axZAhQxg1ahRHHnlkwXrvvvsuW2yxBa+//jp33XUXTz31FJtuuulql0LKy8uprKxclTS99NJL9Z7DAc00kZB0PnAS8AbwPjA1Ii4voamy9Pej1O58oCIi3pdUAVwODADOBFZK+h5wNnAjsGNEfCppE2AW2d1GJ5AlG/sA4yS1I0tA7ie73Xmv1E85MC4idpU0CTg3IirTuj8BBwCLgOMj4r1U50vtRsTlknoCVwMbAa8Ap0fE6ul41u5AYCBAq002L+FQmZkVtra/uTJmzBiGDBmyWtkxxxzDzTffzL777ss//vEPTjvtNFasWEHr1q257rrrVn2FM9eGG27IT3/6Uy6//HKuv/56rr/+es4++2y6du1KRLD33ntz/fXXr6p73333MXjwYAYPHkzr1q3ZddddueKKK+q1L0OGDOG4447j+uuvZ9ttt+X2228H4K233uKMM87ggQceWLV/CxcupHXr1gwbNqzgV0TzTZw4cVXSUx+KiHo3si5JJ/jrgL3JEqVpwDW1TSQkjQT2BxYDXYG/RsQv07r55CUSEdFH0kWkE3eqNwK4JyLuTifor0bET9MJ//mI+FGqt2o7STOAoyPiVUk/B1pHxCW5iYSkAL4XEaMlXQBsERFn1dDuLODsiHhU0sXAJhExuLpj0KZzt+h86l9qc7jMrAYt8eufVZ/gbd318ccfrxqlWX/91ccUCr1+kqZGREWhtprjHIneZCfx5RGxBLi3hDbOi4iewP8DDixh/sN1wGlp+TRgRM66W4tscxtwXFruX6Te5znl/yDb16LtSmoPdIiIR1PRKGC/moI3M7Pm7fXXX2fo0KFfSiJK0RwTiQb7gfeIWApM4osT9md8cczKCm2TtnsCKJe0P9AqIubkrP6oyGa3AsdJ2jFrIl6uTYi1aNfMzGw13bp1o0+fPg3SVnNMJB4H+koqk9QWKHlcUdL6wDfI5hYAzAeqvih8TE7VJUC7vM1vBMaw+mhEURHxCrASOJ/ioxbrAcem5RPJ9rW6NhcDiyTtm4pOBh6tZhMzswbR3C6btxSlvG7NbrJlREyRNA6YCbwGVJLNd6iLyyT9GtgAeBi4K5X/Brhe0i+BZ3Lq3wvcIelIsvkIjwGjgUvIkonauhW4DCh2I/iPgF0kTSXbp/61aPNU4GpJGwGv8sUll6J6dGlPZQu8rmtmDaOsrIyFCxf6VuJNTESwcOFCysqKDrgX1OwmWwJIahsRS9PJczIwMCLq/rNm9YvhWODIiDh5bfbbECoqKqKysrKxwzCzJurTTz9lwYIFrFixorFDsToqKytj6623pnXr1quVVzfZstmNSCTD0484lZF9rXJtJxFXAocC31mb/ZqZrQtat27N9tsXG1i15qZZJhIRcWLuc0nDyH5jIVc3IH9C4xURUas5DTX0f3Z92zAzM2sKmmUikS8iBjV2DGZmZs1Rc/zWhpmZma0lzXKypdWPpCXAi40dRwvTiezn3G3t8TFf+3zM176GOubbRUTB+ye0iEsbVmcvFpuda2uGpEof87XLx3zt8zFf+9bGMfelDTMzMyuZEwkzMzMrmRMJK2R4YwfQAvmYr30+5mufj/nat8aPuSdbmpmZWck8ImFmZmYlcyJhZmZmJXMiYatIOkTSi5L+JWlIY8fTHEnaRtJESS9Iek7S/6TyiyS9KWlGevg+LQ1I0nxJs9OxrUxlHSVNkPRy+rtpY8fZXEj6as57eYakDyUN9vu8YUm6QdK7kubklBV9X0v6Rfr//UVJ326wODxHwgAktQJeAr4FLACmACdExPONGlgzI6kz0DkipklqB0wF+gHHAUsj4vLGjK+5kjQfqIiI93PK/gh8EBFDU+K8aUT8vLFibK7S/y1vAt8ATsPv8wYjaT9gKXBjRHRPZQXf1+lGlmOAXsBWwEPAjhGxsr5xeETCqvQC/hURr0bEJ8AtwJGNHFOzExFvV92NNiKWAC8AXRo3qhbrSGBUWh5FltBZwzsQeCUiXmvsQJqbiJgMfJBXXOx9fSRwS0R8HBHzgH+R/b9fb04krEoX4I2c5wvwCW6NklQO7A48k4rOkjQrDVd6mL1hBTBe0lRJA1PZlhHxNmQJHrBFo0XXvB1P9km4it/na1ax9/Ua+z/eiYRVUYEyX/daQyS1Be4EBkfEh8DfgR2AnsDbwJ8aL7pmaZ+I2AM4FBiUhoRtDZO0AXAEcHsq8vu88ayx/+OdSFiVBcA2Oc+3Bt5qpFiaNUmtyZKI0RFxF0BEvBMRKyPic+BaGmjI0TIR8Vb6+y4wluz4vpPmrFTNXXm38SJstg4FpkXEO+D3+VpS7H29xv6PdyJhVaYA3SRtnz5FHA+Ma+SYmh1JAq4HXoiIP+eUd86pdhQwJ39bK42kjdPEViRtDBxMdnzHAaemaqcC9zROhM3aCeRc1vD7fK0o9r4eBxwvqY2k7YFuwLMN0aG/tWGrpK9i/QVoBdwQEb9r3IiaH0m9gceA2cDnqfiXZP/h9iQbapwP/LDqOqfVj6SvkI1CQHbH45sj4neSNgNuA7YFXge+GxH5E9esRJI2Irsm/5WIWJzKbsLv8wYjaQzQh+xW4e8AFwJ3U+R9LelXwOnAZ2SXVf+vQeJwImFmZmal8qUNMzMzK5kTCTMzMyuZEwkzMzMrmRMJMzMzK5kTCTMzMyuZEwmzRiBpZbr74RxJ90rqUEP9iySdW0OdfunGPFXPL5Z0UAPEOlLSsfVtp459Dk5fH1xnSNopvWbTJe2Qt26+pMfyymZU3ZVRUoWkvzZADOW5d3rMW3dd7uu/pknaUtLNkl5NPz3+lKSj1lb/tu5wImHWOJZHRM90x74PgEEN0GY/YNWJJCIuiIiHGqDdtSrdLXIwsE4lEmTH956I2D0iXimwvp2kbQAkfS13RURURsQ5te0oHYM6iYgz1tbdetMPq90NTI6Ir0TE18l+xG7rNdzv+muyfSuNEwmzxvcU6eY5knaQ9GD6hPeYpJ3yK0v6gaQpkmZKulPSRpK+SXZPg8vSJ+EdqkYSJB0q6bac7ftIujctH5w+SU6TdHu6B0hR6ZP379M2lZL2kPRPSa9IOjOn/cmSxkp6XtLVktZL606QNDuNxFya0+7SNILyDPArstscT5Q0Ma3/e+rvOUm/yYvnNyn+2VXHS1JbSSNS2SxJx9R2fyX1lPR02m6spE3Tj7UNBs6oiqmA24D+aTn/Fx37SLqvhthyj8Hekn6SjtMcSYNz+llf0qi07R1VIzeSJkmqqMVxvjS9vx6S1Ctt96qkI1KdVpIuS++xWZJ+WGBf/wv4JCKuriqIiNci4srq2kjHYVKKe66k0SkpQdLXJT2aYvunvviZ50npPfco8D+S+kp6RtnI0EOStizyetjaEhF++OHHWn4AS9PfVmQ3NDokPX8Y6JaWvwE8kpYvAs5Ny5vltHMJcHZaHgkcm7NuJHAs2a85vg5snMr/DnyP7NfwJueU/xy4oECsq9ol+zXC/07L/wvMAtoBmwPvpvI+wArgK2n/JqQ4tkpxbJ5iegTol7YJ4LicPucDnXKed8w5XpOAXXPqVe3/j4Dr0vKlwF9ytt+0Dvs7C9g/LV9c1U7ua1Bgm/nAjsCT6fl0stGhOTnH5L5iseUfA+DrZL9+ujHQFniO7E6x5anePqneDXzxvpgEVNTiOB+alscC44HWwG7AjFQ+EPh1Wm4DVALb5+3vOcD/VvP+LthGOg6LyUYu1iNLonunGJ4ENk/b9Cf7dd2q/fpb3mtZ9WOKZwB/aux/zy394WEis8axoaQZZCeGqcCE9On4m8Dt6UMaZP8J5+su6RKgA9lJ5p/VdRQRn0l6EOgr6Q7gMOBnwP5kJ7snUn8bkP3HXpOqe7DMBtpGxBJgiaQV+mKux7MR8Sqs+hnf3sCnwKSIeC+Vjwb2IxsiX0l2I7NijlN2++/1gc4p7llp3V3p71Tg6LR8ENlQe9UxWCTp8Jr2V1J7oENEPJqKRvHFnStr8gGwSNLxwAvAsiL1vhRbWsw9Br2BsRHxUYrrLmBfsmP/RkQ8ker9g+ykfnlO+3tS/Dh/AjyY6s0GPo6ITyXNJnsvQnYvkl31xbyY9mT3ZZhXbMclDUsxfxIRe1bTxidk740FabsZqd//AN3J/h1AljDm/nT2rTnLWwO3phGLDaqLy9YOJxJmjWN5RPRMJ677yOZIjAT+ExE9a9h2JNknzJmSBpB9yqvJramPD4ApEbEkDSlPiIgT6hj7x+nv5znLVc+r/k/J/+39oPBtjKusiIiVhVYou8HQucCeKSEYCZQViGdlTv8qEEOp+1sXtwLDgAHV1CkUG6x+DKo7VoWObX77xXwa6aM8Oa9fRHyuL+YfiGyUp7oE9TngmFUBRAyS1Ils5KFoG5L6sPp7puo1E/BcROxdpL+PcpavBP4cEeNSexdVE6etBZ4jYdaIIruZ0TlkJ8rlwDxJ34VsQpuk3Qps1g54W9ntyE/KKV+S1hUyCdgD+AFffLp7GthHUtfU30aSdqzfHq3SS9mdZNcjG6Z+HHgG2F9SJ2WTCU8AHi2yfe6+bEJ2IlmcrocfWov+xwNnVT2RtCm12N/0eiyStG8qOrmaGAsZC/yR6keJCsWWbzLQL8W4MdmdMqu+FbKtpKoT7glkxzZXXY5zIf8E/ju9v5C0Y4oh1yNAmaT/zinLnRxbmzZyvQhsXrVfklpL2qVI3fbAm2n51CJ1bC1yImHWyCJiOjCTbLj7JOD7kmaSfeo7ssAm55OdLCYAc3PKbwHOU4GvJ6ZPuveRnYTvS2XvkX1yHiNpFtmJ9kuTO0v0FDCU7DbR88iG6d8GfgFMJNvfaRFR7Nbdw4H/kzQxImaSzTl4jmxOwBNFtsl1CbBpmmw4EzigDvt7Ktmk1Vlkd6q8uBb9ARARSyLi0oj4pC6xFWhnGtnI07Nkr/V16X0C2WWTU1N8HcnmvORuW5fjXMh1wPPANGVfNb2GvNHrNKrRjyxhmSfpWbLLQD+vbRt57X1CNo/m0nRMZpBd5ivkIrLLf48B79dhv2wN8d0/zaxBpeHmcyPi8EYOxczWAo9ImJmZWck8ImFmZmYl84iEmZmZlcyJhJmZmZXMiYSZmZmVzImEmZmZlcyJhJmZmZXs/wN/YGDKlqeX3gAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.7194125218236453, pvalue=3.413903967066941e-17)\n", "Slope and P-value = PearsonRResult(statistic=0.6764014829341424, pvalue=1.1346185053575552e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.7946089934622645, pvalue=1.3283261250334273e-16)\n", "Slope and P-value = PearsonRResult(statistic=-0.1781579311226821, pvalue=0.0761629699320616)\n", "Slope and P-value = PearsonRResult(statistic=0.8210396173335963, pvalue=1.3474668350854751e-25)\n", "Slope and P-value = PearsonRResult(statistic=0.930442200210169, pvalue=1.6143138975625928e-44)\n", "Slope and P-value = PearsonRResult(statistic=0.6422384554286992, pvalue=5.281858453191303e-10)\n", "Slope and P-value = PearsonRResult(statistic=-0.16340061352194202, pvalue=0.10429043347823115)\n", "Slope and P-value = PearsonRResult(statistic=-0.6840593801358997, pvalue=4.340081967169219e-15)\n", "Slope and P-value = PearsonRResult(statistic=0.8658236885736204, pvalue=1.9695496033528232e-15)\n", "WS 630\n", "PB 50\n", "SDSP 15\n", "Name: BroodBedding, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.9637047269497834, pvalue=5.179989900207274e-58)\n", "Slope and P-value = PearsonRResult(statistic=0.7986560420708166, pvalue=2.4315113213091327e-23)\n", "Slope and P-value = PearsonRResult(statistic=0.8670221465101176, pvalue=2.076235493553199e-31)\n", "Slope and P-value = PearsonRResult(statistic=-0.19478083439135563, pvalue=0.052141633926607506)\n", "Slope and P-value = PearsonRResult(statistic=0.6699659717337554, pvalue=2.489062248322231e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.6285326401678794, pvalue=2.5387059719910892e-12)\n", "Slope and P-value = PearsonRResult(statistic=0.8849911962560632, pvalue=2.6489045339865122e-34)\n", "Slope and P-value = PearsonRResult(statistic=0.9475059488928916, pvalue=2.500341179190047e-50)\n", "Slope and P-value = PearsonRResult(statistic=0.9343356711479582, pvalue=1.0550978397126505e-45)\n", "Slope and P-value = PearsonRResult(statistic=0.7404911391413467, pvalue=1.313915830308418e-18)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'BroodBedding','SampleType','PastureTime']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType','BroodBedding','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns'),sample.BroodBedding,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " print(pd.value_counts(sample['BroodBedding']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"BroodBedding in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','BroodBedding','SampleType','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " #display(prelim2_plot.nlargest(10))\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"BroodBedding_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (22) AnyABXUse" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 881)\n", "Soil (695, 881) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "WS 633\n", "PB 50\n", "SDSP 15\n", "Name: BroodBedding, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.2035496057061832, pvalue=0.04223244128515208)\n", "Slope and P-value = PearsonRResult(statistic=0.4927466972635967, pvalue=1.9111514646478193e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.9526741659975099, pvalue=1.7640399840654616e-52)\n", "Slope and P-value = PearsonRResult(statistic=-0.4228623489580814, pvalue=1.1711378197071932e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.3722256029328299, pvalue=0.006581557733549163)\n", "Slope and P-value = PearsonRResult(statistic=-0.4143360895764977, pvalue=1.8230642839584854e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.5292033339703812, pvalue=1.509009359465051e-08)\n", "Slope and P-value = PearsonRResult(statistic=-0.2306774294725481, pvalue=0.02094323372901353)\n", "Slope and P-value = PearsonRResult(statistic=-0.5820801454093351, pvalue=2.127574671315231e-10)\n", "Slope and P-value = PearsonRResult(statistic=-0.3536119912930648, pvalue=0.00030738583979482973)\n", "WS 630\n", "PB 50\n", "SDSP 15\n", "Name: BroodBedding, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.6597229573424237, pvalue=8.354776175934325e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.8126257552612046, pvalue=1.0304011173180979e-24)\n", "Slope and P-value = PearsonRResult(statistic=0.6654125424403592, pvalue=4.289080706120362e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.7338508971709463, pvalue=3.790231511675416e-18)\n", "Slope and P-value = PearsonRResult(statistic=0.923483794018637, pvalue=1.4564631790293266e-42)\n", "Slope and P-value = PearsonRResult(statistic=0.9503725694879125, pvalue=1.709976723303542e-51)\n", "Slope and P-value = PearsonRResult(statistic=0.9253141968086617, pvalue=4.649939581825397e-43)\n", "Slope and P-value = PearsonRResult(statistic=0.9006253862083038, pvalue=8.683858261682418e-36)\n", "Slope and P-value = PearsonRResult(statistic=0.7982008798589397, pvalue=7.9024559144478e-15)\n", "Slope and P-value = PearsonRResult(statistic=0.5230058806293223, pvalue=2.373350091957317e-08)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'BroodBedding','SampleType','PastureTime']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType','BroodBedding','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns'),sample.BroodBedding,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " print(pd.value_counts(sample['BroodBedding']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"BroodBedding in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','BroodBedding','SampleType','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " #display(prelim2_plot.nlargest(10))\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"BroodBedding_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (23) AnimalSource" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 881)\n", "Soil (695, 881) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "Broiler 553\n", "Layer 65\n", "Swine 45\n", "Cattle 35\n", "Name: AnimalSource, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.4854776268089492, pvalue=0.00012928865637780102)\n", "Slope and P-value = PearsonRResult(statistic=-0.9567875981809941, pvalue=2.261808965992906e-54)\n", "Slope and P-value = PearsonRResult(statistic=-0.8662943133012504, pvalue=2.663739434901824e-31)\n", "Slope and P-value = PearsonRResult(statistic=-0.8032690919271991, pvalue=8.804915493582486e-24)\n", "Slope and P-value = PearsonRResult(statistic=-0.958254406246357, pvalue=4.3138050431894505e-55)\n", "Slope and P-value = PearsonRResult(statistic=-0.743819891517007, pvalue=9.200556374713612e-15)\n", "Slope and P-value = PearsonRResult(statistic=-0.8920414220991123, pvalue=1.422093601395298e-35)\n", "Slope and P-value = PearsonRResult(statistic=-0.9638566003845527, pvalue=4.233264064358638e-58)\n", "Slope and P-value = PearsonRResult(statistic=-0.19131319802375765, pvalue=0.24989508018606613)\n", "Slope and P-value = PearsonRResult(statistic=-0.8012946867324313, pvalue=1.3644453742966968e-23)\n", "Broiler 550\n", "Layer 65\n", "Swine 45\n", "Cattle 35\n", "Name: AnimalSource, dtype: int64\n" ] }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.8124321271687273, pvalue=1.0784816356430608e-24)\n", "Slope and P-value = PearsonRResult(statistic=-0.8157291242467835, pvalue=4.924882249178171e-25)\n", "Slope and P-value = PearsonRResult(statistic=0.16040771548808663, pvalue=0.11087964067231242)\n", "Slope and P-value = PearsonRResult(statistic=-0.8007733343351267, pvalue=1.5304933272898515e-23)\n", "Slope and P-value = PearsonRResult(statistic=-0.4342502298910395, pvalue=0.008138250380714229)\n", "Slope and P-value = PearsonRResult(statistic=-0.675076857813041, pvalue=1.3359071768287366e-14)\n", "Slope and P-value = PearsonRResult(statistic=-0.39348865747277384, pvalue=0.00011402522796176449)\n", "Slope and P-value = PearsonRResult(statistic=-0.7942601877623217, pvalue=6.2479075128133e-23)\n", "Slope and P-value = PearsonRResult(statistic=0.3043140370532247, pvalue=0.002082301376052594)\n", "Slope and P-value = PearsonRResult(statistic=0.2034328318345872, pvalue=0.04235327793814585)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'AnimalSource','SampleType','PastureTime']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType','AnimalSource','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns'),sample.AnimalSource,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " print(pd.value_counts(sample['AnimalSource']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"AnimalSource in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','AnimalSource','SampleType','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " #display(prelim2_plot.nlargest(10))\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"AnimalSource_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SampleIDFarmAvgNumBirdsAvgNumFlocksYearsFarmingEggSourceBroodBeddingBroodFeedBrGMOFreeBrSoyFree...MnMoNaNiPPbSSiZnEcoli
0E2-1E600414MMWSSSNN...173.7201.290642.0211.5633977.071.8591091.660863.766128.0391
1E2-2E600414MMWSSSNN...128.7631.000477.2421.1562696.481.739822.626534.30092.8691
2E2-3E600414MMWSSSNN...162.2491.278629.5401.3843385.392.3041068.750546.466136.0831
3E2-4E600414MMWSSSNN...155.9332.225602.2152.5752872.7013.079878.725242.178103.2001
4E2-5E600414MMWSSSNN...131.5561.0001473.7501.7302175.495.5221015.680157.706125.8681
\n", "

5 rows × 162 columns

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" ], "text/plain": [ " SampleID Farm AvgNumBirds AvgNumFlocks YearsFarming EggSource \\\n", "0 E2-1 E 600 4 14 MM \n", "1 E2-2 E 600 4 14 MM \n", "2 E2-3 E 600 4 14 MM \n", "3 E2-4 E 600 4 14 MM \n", "4 E2-5 E 600 4 14 MM \n", "\n", " BroodBedding BroodFeed BrGMOFree BrSoyFree ... Mn Mo Na \\\n", "0 WS SS N N ... 173.720 1.290 642.021 \n", "1 WS SS N N ... 128.763 1.000 477.242 \n", "2 WS SS N N ... 162.249 1.278 629.540 \n", "3 WS SS N N ... 155.933 2.225 602.215 \n", "4 WS SS N N ... 131.556 1.000 1473.750 \n", "\n", " Ni P Pb S Si Zn Ecoli \n", "0 1.563 3977.07 1.859 1091.660 863.766 128.039 1 \n", "1 1.156 2696.48 1.739 822.626 534.300 92.869 1 \n", "2 1.384 3385.39 2.304 1068.750 546.466 136.083 1 \n", "3 2.575 2872.70 13.079 878.725 242.178 103.200 1 \n", "4 1.730 2175.49 5.522 1015.680 157.706 125.868 1 \n", "\n", "[5 rows x 162 columns]" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "poultry[['pH', 'EC', 'Moisture', 'TotalC', 'TotalN', 'CNRatio', 'Al', 'B', 'Ca',\n", " 'Cd', 'Cr', 'Cu', 'Fe', 'K', 'Mg', 'Mn', 'Mo', 'Na', 'Ni', 'P', 'Pb',\n", " 'S', 'Si', 'Zn']] = poultry[['pH', 'EC', 'Moisture', 'TotalC', 'TotalN', 'CNRatio', 'Al', 'B', 'Ca',\n", " 'Cd', 'Cr', 'Cu', 'Fe', 'K', 'Mg', 'Mn', 'Mo', 'Na', 'Ni', 'P', 'Pb',\n", " 'S', 'Si', 'Zn']].apply(pd.to_numeric, errors='coerce', axis=1)\n", "poultry = poultry[poultry.SampleType.str.contains('Feces|Soil')]\n", "poultry.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Physicochemical Properties as Targets:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (24) pH" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 6.55\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 819\n", "0.0 816\n", "Name: new_pH, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 379\n", "0.0 319\n", "Name: new_pH, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.49316740833241207, pvalue=1.8590487994371666e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.9811703005768369, pvalue=8.50748605028504e-72)\n", "Slope and P-value = PearsonRResult(statistic=0.6719821289364183, pvalue=1.9501252492626152e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.23704049619561135, pvalue=0.017570317269634348)\n", "Slope and P-value = PearsonRResult(statistic=0.6940375416796271, pvalue=1.1864444379729647e-15)\n", "Slope and P-value = PearsonRResult(statistic=0.38661061530649854, pvalue=7.09304931066444e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.618610789444673, pvalue=6.95158040077386e-12)\n", "Slope and P-value = PearsonRResult(statistic=0.8032404293994245, pvalue=8.861396672420476e-24)\n", "Slope and P-value = PearsonRResult(statistic=0.9538840691119174, pvalue=5.105441696779946e-53)\n", "Slope and P-value = PearsonRResult(statistic=0.8213626330907897, pvalue=1.2436026267061578e-25)\n", "0.0 357\n", "1.0 338\n", "Name: new_pH, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.9108127498999394, pvalue=1.947271947734906e-39)\n", "Slope and P-value = PearsonRResult(statistic=-0.7030042149187083, pvalue=3.533922685332825e-16)\n", "Slope and P-value = PearsonRResult(statistic=-0.7700881593903877, pvalue=7.656006829894263e-21)\n", "Slope and P-value = PearsonRResult(statistic=-0.9309921916562955, pvalue=1.1088346144399905e-44)\n", "Slope and P-value = PearsonRResult(statistic=-0.8881767033177611, pvalue=7.239961549120161e-35)\n", "Slope and P-value = PearsonRResult(statistic=0.9406532587895231, pvalue=8.652116779948058e-48)\n", "Slope and P-value = PearsonRResult(statistic=0.8242205659656774, pvalue=6.07258951298498e-26)\n", "Slope and P-value = PearsonRResult(statistic=0.4998582824078726, pvalue=1.1917842727419939e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.9386623138591296, pvalue=4.1531139362484084e-47)\n", "Slope and P-value = PearsonRResult(statistic=0.6865689690851359, pvalue=3.147386670793048e-15)\n" ] } ], "source": [ "print(\"Median:\",poultry.pH.median())\n", "\n", "poultry.loc[poultry['pH'] < poultry.pH.median(), 'new_pH'] = 0\n", "poultry.loc[poultry['pH'] >= poultry.pH.median(), 'new_pH'] = 1 \n", "print(\"Total sample count:\", poultry.pH.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_pH.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_pH','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns')\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_pH'],axis='columns'),sample.new_pH,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"pH_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_pH']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"pH in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_pH','SampleType'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " \n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"pH_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (25) EC" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 879.0\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 818\n", "0.0 817\n", "Name: new_EC, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 671\n", "0.0 27\n", "Name: new_EC, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=-0.6099442459763682, pvalue=0.04630470364480025)\n", "Slope and P-value = PearsonRResult(statistic=-0.9248865440778429, pvalue=1.1418725216913e-19)\n", "0.0 667\n", "1.0 28\n", "Name: new_EC, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.8187442411903334, pvalue=2.371750506852926e-25)\n", "Slope and P-value = PearsonRResult(statistic=0.688464764368405, pvalue=4.843160466254269e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.20395697399799176, pvalue=0.041813145983758085)\n", "Slope and P-value = PearsonRResult(statistic=0.8644749400090499, pvalue=1.0865688286906399e-11)\n", "Slope and P-value = PearsonRResult(statistic=0.35098248611341415, pvalue=0.0003431944816848938)\n", "Slope and P-value = PearsonRResult(statistic=0.8066427183728184, pvalue=0.0008627979115142092)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/mosesayoola/.local/lib/python3.10/site-packages/scipy/stats/_stats_py.py:4424: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n", " warnings.warn(stats.ConstantInputWarning(msg))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "Slope and P-value = PearsonRResult(statistic=0.5976953977851474, pvalue=5.1956825666107974e-11)\n", "Slope and P-value = PearsonRResult(statistic=0.8677497256168993, pvalue=1.6160661364898033e-31)\n", "Slope and P-value = PearsonRResult(statistic=-0.2954805084486747, pvalue=0.002839502734614327)\n" ] } ], "source": [ "print(\"Median:\",poultry.EC.median())\n", "\n", "poultry.loc[poultry['EC'] < poultry.EC.median(), 'new_EC'] = 0\n", "poultry.loc[poultry['EC'] >= poultry.EC.median(), 'new_EC'] = 1\n", "print(\"Total sample count:\", poultry.EC.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_EC.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_EC','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_EC'],axis='columns'),sample.new_EC,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"EC_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_EC']))\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"EC in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_EC','SampleType'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"EC_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0 818\n", "0.0 817\n", "Name: new_EC, dtype: int64" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "poultry.new_EC.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (27) Moisture" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 0.3699999999999999\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 820\n", "0.0 815\n", "Name: new_Moisture, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 684\n", "0.0 14\n", "Name: new_Moisture, dtype: int64\n" ] }, { "data": { "image/png": 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vY0mda1oXEZURcWaTIzQzM2tm7T5BkDRU0ouSHpQ0Jj3CuSHGAMek5QHA48Cnqe/fSfpxblu/l3SmpAGSJki6EaiSVCbpeklVkp6VtF+qP0DSuLS8oaTxaf1VgHL9flfSM5KmS7pKUuf0M1LSrNTvTxp7jMzMzAq16wRBUgVwJNAPOAIo+czrOrwMbCRpfeA44KbcumuBE9K2OgHHAqPTut2AcyNiB+A0gIjYMfUxSlJZwXZ+A0yOiH7AWGCL1O/2ZAnKXhHRF1gOHA/0Bb4QEX1SvyWfcClpsKRKSZXLP1zYiN03M7OOqF0nCMDewN0R8VFELALuaWQ/d5Cd/HcHHqsujIi5wLuS+gEHAc9GxLtp9TMRMScXxz9SmxeBecC2BdvYB7gh1bkXeD+V7w/0B6ZImp5ebw28Cmwt6VJJBwMflAo8IkZEREVEVHTu2qNxe29mZh1Oe78PguquUi83AdOAURGxQvpct9cAJwL/BVyXK1/SiDiiRJnSdn9ZtELaGfg62QjF0cDJ9dyOmZlZrdr7CMJkYGCaA9ANaNRdRSLiNeBc4IoSq+8EDgZ2BR6ooYtJZJcFkLQt2eWDl2qpcwiwfip/GDhK0sZp3QaStkzfcOgUEbcDQ4FdGrNvZmZmpbTrEYSImCJpLDCDbFi/EmjUhfiIuKqG8k8kTQD+ExHLa2h+BXClpCqyCY4nRsTHBSMRvwXGSJoGPAq8lvp/XtJ5wPg0z2EZ2YjBR8D1qQygaITBzMyssRRRalS7/ZDULSIWS+pK9il9cERMa8b+O5FdfvhORLzcXP22hIqKiqisrGztMMzMrI2QNDUiSk7gb++XGABGpMl904Dbmzk52IHsPgkPt/XkwMzMrCHa9SUGgIgYlH8t6XJgr4Jqvci+zph3cUSU/Opgru/nyb5RYGZm1q60+wShUESc1toxmJmZtXUd4RKDmZmZNZATBDMzMyviBMHMzMyKOEEwMzOzIk4QzMzMrIgTBDMzMyviBMHMzMyKdLj7IHRkVfMXUj7k3tYOw8zqMHd4o54rZ9asPIJgZmZmRZwgmJmZWZF2lSBIGilpjqTpkqZJ2qOZ+/+RpO+n5YmSSj4Bq4F9XpMe+oSkuZJ6puXFTe3bzMyssdrjHIRzIuI2SQcBVwE7NVfHEXFlc/WV6/OU5u7TzMysqdrcCIKkoZJelPSgpDGSzm5kV5OAbSR1k/RwGlGoknRoblvflfRMGnG4SlLnVL5Y0u8lzZD0lKRNUvmwgni+K+kJSbMk7Zbq7JbKnk2/v5zKO0u6MMUwU9IZqbzWkQhJAySNy72+TNKJaXm4pOdTfxfW0H6wpEpJlcs/XNjIQ2lmZh1Nm0oQ0onySKAfcATQlCH8gUAVsBQ4PCJ2AfYD/qzM9sAxwF4R0RdYDhyf2q4DPBURO5MlGj+sYRvrRMSewP8DrktlLwL7REQ/4NfAH1L5YGAroF9E7ASMbsK+IWkD4HCgd+rv/FL1ImJERFREREXnrj2askkzM+tA2tolhr2BuyPiIwBJ9zSijz9JOg94B/gBIOAPkvYBVgBfADYB9gf6A1MkAawNvJ36+ASo/tQ+FTiwhm2NAYiISZLWlbQe0B0YJakXEECXVPcA4MqI+DS1ea8R+5b3AVnyc42ke3PxmpmZNVlbSxDUDH2cExG3rewwG47fCOgfEcskzQXK0rZGRcQvS/SxLCIiLS+n5uMUJV7/DpgQEYdLKgcmVodSon59fMrnR3rKACLi03RZY3/gWOB04GuN6N/MzKxIm7rEAEwGBkoqk9QNaI67hfQA3k7JwX7Alqn8YeAoSRtDNmQvacuaOqnBMant3sDCiFiYtjc/rT8xV3c88CNJa1Rvr57bmAfsIGktST3IEgLS8ekREfcBZwF9Gxi7mZlZjdrUCEJETJE0FphBdmKsBJo6s240cI+kSmA62RwBIuL5dClivKROwDLgtLTd+npf0hPAusDJqeyPZJcYfgo8kqt7DbAtMFPSMuBq4LK6NhARr0u6BZgJvAw8m1Z1B+6WVD0a8pMGxG1mZlYrfTaS3jZI6hYRiyV1JZsgODgiprV2XO1BRUVFVFZWtnYYZmbWRkiaGhElvxDQpkYQkhHpxkFlZHMEnByYmZmtYm0uQYiIQfnXki4H9iqo1otsuD3v4oi4viVjMzMz6yjaXIJQKCJOa+0YzMzMOpq29i0GMzMzawOcIJiZmVkRJwhmZmZWxAmCmZmZFXGCYGZmZkWcIJiZmVkRJwhmZmZWpM3fB8GaT9X8hZQPube1wzDrUOYOb45nzpmteh5BMDMzsyJOEMzMzKxIm08QlDlP0suSZkuaIKl3G4hrrqSezdDPfZLWS8uL0+9ySbOa2reZmVljrQ5zEE4D9gR2jogPJR0EjJXUOyKWtnJsTRYR32jtGMzMzAqtkhEESUMlvSjpQUljJJ3dgOa/AM6IiA8BImI88ARwfOr7YEnTJM2Q9HAqW0fSdZKmSHpW0qGpvFzSY6n+NEl7pvIBkiZKui3FOVqS0rr9Ux9Vqc+1crGdI+mZ9LNNqj9Q0tOpzUOSNknl3SRdn/qZKenIVF7rSISkEyVdlns9LsXbWdJISbNSnz+pof1gSZWSKpd/uLABh93MzDqyFh9BkFQBHAn0S9ubBkytZ9t1gXUi4pWCVZVAb0kbAVcD+0TEHEkbpPXnAo9ExMlp+P4ZSQ8BbwMHRsRSSb2AMUBFatMP6A28CTwO7CWpEhgJ7B8RsyX9HTgVuCi1+SAidpP0/VT2LWAy8JWICEmnAD8HfgYMBRZGxI5p39avzzGoRV/gCxHRJ/W3XqlKETECGAGw1qa9oonbNDOzDmJVXGLYG7g7Ij4CkHRPM/QpIICvAJMiYg5ARLyX1h8EfDs3UlEGbEF28r9MUl9gObBtrs9nIuKNFON0oBxYBMyJiNmpziiySx4Xpddjcr//mpY3B26WtCmwJjAnlR8AHFu9sYh4v1F7/plXga0lXQrcC4xvYn9mZmYrrYpLDGpsw4j4AFgiaeuCVbsAz/NZolBqm0dGRN/0s0VEvAD8BHgL2Jls5GDNXJuPc8vLyZKnumKPEsuXApelkYL/JktOqmNqzCf4T/n8+1QGKxOMnYGJZEnLNY3o28zMrKRVkSBMBgZKKpPUDWjoXUP+BFwiaW0ASQeQjUrcCDwJ7Ctpq7Su+hLDA8AZuXkE/VJ5D2BBRKwAvgd0rmPbLwLl1fMLUptHc+uPyf1+MreN+Wn5hFzd8cDp1S8acIlhLtBXUidJXwR2S+17Ap0i4nayyxe71LM/MzOzOrX4JYaImCJpLDADmEc2f6Ahs+UuBdYHqiQtB/4FHJouWXwkaTBwh6ROpDkGwO/ILgPMTEnCXLL5AVcAt0v6DjABWFJH7EslnQTcKmkNYApwZa7KWpKeJku0jktlw1L9+cBTwFap/Hzg8vT1xeXAb4E76rH/j5NdpqgCZpHN4QD4AnB92m+AX9ajLzMzs3pRRMvPW5PULSIWS+oKTAIGR8S0utpZ86qoqIjKysrWDsPMzNoISVMjoqLUulV1H4QRknYgu34+ysmBmZlZ27ZKEoSIGJR/LelyYK+Car2AlwvKLo6I61syNjMzMyvWKndSjIjTWmO7ZmZmVj9t/lkMZmZmtuo5QTAzM7MiThDMzMysiBMEMzMzK+IEwczMzIo4QTAzM7MiThDMzMysSKvcB8FaR9X8hZQPube1wzBrt+YOb+iz6MzaLo8gmJmZWREnCGZmZlak1RMESWMlfS/3+mpJ59TRZq6kno3c3o8kfb+BbZ5ozLZK9FMy7sbEZGZm1pLawhyEM4EJku4BdgB2B/5fS20sIq5sRJs961tX0hoR8WlLx2RmZtaSmmUEQdJQSS9KelDSGEln17dtRMwFRgB/BK4ATo+IZZL2l3RnbhsHSrqjxLbvkjRV0nOSBufKF0v6vaQZkp6StEkqH1Ydn6SJkv4qaZKkFyTtKukOSS9LOj/fV27555KqUr/Dc/38QdKjwI9T7M+metdJWisX8jmSnkk/29QQU0Va7ilpblo+Me3rPZLmSDpd0k/Tdp6StEEN781gSZWSKpd/uLC+b4uZmXVwTU4Q0snsSKAfcARQ0YhuLgQOBp6LiEmp7BFge0kbpdcnAaUe/XxyRPRP2z1T0oapfB3gqYjYGZgE/LCGbX8SEfsAVwJ3A6cBfYATc30BIOkQ4DBg99TvH3Or14uIfYHLgZHAMRGxI9kozam5eh9ExG7AZcBFNcRUkz7AIGA34PfAhxHRD3gSKHmJIiJGRERFRFR07tqjgZszM7OOqjlGEPYG7o6IjyJiEXBPI/rYCRCwnaROABERwD+A70paD9gD+L8Sbc+UNAN4Cvgi0CuVfwKMS8tTgfIatj02/a4iS1AWRMTHwKupv7wDgOsj4sMU43u5dTen318G5kTE7PR6FLBPrt6Y3O89aoipJhMiYlFEvAMs5LNjXUXN+2dmZtZgzZEgqEmNs4TgCuB7wMt8/tP29cB3geOAWwuv7UsaQHbS3iN9on8WKEurl6UkA2A5Nc+3+Dj9XpFbrn5d2EZAUNqSXJ3aRA3L1T7ls/elrGBdYXz52NvCfBIzM2snmiNBmAwMlFQmqRvQ0DuF/DfwckRMBH4K/Lz6skJEvAm8CZxHNmxfqAfwfkR8KGk74CuN24V6Gw+cLKkrQA3X/V8EyqvnF5AlPo/m1h+T+/1kifZzgf5p+aimBmxmZtYYTf7UGRFTJI0FZgDzgEqy4e86SdoY+AXpxB4Rb0q6mOza/kmp2mhgo4h4vkQX9wM/kjQTeInsMkOLiYj7JfUFKiV9AtwH/KqgzlJJJwG3SloDmEI2v6HaWpKeJkvOjiuxmQuBW9JXPx9pzvh3/EIPKn2nNzMzqwd9NgrfhE6kbhGxOH2yngQMjohpTe446/sy4NmIuLY5+uvIKioqorKysrXDMDOzNkLS1Igo+eWC5rpuPULSDmTXzEc1Y3Iwleza/s+aoz8zMzOrn2ZJECJiUP61pMuBvQqq9SKbhJh3cUSU+upidb/9a1pnZmZmLadFZr5HxGkt0a+ZmZmtGq3+LAYzMzNre5wgmJmZWREnCGZmZlbECYKZmZkVcYJgZmZmRZwgmJmZWRE/4KcDqZq/kPIh97Z2GGbtzlzfwtzaIY8gmJmZWREnCGZmZlakwyQIkkZKmiNpuqRpkvZopn6fqGV7zfq4ZknXpGdemJmZtaiONgfhnIi4TdJBwFXATvmVkjpHxPKGdBgRezZngHVs65RVtS0zM+vYVqsRBElDJb0o6UFJYySd3ciuJgHbpD7nSvq1pMnAdyQdJ6lK0ixJF6Q6p0r6Yy6OEyVdmpYXp9+SdJmk5yXdC2ycq99f0qOSpkp6QNKmqXyipAskPSNptqSvpvLOki5MccyUdEaufkVa/pukSknPSfptI4+DmZlZSavNCEI6MR4J9COLexowtZHdDQSqcq+XRsTekjYDngL6A+8D4yUdBtwGPAn8PNU/Bvh9QZ+HA18GdgQ2AZ4HrpPUBbgUODQi3pFU3fbk1G6NiNhN0jeA3wAHAIOBrYB+EfGppA1K7MO5EfGepM7Aw5J2ioiZhZUkDU790Xndjep5eMzMrKNbbRIEYG/g7oj4CEDSPY3o40+SzgPeAX6QK785/d4VmBgR76RtjAb2iYi7JL0q6Stkj6z+MvB4Qd/7AGPSJYo3JT2Syr8M9AEelATQGViQa3dH+j0VKE/LBwBXRsSnABHxXol9OTqd/NcANgV2AIoShIgYAYwAWGvTXlHqoJiZmRVanRIENUMf50TEbSXKl9RjGzcDRwMvAndGRKmTbakyAc9FRE2TIj9Ov5fz2fuhGvrKVkpbAWcDu0bE+5JGAmW1xG5mZtYgq9MchMnAQEllkroBLXFnkqeBfSX1TEP3xwGPpnV3AIelsptLtJ0EHJvmD2wK7JfKXwI2qv7WhKQuknrXEcd44EeS1khtCi8xrEuW1CyUtAlwSAP20czMrE6rzQhCREyRNBaYAcwDKoGFzbyNBZJ+CUwg+xR/X0Tcnda9L+l5YIeIeKZE8zuBr5HNbZhNSiwi4pP0dcdLJPUgO+YXAc/VEso1wLbATEnLgKuBy3JxzpD0bOrjVYovd5iZmTWJSo+Ut02SukXEYkldyT6xD46Iaa0d1+qioqIiKisrWzsMMzNrIyRNjYiKUutWmxGEZES6UVAZMMrJgZmZWctYrRKEiBiUfy3pcmCvgmq9yL5pkHdxRFzfkrGZmZm1J6tVglAoIk5r7RjMzMzao9XpWwxmZma2ijhBMDMzsyJOEMzMzKyIEwQzMzMr4gTBzMzMijhBMDMzsyJOEMzMzKzIan0fBGuYqvkLKR9yb2uHYbbKzB3eEs90M+sYPIJgZmZmRZwgmJmZWZF2nyBI+qmkFyVVSZoh6S+SutTRZqKkirR8n6T1StQZJunstPw/kg5Iy3Ml9WyGuH/V1D7MzMwaq10nCJJ+BBwEfCUidgR2Bd4G1q5vHxHxjYj4Tx11fh0RDzUl1hIanCBI6tzMMZiZWQfV5hMESUPTCMCDksZUf2qvp3OBU6tP8BHxSUQMj4gPUt8HSXpS0jRJt0rqVmL7K0cEJJ0r6SVJDwFfztUZKemoXLNzJD2TfrZJdQZKelrSs5IekrRJKu8m6fo0wjFT0pGShgNrS5ouaXSq993U33RJV1UnA5IWpxGMp4E9SsQ/WFKlpMrlHy5swKEzM7OOrE0nCGmY/0igH3AEUNGAtt2BbhExp4b1PYHzgAMiYhegEvhpLf31B47NxbJrLZv/ICJ2Ay4DLkplk8lGMvoBNwE/T+VDgYURsWNE7AQ8EhFDgI8iom9EHC9pe+AYYK+I6AssB45P7dcBZkXE7hExuTCQiBgRERURUdG5a49aQjYzM/tMW/+a497A3RHxEYCkexrQVkCsfCF9HbgAWA8YBGwA7AA8LglgTeDJWvr7KnBnRHyY+htbS90xud9/TcubAzdL2jRtqzpxOYAs8QAgIt4v0d/+QH9gSop1bbJLJZAlC7fXEouZmVmDtfUEQY1tGBEfSFoiaauImBMRDwAPSBpHdoIW8GBEHNeQbhtRr3r5UuAvETFW0gBgWCoXdfcrYFRE/LLEuqURsbyecZmZmdVLm77EQDYsP1BSWZof0NC7nvwv8LfqbyEo+/hdltY9BeyVmyPQVdK2tfQ1CThc0trp8sXAWuoek/tdPSrRA5iflk/I1R0PnF79QtL6aXFZ7tsWDwNHSdo41dlA0pa1bN/MzKxJ2vQIQkRMSUP5M4B5ZPMEGjLT7m9AV+BpSR8Di4HHgWcjYqGkE4ExktZK9c8DZtcQyzRJNwPTUyyP1bLdtdKkwU5A9QjFMOBWSfPJkpOtUvn5wOWSZpFdLvgtcAcwApgpaVqah3AeMF5SJ2AZcFqKw8zMrNkpor6j5q1DUreIWCypK9mn+MERMa2141odVVRURGVlZWuHYWZmbYSkqRFR8gsAbXoEIRkhaQeySwOjnByYmZm1vDafIETEoPxrSZcDexVU6wW8XFB2cURc35KxmZmZtVdtPkEoFBGntXYMZmZm7V1b/xaDmZmZtQInCGZmZlbECYKZmZkVcYJgZmZmRZwgmJmZWREnCGZmZlbECYKZmZkVWe3ug2CNVzV/IeVD7m3tMMxWibnDG/psNzPL8wiCmZmZFXGCYGZmZkU6RIKgzHmSXpY0W9IESb1rqHuipMsa2P+3JQ1JyyMlHdUMMZ8oabOm9mNmZtYYHWUOwmnAnsDOEfGhpIOAsZJ6R8TSpnQsaY2IGAuMbY5Ac04EZgFvNjCWT5s5DjMz64BWmxEESUMlvSjpQUljJJ3dgOa/AM6IiA8BImI88ARwfOr7pDSy8Ci5J0VK2lLSw5Jmpt9bpPKRkv4iaQJwQYlRhwMkPZb6/FZqU57KpqWfPXPb+bmkKkkzJA1PIxAVwGhJ0yWtLam/pEclTZX0gKRNU9uJkv6QYv9xieM2WFKlpMrlHy5swCEzM7OObLUYQZBUARwJ9COLeRowtZ5t1wXWiYhXClZVAr3Tifa3QH9gITABeDbVuQz4e0SMknQycAlwWFq3LXBARCyXdGJB3+XAvsCXgAmStgHeBg6MiKWSegFjgApJh6Q+d0+jGxtExHuSTgfOjohKSV2AS4FDI+IdSccAvwdOTttbLyL2LbX/ETECGAGw1qa9oj7HzMzMbLVIEIC9gbsj4iMASfc0Q58CAtgdmBgR76S+byY7+QPsARyRlv8B/DHX/taIWF5D37dExArgZUmvAtsBc4DLJPUFlue2cQBwfW50470S/X0Z6AM8KAmgM7Agt/7m+uywmZlZfa0uCYIa2zAiPpC0RNLWEfFqbtUuwKPV1erbXW55ST3rVb/+CfAWsDPZpZ3quQ8qUb+QgOciYo8a1tcWi5mZWYOtLnMQJgMDJZVJ6gY09A4ofwIukbQ2gKQDyEYlbgSeBgZI2jAN5X8n1+4J4Ni0fHyKoz6+I6mTpC8BWwMvAT2ABWlk4XtkowAA44GTJXVNsW2QyhcB3dPyS8BGkvZIdbrU9C0MMzOz5rBajCBExBRJY4EZwDyy+QMNmXF3KbA+UCVpOfAvsuv5HwEfSRoGPEk2bD+Nz07eZwLXSToHeAc4qZ7be4lsdGIT4Edp3sEVwO2SvkM2z2FJ2rf702WHSkmfAPcBvwJGAldK+ojsUsdRZElOD7L37SLguQYcAzMzs3pTxOoxb01St4hYnD5pTwIGR8S01o5rdVJRURGVlZWtHYaZmbURkqZGREWpdavFCEIyQtIOQBkwysmBmZlZy1ltEoSIGJR/LelycvcsSHoBLxeUXRwR17dkbGZmZu3NapMgFIqI01o7BjMzs/ZqtU0QzMxs1Vq2bBlvvPEGS5c26Q711grKysrYfPPN6dKlS73bOEEwM7N6eeONN+jevTvl5eWkm7bZaiAiePfdd3njjTfYaqut6t1udbkPgpmZtbKlS5ey4YYbOjlYzUhiww03bPDIjxMEMzOrNycHq6fGvG9OEMzMzKyI5yCYmVmjlA+5t1n7mzu8fnfRv/POOzniiCN44YUX2G677QCYOHEiF154IePGjVtZ78QTT+Rb3/oWRx11FAMGDGDBggWUlZWx5pprcvXVV9O3b18AFi5cyBlnnMHjjz8OwF577cWll15Kjx49AJg9ezZnnXUWs2fPpkuXLuy4445ceumlbLLJJo3e1/fee49jjjmGuXPnUl5ezi233ML6669fVK+8vJzu3bvTuXNn1lhjDapvdjds2DCuvvpqNtpoIwD+8Ic/8I1vfIOqqir+/Oc/M3LkyEbHVs0JQgdSNX9hs/+DNmuK+p4QzPLGjBnD3nvvzU033cSwYcPq3W706NFUVFRw/fXXc8455/Dggw8C8IMf/IA+ffrw97//HYDf/OY3nHLKKdx6660sXbqUb37zm/zlL39h4MCBAEyYMIF33nmnSQnC8OHD2X///RkyZAjDhw9n+PDhXHDBBSXrTpgwgZ49exaV/+QnP+Hss8/+XNmOO+7IG2+8wWuvvcYWW2zR6PjAlxjMzGw1snjxYh5//HGuvfZabrrppkb1scceezB//nwA/vnPfzJ16lSGDh26cv2vf/1rKisreeWVV7jxxhvZY489ViYHAPvttx99+vRp0n7cfffdnHDCCQCccMIJ3HXXXU3qL2/gwIGNPjZ5ThDMzGy1cdddd3HwwQez7bbbssEGGzBtWsPvun///fdz2GGHAfD888/Tt29fOnfuvHJ9586d6du3L8899xyzZs2if//+dfa5aNEi+vbtW/Ln+eefL6r/1ltvsemmmwKw6aab8vbbb5fsVxIHHXQQ/fv3Z8SIEZ9bd9lll7HTTjtx8skn8/77768sr6io4LHHHqsz5rr4EoOZma02xowZw1lnnQXAsccey5gxY9hll11qnKWfLz/++ONZsmQJy5cvX5lYRETJtjWV16R79+5Mnz69/jtST48//jibbbYZb7/9NgceeCDbbbcd++yzD6eeeipDhw5FEkOHDuVnP/sZ1113HQAbb7wxb775ZpO3vUpGECT9VNKLkqokzZD0F0n1v51Tzf0OkDSu7prNR9Jh6aFRjW0/TNLZNax7Iv0ulzSrsdswM2uP3n33XR555BFOOeUUysvL+dOf/sTNN99MRLDhhht+7lM0ZBMB89fuR48ezZw5cxg0aBCnnZbdrb937948++yzrFixYmW9FStWMGPGDLbffnt69+7N1KlT64ytoSMIm2yyCQsWLABgwYIFbLzxxiX73WyzzYDspH/44YfzzDPPrGzfuXNnOnXqxA9/+MOV5ZDdr2LttdeuM+a6tHiCIOlHwEHAVyJiR2BX4G2g6dE3PJbmGDE5DGh0glCbiNizJfo1M2sPbrvtNr7//e8zb9485s6dy+uvv85WW23F5MmT6dWrF2+++SYvvPACAPPmzWPGjBkrv6lQrUuXLpx//vk89dRTvPDCC2yzzTb069eP888/f2Wd888/n1122YVtttmGQYMG8cQTT3DvvZ9N8L7//vupqqr6XL/VIwilfnbYofiU8e1vf5tRo0YBMGrUKA499NCiOkuWLGHRokUrl8ePH79y7kN1cgHZtzrycyJmz57d5DkSUM9LDJKGAscDrwP/BqZGxIX13Ma5wD4R8R+AiPgEGJ7r+yDgt8BawCvASRGxWNL+wIUpxinAqRHxsaSDgYtSHNNy/awDXArsmNoMi4i7JZ0IfJPsMdHrSPo72Um+M9AH+DOwJvA94GPgGxHxnqQfAoPTun+m9X2BbwP7SjoPOBLoDlwJdE3xnxwR70uaCEwHdgPWTeXVKd4Oaf0WwEURcUnah8UR0a3g2J8IVETE6en1uHRcHgOuBSqAAK6LiL8WHnxJg9N+0HndjYreHDOzxlrV30IZM2YMQ4YM+VzZkUceyY033shXv/pVbrjhBk466SSWLl1Kly5duOaaa1Z+VTFv7bXX5mc/+xkXXngh1157Lddeey1nnHEG22yzDRHBHnvswbXXXruy7rhx4zjrrLM466yz6NKlCzvttBMXX3xxk/ZlyJAhHH300Vx77bVsscUW3HrrrQC8+eabnHLKKdx333289dZbHH744QB8+umnDBo0iIMPPhiAn//850yfPh1JlJeXc9VVV63se8KECXzzm01/bxQRtVeQKoBrgD3ITrzTgKvqkyBI6g68FhHFX+7M1vcE7gAOiYglkn5Blij8keyxzftHxOx0Up9GdiJ+Gfga2Un7ZqBrRHxL0h+A5yPiBknrAc8A/YDvAOcDO6UT/4nAeWldWernFxFxpaS/AvMi4iJJG0bEuynO84G3IuJSSSOBcRFxW1o3EzgjIh6V9D/AuhFxVkoAXo6IH0raB7giIvpIGkY2orIfWXLxEvBfEbGsOkGQVJ620aeWBGERMDwiDkzl61UnYTVZa9NesekJF9VWxWyV8tccVy8vvPAC22+/fWuHYbX4+OOP2XfffZk8eTJrrPH5MYBS75+kqRFRUaqv+lxi2Bu4OyI+iohFwD0NiFVkn26rA/m6pOmS5kraE/gK2XD945KmAycAWwJfBuZExOzUdBSwD7BdKn85sszmhty2DgKGpH4mkp38q78E+mBEvJerOyEiFkXEO8DC3D5VAeVpuY+kxyRVkY2e9C7aOakHsF5EPFoQZ7UxABExCVg3JS4A90bExxHxb7LLLY35Mu2rwNaSLk2jKh80og8zM2tHXnvtNYYPH16UHDRGfXpo9I23I+IDSUskbRURcyLiAeCB9Cl4zdT3gxFx3Oc2KPWtrdta4jwyIl4q6Gt3YElB3Y9zyytyr1fw2TEZCRwWETPSp/gBtcRU31irX+e3v5za34dP+XwiVwaQLmPsDHwdOA04Gji5ETGamVk70atXL3r16tUsfdVnBGEyMFBSmaRuZNfzG+J/gb9Vf3pW9r2RsrTuKWAvSdukdV0lbQu8CJRXl5Nd/380lW8l6UupPJ9YPACckfpHUr8GxlmoO7Agfdvi+Fz5orSOiFgIvC/pqwVxVjsmxbI3sDDVb6i5QF9JnSR9kWxOQ/XlmU4RcTswFNilEX2bmTVIXZelrW1qzPtW5whCREyRNBaYAcwDKsmG5evrb2QT+J6W9DGwGHgceDYiFqZP52MkrZXqn5fmHZwE3Jq+eTAFuDJNUhwM3Cvp32TJS/VUzd+RTV6cmZKEucC3GhBnoaHA02T7XEVKCoCbgKslnQkcRXZZ5EpJXcmG/U/K9fF++uriujT+0/3jwJwUwyw+m5j5BeB6SdVJ3i/r6mjHL/Sg0td8zayRysrKePfdd/3I59VMRPDuu+9SVlZWd+WcOicpAkjqlr5Z0BWYBAyOiIbfvqoDSZMUz46IytaOpVpFRUVUP+jDzKyhli1bxhtvvMHSpUtbOxRroLKyMjbffHO6dPn8LYhqm6RY31kMI9LNgcqAUU4OzMw6ni5durDVVlu1dhi2itQrQYiIQfnXki4H9iqo1ovsK4h5F0fE9Y0Pb/UVEQNaOwYzM7PGatT3ICLitOYOxMzMzNoOP83RzMzMitRrkqK1D5IWkd250VadnmS3BbdVx8d81fMxX/Wa65hvGREl78Pvxz13LC/VNFvVWoakSh/zVcvHfNXzMV/1VsUx9yUGMzMzK+IEwczMzIo4QehYRrR2AB2Qj/mq52O+6vmYr3otfsw9SdHMzMyKeATBzMzMijhBMDMzsyJOEDoASQdLeknSPyUNae142iNJX5Q0QdILkp6T9ONUPkzSfEnT0883WjvW9kTSXElV6dhWprINJD0o6eX0e/3WjrO9kPTl3N/ydEkfSDrLf+fNS9J1kt6WNCtXVuPftaRfpv/fX5L09WaLw3MQ2jdJnYHZwIHAG2SPzj4uIp5v1cDaGUmbAptGxDRJ3YGpwGHA0cDiiLiwNeNrryTNBSoi4t+5sj8C70XE8JQQrx8Rv2itGNur9H/LfGB3ssfc+++8mUjaB1gM/D0i+qSykn/X6UGKY4DdgM2Ah4BtI2J5U+PwCEL7txvwz4h4NSI+AW4CDm3lmNqdiFhQ/ZTTiFgEvAB8oXWj6rAOBUal5VFkiZo1v/2BVyJiXmsH0t5ExCTgvYLimv6uDwVuioiPI2IO8E+y//ebzAlC+/cF4PXc6zfwiatFSSoH+gFPp6LTJc1Mw4Ye7m5eAYyXNFXS4FS2SUQsgCxxAzZutejat2PJPrlW8995y6rp77rF/o93gtD+qUSZryu1EEndgNuBsyLiA+BvwJeAvsAC4M+tF127tFdE7AIcApyWhmathUlaE/g2cGsq8t9562mx/+OdILR/bwBfzL3eHHizlWJp1yR1IUsORkfEHQAR8VZELI+IFcDVNNPQn2Ui4s30+23gTrLj+1aaE1I9N+Tt1ouw3ToEmBYRb4H/zleRmv6uW+z/eCcI7d8UoJekrVLWfywwtpVjanckCbgWeCEi/pIr3zRX7XBgVmFbaxxJ66QJoUhaBziI7PiOBU5I1U4A7m6dCNu148hdXvDf+SpR09/1WOBYSWtJ2groBTzTHBv0txg6gPSVo4uAzsB1EfH71o2o/ZG0N/AYUAWsSMW/IvuPtC/ZkN9c4L+rryNa00jammzUALIn094YEb+XtCFwC7AF8BrwnYgonPBljSSpK9k1760jYmEq+wf+O282ksYAA8ge6fwW8BvgLmr4u5Z0LnAy8CnZ5c3/a5Y4nCCYmZlZIV9iMDMzsyJOEMzMzKyIEwQzMzMr4gTBzMzMijhBMDMzsyJOEMyakaTl6Wl2syTdI2m9OuoPk3R2HXUOSw9kqX79P5IOaIZYR0o6qqn9NHCbZ6WvybUZkrZL79mzkr5UsG6upMcKyqZXP2VPUoWkS5ohhvL8k/sK1l2Tf/9bmqRNJN0o6dV0C+snJR2+qrZvbYcTBLPm9VFE9E1PYHsPOK0Z+jwMWHmCiIhfR8RDzdDvKpWe/ncW0KYSBLLje3dE9IuIV0qs7y7piwCSts+viIjKiDizvhtKx6BBIuKUVfX01XTDr7uASRGxdUT0J7u52uYtvN01WrJ/axwnCGYt50nSQ1MkfUnS/ekT2WOStiusLOmHkqZImiHpdkldJe1Jds/7P6VPrl+q/uQv6RBJt+TaD5B0T1o+KH3ymybp1vSMiBqlT8p/SG0qJe0i6QFJr0j6Ua7/SZLulPS8pCsldUrrjpNUlUZOLsj1uziNeDwNnEv2ONoJkiak9X9L23tO0m8L4vltir+q+nhJ6ibp+lQ2U9KR9d1fSX0lPZXa3Slp/XQTsbOAU6pjKuEW4Ji0XHgHwQGSxtURW/4Y7CHpp+k4zZJ0Vm47a0galdreVj3SImmipIp6HOcL0t/XQ5J2S+1elfTtVKezpD+lv7GZkv67xL5+DfgkIq6sLoiIeRFxaW19pOMwMcX9oqTRKdlAUn9Jj6bYHtBntwuemP7mHgV+LGmgpKeVjeQ8JGmTGt4PW1Uiwj/+8U8z/QCL0+/OZA+yOTi9fhjolZZ3Bx5Jy8OAs9Pyhrl+zgfOSMsjgaNy60YCR5HdPfA1YJ1U/jfgu2R3X5uUK/8F8OsSsa7sl+zud6em5b8CM4HuwEbA26l8ALAU2Drt34Mpjs1SHBulmB4BDkttAjg6t825QM/c6w1yx2sisFOuXvX+/z/gmrR8AXBRrv36DdjfmcC+afl/qvvJvwcl2swFtgWeSK+fJRvNmZU7JuNqiq3wGAD9ye62uQ7QDXiO7Mmf5aneXqnedXz2dzERqKjHcT4kLd8JjAe6ADsD01P5YOC8tLwWUAlsVbC/ZwJ/reXvu2Qf6TgsJBtp6ESWHO+dYngC2Ci1OYbsbq7V+3VFwXtZffO+U4A/t/a/547+42Eds+a1tqTpZP/hTwUeTJ9m9wRuTR+qIPvPtVAfSecD65GdPB6obUMR8amk+4GBkm4Dvgn8HNiX7CT2eNremmT/Ydel+hkdVUC3iFgELJK0VJ/NpXgmIl6FlbeD3RtYBkyMiHdS+WhgH7Kh6uVkD7CqydHKHtO8BrBpintmWndH+j0VOCItH0A25F19DN6X9K269ldSD2C9iHg0FY3isycR1uU94H1JxwIvAB/WUK8otrSYPwZ7A3dGxJIU1x3AV8mO/esR8XiqdwPZyfrCXP+7UvNx/gS4P9WrAj6OiGWSqsj+FiF7VsVO+mzeSQ+y+/bPqWnHJV2eYv4kInatpY9PyP423kjtpqft/gfoQ/bvALJEMH8L5ptzy5sDN6cRhjVri8tWDScIZs3ro4jom05I48jmIIwE/hMRfetoO5LsE+EMSSeSfSqry81pG+8BUyJiURrafTAijmtg7B+n3ytyy9Wvq/+vKLw3e1D6cbPVlkbE8lIrlD1Y5mxg13SiHwmUlYhneW77KhFDY/e3IW4GLgdOrKVOqdjg88egtmNV6tgW9l+TZZE+epN7/yJihT67vi+yUZnaEs/ngCNXBhBxmqSeZCMFNfYhaQCf/5upfs8EPBcRe9SwvSW55UuBv0TE2NTfsFritFXAcxDMWkBkD7E5k+wE+BEwR9J3IJsIJmnnEs26AwuUPTb6+Fz5orSulInALsAP+ezT2FPAXpK2SdvrKmnbpu3RSrspezJoJ7Lh4snA08C+knoqm4R3HPBoDe3z+7Iu2QliYbrefEg9tj8eOL36haT1qcf+pvfjfUlfTUXfqyXGUu4E/kjtozqlYis0CTgsxbgO2ZMPq78lsYWk6hPpcWTHNq8hx7mUB4BT098XkrZNMeQ9ApRJOjVXlp9UWp8+8l4CNqreL0ldJPWuoW4PYH5aPqGGOrYKOUEwayER8Swwg2zY+XjgB5JmkH1KO7REk6FkJ4EHgRdz5TcB56jE1/DSJ9NxZCfXcansHbJPumMkzSQ7gRZNimykJ4HhZI/znUM2XL4A+CUwgWx/p0VETY9YHgH8n6QJETGD7Jr+c2TX3B+voU3e+cD6aZLeDGC/BuzvCWSTPWeSPXnwf+qxPQAiYlFEXBARnzQkthL9TCMbKXqG7L2+Jv2dQHb54oQU3wZkc0rybRtynEu5BngemKbsK5VXUTCKnEYhDiNLROZIeobscswv6ttHQX+fkM1TuSAdk+lkl9tKGUZ2Ge4x4N8N2C9rIX6ao5nVSxr2PTsivtXKoZjZKuARBDMzMyviEQQzMzMr4hEEMzMzK+IEwczMzIo4QTAzM7MiThDMzMysiBMEMzMzK/L/AY1qxU3bWlRYAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.6654654644896629, pvalue=4.262271962549621e-14)\n", "Slope and P-value = PearsonRResult(statistic=-0.5046993639853017, pvalue=0.1133531613107735)\n", "Slope and P-value = PearsonRResult(statistic=-0.8960240259474506, pvalue=5.0792259733338664e-11)\n", "Slope and P-value = PearsonRResult(statistic=0.17234549688642786, pvalue=0.08641085291486883)\n", "Slope and P-value = PearsonRResult(statistic=-0.918850544468351, pvalue=1.5387199825992882e-27)\n", "Slope and P-value = PearsonRResult(statistic=-0.7445109845604622, pvalue=6.810407789528541e-19)\n", "Slope and P-value = PearsonRResult(statistic=-0.9215829665437967, pvalue=3.3114537979678407e-34)\n", "Slope and P-value = PearsonRResult(statistic=-0.8251767271889463, pvalue=0.1748232728110537)\n", "Slope and P-value = PearsonRResult(statistic=0.49570613518217194, pvalue=1.5722072055295422e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.8165159509111293, pvalue=7.61799191769311e-11)\n", "0.0 681\n", "1.0 14\n", "Name: new_Moisture, dtype: int64\n" ] }, { "data": { "image/png": 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5OPpKeigtnyapStIsSZeksmHARmm047ZU9nVJk1PZdXUlK6nP8SmGKknH1XM/zczMVtOmEghJFcCJwN7ACcBq9zcvQz+gKvd8aUT0Af4O/BI4IiL2ASqBHwPjgAMlbZzqDwDulLQNcAlwGNAT2E9S/4gYCiyJiJ4RMVDSLmmb3mkEpBoYWEd8S4HjUwyHAr+XpMJKkgZLqpRUWb14Qf2OhJmZtTlt7WucfYAHI2IJQM0IQJkuk/RL4D3g27nyO9PjgcCuwJPpfL0B8HREfCppLNBP0j3AV4H/JkscHo+I91JMtwEHAw8U9Hs4sC8wJbW7EfBuHXEK+B9JBwMrgM8BWwP/yleKiOHAcIANu/WI0g6BmZm1dW0tgVjtE3g9nBcR9xQp/zjXx7iIOK1InTuBs4D5wJSIWFhsVKAWAkZGxM9KrD8Q2BLYNyKWS5oDdChxWzMzszq1qUsYwCSyEYAOkjqRjQI0tGeA3pJq5kd0lLRTWvc4sA/wXT4bsXgWOERS1zSn4TRgQlq3XFL7tDweOEnSVqndLpK2qyOOzsC7KXk4FKirrpmZWVnaVAIREVOA0cAM4D6y+QkNeuE/XYoYBIySNJMsodg5rasGxgDHpEciYh7wM+CxFNe0iHgwNTccmCnptoh4gWxuxSOp3XFAtzpCuQ2okFRJNhrxYkPup5mZtW2KaFuXvSV1iohFkjqSfZNicERMa+64WoKKioqorKxs7jDMzKwFkTQ1Ilb70kFbmwMBMFzSrmTzAUY6eTAzMytfm0sgIuL0/HNJ1wC9C6r1AF4pKLsiIm5uzNjMzMzWFW0ugSgUEWc1dwxmZmbrmjY1idLMzMwahhMIMzMzK5sTCDMzMyubEwgzMzMrmxMIMzMzK5sTCDMzMyubEwgzMzMrW5v/HQj7TNVbC+g+9OHmDsPMajFnWGPc/8+sfjwCYWZmZmVzAmFmZmZlW6cTCEkjJL0uabqkaZJ6lbHtIElXN1Ac/ydpswZo53FJq93xTNKxkoaubftmZmYNpTXMgTgvIu6RdBRwHbBnUwcQEV8pta6k9SPi0zLbHw2MLjswMzOzRtLsIxCSzpf0oqRxkkZJOreeTU0Edkxtfl3S5DQycZ2kdqn8TEkvS5pA7g6ckraTNF7SzPS4bSofIenPkh6T9JqkQyTdJGm2pBG57edI6pqWv5namSHp1lw7f5D0GHCJpJ6Snkn17pe0eW4/vi7pKUmzJO2ftl85WpLaOinX96L02FfSBEl3pX0cJmlgOg5Vknao5fgPllQpqbJ68YJ6HnozM2trmjWBSMP1JwJ7AycAqw3fl6EfUCVpF2AA0DsiegLVwEBJ3YDfkCUORwK75ra9GrglIvYEbgOuzK3bHDgM+C/gIeCPwG7AHpJ6FuzPbsAvgMMiYi/gR7nVOwFHRMRPgFuAn6b+qoBf5+ptHBFfBv4TuKnMY1DT5x7AN4CdImJ/4Abg7GIbRMTwiKiIiIp2HTuX2Z2ZmbVVzT0C0Qd4MCKWRMRCshN0uS6TNB0YDHwbOBzYF5iSyg8HvggcADweEe9FxCfAnbk2egG3p+VbU1w1HoqIIDvRvxMRVRGxAnge6F4Qy2HAPRHxPkBEzM+tuzsiqiV1BjaLiAmpfCRwcK7eqLTtRGDTMudWTImIeRGxDHgVeCSVVxWJ1czMrN6aew6EGqCN8yLinpUNSocCIyPiZ6t0JPUHosQ28/WWpccVueWa54XHT3X08XE9+i72/FNS4idJwAZFYq2JLx97c7/WZmbWijT3CMQkoJ+kDpI6AQ3xKynjgZMkbQUgqYuk7YBngb6StpDUHjg5t81TwKlpeWCKq759nyJpi5q+CytExALgQ0kHpaJvABNyVQakbfsAC1L9vDlkIywAxwHt6xmrmZlZvTXrp9KImCJpNDADeAOoBNZqJl9EvCDpl8AjktYDlgNnRcQzki4AngbmAdOAdmmzc4CbJJ0HvAecWc++n5f0O2CCpGrgOWBQkapnANdK6gi8VtDfh5KeAjYFvlVk2+uBByVNJktYSh3ZMDMzazDKLu83YwBSp4hYlE6mE4HBETGtWYNqoyoqKqKysrK5wzAzsxZE0tSIWO1LDi3huvhwSbsCHcjmLjh5MDMza+GaPYGIiNPzzyVdQ+43GpIewCsFZVdExM2NGZuZmZkV1+wJRKGIOKu5YzAzM7O6Nfe3MMzMzGwd5ATCzMzMyuYEwszMzMrmBMLMzMzK5gTCzMzMyuYEwszMzMrmBMLMzMzK1uJ+B8KaT9VbC+g+9OHmDsPMgDnDGuLegmaNxyMQZmZmVjYnEGZmZla2Rk8gJI2QtFjSJrmyKySFpK5r2PbnueXukmY1cqx9JY1ppLafasg+JPWU9JW1j8zMzKx8TTUC8Q/gOABJ6wGHAm+VsN3P11yl6UlqV+42EfHlBg6jJ1BWAiHJc17MzKxBlJRASDpf0ouSxkkaJencMvsZBQxIy32BJ4FPc+1/XdJkSdMlXSepnaRhwEap7LZUtZ2k6yU9L+kRSRul7b8raYqkGZLuldQxlY+QdKWkpyS9JumkVC5Jl0maJalKUk1sAJtKul/SC5KuTQkPkhZJulDSs0CvWmL+gaRLc/s1SNJVNduX0MefJVWm/ftNrp390j7MSH12Bi4EBqT+B0jaWNJN6Tg8J+m4XAx3S3oIeKTIazs49VlZvXhBmS+rmZm1VWtMICRVACcCewMnABX16OcVYEtJmwOnAXfk2t+FLLnoHRE9gWpgYEQMBZZERM+IGJiq9wCuiYjdgH+nuADui4j9ImIvYDbw7Vzf3YA+wNeAYansBLJP8HsBRwCXSeqW1u0P/ATYA9gh1QXYGJgVEQcAHxSLGbgnV59U584ix6O2Pn4RERXAnsAhkvaUtEFq40dp/44APgZ+BdyZjs+dwC+ARyNiP7IRnsskbZza7QWcERGHFQYSEcMjoiIiKtp17FwkVDMzs9WVMqTdB3gwIpYApE+y9XEfcCpwAPC9XPnhwL7AFEkAGwHv1tLG6xExPS1PBbqn5d0lXQRsBnQC/pbb5oGIWAG8IGnr3D6Niohq4B1JE4D9gI+AyRHxGoCkUanuPWRJwr11xRwR76WRjgPJkqYvkY22FKqtj1MkDSZ7XboBuwIBzIuIKQAR8VHarrDNo4Bjc6NDHYBt0/K4iJhfJA4zM7N6KSWBWO1MVU93ANOAkRGxIncCVCr7WQltLMstV5OduAFGAP0jYoakQWSXSYpto4LHYqKW50tTwrGmmO8ETgFeBO6PiML2ivYhaXvgXGC/iPhQ0giyJEBF6hcj4MSIeGmVQukAshELMzOzBlPKHIhJQD9JHSR1Aur16yYR8SbZMPufClaNB06StBWApC6StkvrlktqX0LzmwDzUt2Ba6oMTCSbP9BO0pbAwcDktG5/SduneQkDyPa/UF0x3wf0J7tUU+zyRW19bEp2ol+QRkqOSXVfBLaRtF/qaxNlkyEXpv2u8TfgbKXMTNLeJRwHMzOzelljApGGzkcDM8hOjpVAvWbbRcR1EfFqQdkLwC+BRyTNBMaRDd8DDAdm5iZR1uZ84Nm07YslhHI/MJNsnx4F/jsi/pXWPU02V2IW8HqqW7gftcYcER8CLwDbRcTkwm1r6yMiZgDPAc8DN5EufUTEJ2RJxlWSZqS+OgCPAbvWTKIEfgu0Jztes9JzMzOzRqHiI+wFlaROEbEofbthIjA4IqY1enTWpCoqKqKysrK5wzAzsxZE0tQ0wX8Vpf4uwHBJu5J98h3p5MHMzKxtKymBiIjT888lXQP0LqjWg+ybB3lXRMTN9Q/PzMzMWqJ6/TJhRJzV0IGYmZnZusM30zIzM7OyOYEwMzOzsjmBMDMzs7I5gTAzM7OyOYEwMzOzsjmBMDMzs7I5gTAzM7Oy1et3IKx1qnprAd2HPtzcYVgLMmdYve6dZ2ZtgEcgzMzMrGxOIMzMzKxsTiBqIelASc+m22XPlnRBM8ZyrKShzdW/mZlZIc+BqN1I4JSImCGpHfClUjeUtH5EfNpQgUTEaGB0Q7VnZma2tlr1CISk8yW9KGmcpFGSzi1j862AeQARUR0RL6Q2u0h6QNJMSc9I2jOVXyBpuKRHgFskdZf0hKRp6e/LqV5fSY9LuifFdpskpXVfSWWTJF0paUwqHyTp6rQ8QtJJuX1clGt3gqS7JL0saZikgZImS6qStEMtx2iwpEpJldWLF5R5hM3MrK1qtSMQkiqAE4G9yfZzGjC1jCb+CLwk6XFgLDAyIpYCvwGei4j+kg4DbgF6pm32BfpExBJJHYEjI2KppB7AKKAi1dsb2A14G3gS6C2pErgOODgiXpc0qh67vRewCzAfeA24ISL2l/Qj4GxgSOEGETEcGA6wYbceUY8+zcysDWrNIxB9gAcjYklELAQeKmfjiLiQ7IT/CHA6WRJR0+6tqc6jwBaSOqd1oyNiSVpuD1wvqQq4G9g11/zkiJgbESuA6UB3YGfgtYh4PdWpTwIxJSLmRcQy4NUUO0BV6sPMzKxBtNoRCEBr20BEvAr8WdL1wHuStqil3ZpP7h/nyv4LeIdsVGA9YGlu3bLccjXZ61BqvJ+m9kiXPjaopd0VuecraN2vtZmZNbHWPAIxCegnqYOkTkBZv4gj6as1cxOAHmQn+n8DE4GBqU5f4P2I+KhIE52BeWmU4RtAuzV0+SLwRUnd0/MBtdSbQ3apBOA4spEOMzOzJtVqP5VGxBRJo4EZwBtAJVDOLMFvAH+UtJjsU//AiKhOX+e8WdJMYDFwRi3b/wm4V9LJwGOsOjpRLN4lkv4TGCvpfWByLVWvBx6UNBkYv6Z2y7HH5zpT6V8eNDOzEiii9c6bk9QpIhalCY0TgcERMa2546pNLl4B1wCvRMQfm6r/ioqKqKysbKruzMxsHSBpakRUFJa35ksYAMMlTSf7Bsa9LTl5SL6b4n2e7BLIdc0bjpmZWXGt9hIGQEScnn8u6Rqgd0G1HsArBWVXRMTNjRlbMWm0oclGHMzMzOqrVScQhSLirOaOwczMrDVo7ZcwzMzMrBE4gTAzM7OyOYEwMzOzsjmBMDMzs7I5gTAzM7OyOYEwMzOzsrWpr3Fa3areWkD3oQ83dxjWDOb4J8zNrEwegTAzM7OyOYEwMzOzsrWqBELSgZKelTRd0ux050wk9ZX05Qbs5/uSvlmkvLukWWvYdpCkqxsghgbdJzMzs3K0tjkQI4FTImKGpHbAl1J5X2AR8FSpDUlaPyI+LbYuIq5d20AbQF8acJ/MzMzK0eJGICSdL+lFSeMkjZJ0bhmbbwXMA4iI6oh4QVJ34PvAf6WRiYMkbSnpXklT0l/v1PcFkoZLegS4RdJ2ksZLmpket83VOzct7ytphqSngZX32pDUQdLNkqokPSfp0FycX5A0VtJLkn6d2+YBSVMlPS9pcK78aEnTUj/j12afyjiWZmZmtWpRIxCSKoATgb3JYpsGTC2jiT8CL0l6HBgLjIyIOZKuBRZFxOWpn9uBP0bEpJQU/A3YJbWxL9AnIpZIegi4JSJGSvoWcCXQv6DPm4GzI2KCpMty5WcBRMQeknYGHpG0U1q3P7A7sBiYIunhiKgEvhUR8yVtlMrvJUvyrgcOjojXJXVJdeq1T4UHLCUqgwHabbpl6UfazMzatBaVQAB9gAdrTnTpBF6yiLhQ0m3AUcDpwGlkQ/2FjgB2lVTzfFNJm6Tl0bkTbS/ghLR8K3BpvhFJnYHNImJCrs4xuX25KsX1oqQ3gJoEYlxEfJDauC/VrQTOkXR8qvMFsluNbwlMjIjXU1vza9n9UvdpFRExHBgOsGG3HlFL22ZmZqtoaQmE1lylbhHxKvBnSdcD70naoki19YBehSfVdPL9uK7mC56rSFl+XanthKS+ZElAr4hYnEZROqyhj7z67pOZmVnZWtociElAvzR/oBNQ1q/bSPqqPvsI3gOoBv4NLAQ2yVV9BPhhbruetTT5FHBqWh6Y4lspIv4NLJDUJ1enxsSa5+nSxbbAS2ndkZK6pEsV/YEngc7Ahyl52Bk4MNV9GjhE0vaprS6pvL77ZGZmttZaVAIREVOA0cAM4D6yYf0FZTTxDbI5ENPJLicMjIhq4CHg+JoJh8A5QEWaHPkC2YTEYs4BzpQ0M7X9oyJ1zgSuSZMo85/+/wS0k1QF3AkMiohlad2kFN904N40/2EssH7q67fAM+mYvEc2R+E+STNSW6zFPpmZma01RbSsy96SOkXEIkkdyT7FD46Iac0dV1tQUVERlZWVzR2GmZm1IJKmRkRFYXlLmwMBMFzSrmTX/0c6eTAzM2t5WlwCERGn559LugboXVCtB/BKQdkVEXFzY8ZmZmZmmRaXQBSKiLPWXMvMzMyaUouaRGlmZmbrBicQZmZmVjYnEGZmZlY2JxBmZmZWNicQZmZmVjYnEGZmZlY2JxBmZmZWthb/OxDWdKreWkD3oQ83dxjWiOYMK+v+dGZmtfIIhJmZmZXNCYSZmZmVrcUmEJKq062qZ0m6O92ds9RtB0m6upZ1T61h2+6STs89r5B0ZemRr9xujqSqtA9Vko4rt43UzrGShqblCySdm5ZHSDqpPm2amZmtrRabQABLIqJnROwOfAJ8P79SUrv6NBoRX15Dle7AygQiIioj4pz69AUcGhE9gZOAspOQ1P/oiBhWz/7NzMwaRUtOIPKeAHaU1FfSY5JuB6okdZB0c/qE/5ykQ3PbfEHSWEkvSfp1TaGkRelRki5LIxxVkgakKsOAg9LIwX+lPsekbTrl+psp6cQS498U+DAXwwOSpkp6XtLgXPnRkqZJmiFpfCqrdTQlt90cSV3TcoWkx9PyIWk/pqfjs0mRbQdLqpRUWb14QYm7Y2ZmbV2L/xaGpPWBY4CxqWh/YPeIeF3STwAiYg9JOwOPSNopXw9YDEyR9HBEVOaaPgHoCewFdE11JgJDgXMj4mup/765bc4HFkTEHmnd5msI/zFJAr4InJIr/1ZEzJe0Uer3XrJk7nrg4LRvXUo4PGtyLnBWRDwpqROwtLBCRAwHhgNs2K1HNECfZmbWBrTkEYiNJE0HKoE3gRtT+eSIeD0t9wFuBYiIF4E3gJoEYlxEfBARS4D7Ut28PsCoiKiOiHeACcB+a4jpCOCamicR8WEddSG7hLE7sAdwdTqJA5wjaQbwDPAFoAdwIDCxZt8iYv4a2i7Fk8AfJJ0DbBYRnzZAm2ZmZi16BGJJmj+wUvZhno/zRXVsX/hpuvB5XdvWRkXaWaOIeFXSO8CuaTLoEUCviFicLjd0qG/byad8lgx2yPU7TNLDwFeAZyQdkRItMzOztdKSRyBKMREYCJAuXWwLvJTWHSmpS7pM0J/s03jhtgMktZO0JXAwMBlYCKw2VyB5BPhhzZMSLmHU1NsK2J5shKQz8GFKHnYmG3kAeBo4RNL2aZtyLmHMAfZNyyvnZUjaISKqIuISspGcncto08zMrFbregLxJ6CdpCrgTmBQRCxL6yaRXd6YDtxbMP8B4H5gJjADeBT474j4Vyr7NE1k/K+CbS4CNk8TL2cAh1K3x9JlmMeAoelSyVhgfUkzgd+SXcYgIt4DBgP3pbbvLOM4/Aa4QtITQHWufEgu1iXAX8to08zMrFaK8Lw5y1RUVERlZWGeZWZmbZmkqRFRUVi+ro9AmJmZWTNoyZMo1wmSngU2LCj+RkRUNUc8ZmZmTcEJxFqKiAOaOwYzM7Om5ksYZmZmVjYnEGZmZlY2JxBmZmZWNicQZmZmVjYnEGZmZlY2JxBmZmZWNicQZmZmVjb/DoStVPXWAroPfbi5w7BGMmfYV5s7BDNrRTwCYWZmZmVzAmFmZmZlcwJRIknrS3pf0sUl1D1W0tBa1i1Kj9tIuqeh4zQzM2sKTiBKdxTwEnCKJNVVMSJGR8SwNdR5OyJOasgAzczMmkqbSSAknS/pRUnjJI2SdG6ZTZwGXAG8CRyYa/doSdMkzZA0PpUNknR1Wt5e0tOSpkj6bW677pJmpeXdJE2WNF3STEk9UvmPJc1Kf0Ny270o6YZUfpukIyQ9KekVSfunevtLekrSc+nxS7Ucl8GSKiVVVi9eUOYhMTOztqpNJBCSKoATgb2BE4CKMrffCDgcGAOMIksmkLQlcD1wYkTsBZxcZPMrgD9HxH7Av2rp4vvAFRHRM8U2V9K+wJnAAWQJy3cl7Z3q75ja3RPYGTgd6AOcC/w81XkRODgi9gZ+BfxPsY4jYnhEVERERbuOnUs4GmZmZm0kgSA7uT4YEUsiYiHwUJnbfw14LCIWA/cCx0tqR3ZinxgRrwNExPwi2/YmSzoAbq2l/aeBn0v6KbBdRCxJMd8fER9HxCLgPuCgVP/1iKiKiBXA88D4iAigCuie6nQG7k6jHH8Editzn83MzGrVVhKIOucslOA04AhJc4CpwBbAoandKGH7OutExO3AscAS4G+SDqPumJflllfknq/gs9/2+C1Z0rM70A/oUEKcZmZmJWkrCcQkoJ+kDpI6ASX/oo6kTclGA7aNiO4R0R04iyypeBo4RNL2qW6XIk08CZyalgfW0scXgdci4kpgNNmliYlAf0kdJW0MHA88UWrcZCMQb6XlQWVsZ2ZmtkZtIoGIiClkJ+YZZJcCKoFSZwyeADwaEflP/Q+SjRh8BAwG7pM0A7izyPY/As6SNIXspF7MAGCWpOlkcxpuiYhpwAhgMvAscENEPFdizACXAhdLehJoV8Z2ZmZma6Ts0nnrJ6lTRCyS1JHs0/3gdJK2pKKiIiorK5s7DDMza0EkTY2I1b580JbuhTFc0q5kcwFGOnkwMzOrvzaTQETE6fnnkq4h+4ZEXg/glYKyKyLi5saMzczMbF3TZhKIQhFxVnPHYGZmtq5qswmEmZk1rOXLlzN37lyWLl3a3KFYPXTo0IHPf/7ztG/fvqT6TiDMzKxBzJ07l0022YTu3buzhlsGWQsTEXzwwQfMnTuX7bffvqRt2sTXOM3MrPEtXbqULbbYwsnDOkgSW2yxRVmjR04gzMyswTh5WHeV+9o5gTAzM7OyeQ6EmZk1iu5DH27Q9uYMK+0uBPfffz8nnHACs2fPZueddwbg8ccf5/LLL2fMmDEr6w0aNIivfe1rnHTSSfTt25d58+bRoUMHNthgA66//np69uwJwIIFCzj77LN58sknAejduzdXXXUVnTtnPy788ssvM2TIEF5++WXat2/PHnvswVVXXcXWW29d732dP38+AwYMYM6cOXTv3p277rqLzTfffJU6L730EgMGDFj5/LXXXuPCCy9kyJAhnHfeeTz00ENssMEG7LDDDtx8881sttlmVFVV8fvf/54RI0bUO7YaTiBspaq3FjT4P3hrfqX+p2vWWowaNYo+ffpwxx13cMEFF5S83W233UZFRQU333wz5513HuPGjQPg29/+Nrvvvju33HILAL/+9a/5zne+w913383SpUv56le/yh/+8Af69esHwGOPPcZ77723VgnEsGHDOPzwwxk6dCjDhg1j2LBhXHLJJavU+dKXvsT06dMBqK6u5nOf+xzHH388AEceeSQXX3wx66+/Pj/96U+5+OKLueSSS9hjjz2YO3cub775Jttuu2294wNfwjAzs1Zk0aJFPPnkk9x4443ccccd9WqjV69evPVWdi/Cf/zjH0ydOpXzzz9/5fpf/epXVFZW8uqrr3L77bfTq1evlckDwKGHHsruu+++Vvvx4IMPcsYZZwBwxhln8MADD9RZf/z48eywww5st912ABx11FGsv342RnDggQcyd+7clXX79etX72OT5wTCzMxajQceeICjjz6anXbaiS5dujBtWvl3LRg7diz9+/cH4IUXXqBnz560a/fZPQnbtWtHz549ef7555k1axb77rvvGttcuHAhPXv2LPr3wgsvrFb/nXfeoVu3bgB069aNd999t87277jjDk477bSi62666SaOOeaYlc8rKip44olybu5cnC9hmJlZqzFq1CiGDBkCwKmnnsqoUaPYZ599av2GQb584MCBfPzxx1RXV69MPCKi6La1lddmk002WXm5oaF98sknjB49mosvvni1db/73e9Yf/31GThw4Mqyrbbairfffnut+12nEghJI4BDyG7FvQI4KyKelvQ4cG5ElHQrSUmDgIqI+KGkC4BFEXF5HfXLar+OdoYAwyNicZnbXQhMjIi/r03/Zmat2QcffMCjjz7KrFmzkER1dTWSuPTSS9liiy348MMPV6k/f/58unbtuvL5bbfdxl577cXQoUM566yzuO+++9htt9147rnnWLFiBeutlw3ar1ixghkzZrDLLrvw7rvvMmHChDXGtnDhQg466KCi626//XZ23XXXVcq23npr5s2bR7du3Zg3bx5bbbVVrW3/9a9/ZZ999lltzsXIkSMZM2YM48ePXyXZWbp0KRtttNEaY16TdfESxnkR0RMYClzXzLGUawjQsZwNJLWLiF85eTAzq9s999zDN7/5Td544w3mzJnDP//5T7bffnsmTZpEjx49ePvtt5k9ezYAb7zxBjNmzFj5TYsa7du356KLLuKZZ55h9uzZ7Ljjjuy9995cdNFFK+tcdNFF7LPPPuy4446cfvrpPPXUUzz88GcT0MeOHUtVVdUq7daMQBT7K0weAI499lhGjhwJZInAcccdV+t+jxo1arXLF2PHjuWSSy5h9OjRdOy46mnn5ZdfXus5GtAMIxCSzgcGAv8E3gem1vXpvw4TgR1zz0+W9CdgM+DbEfGEpCeAsyNieur7SeAHdcTWE7iW7CT/KvCtiKhJWb8u6Upg01Q+WdL+wP8CGwFLgDMj4iVJ7YBLgP8AArgeELAN8Jik9yPiUElHAb8BNkz9nRkRiyTNAW4CjgKulnQ0MCYi7knrKiLifUkVwOUR0TeNpGwPdAN2An4MHAgcA7wF9IuI5UX2eTAwGKDdplvWdbzNzMrS1N8AGjVqFEOHDl2l7MQTT+T222/noIMO4i9/+QtnnnkmS5cupX379txwww0rv4qZt9FGG/GTn/yEyy+/nBtvvJEbb7yRs88+mx133JGIoFevXtx4440r644ZM4YhQ4YwZMgQ2rdvz5577skVV1yxVvsydOhQTjnlFG688Ua23XZb7r77bgDefvttvvOd7/B///d/ACxevJhx48Zx3XWrfp7+4Q9/yLJlyzjyyCOBbCLltddeC2TfEvnqV9f+tVFErHUjJXeWnfBuAHqRJS/TgOtKTSDSJYyaE+nJZJcVDkiXGKZGxE8kfQX4cUQcIekMYO+IGCJpJ+D2iKio7RKGpJlkCceEdNlg07Tt48ArEfFdSQcDf4qI3SVtCiyOiE8lHQH8ICJOlPQD4AhgQFrXJSLmF5z8uwL3AcdExMeSfgpsGBEXpnp/iohLi+x3vo3CBOII4FBgV+Bp4MSI+Kuk+4GREfFAXcd3w249otsZ/1vKS2HrEH+N05rK7Nmz2WWXXZo7DKvDsmXLOOSQQ5g0adLKb2nkFXsNJU2NiIrCuk09AtEHeDAilqSgHqpHG5dJ+iXwHvDtXPl96XEq0D0t3w2cL+k84FvAiNoaldQZ2Cwiai5mjUzb1xgFEBETJW0qaTNgE2CkpB5kIw01tzA7Arg2Ij5N28wv0uWBZCf6J9O1qQ3ITvo17qwt1jr8NSKWS6oC2gFjU3kVnx0TMzNro958802GDRtWNHkoV1MnEA3xI+nnRcQ9RcqXpcdq0n5FxGJJ44DjgFOA1TKoMhQO1QTwW+CxiDheUnfg8bROReoXEjAuIop/7wY+rqX8Uz6bu9KhYN0ygIhYIWl5fDa8tIJ1bMKsmZk1vB49etCjR48GaaupJ1FOAvpJ6iCpE9AUY6s3AFcCU2oZCQAgIhYAH0qqmSb7DSA/tXYAgKQ+wIJUvzPZ/AKAQbm6jwDfl7R+2qZLKl9INmoB8AzQW9KOqU7HdJllTeYANV86PrGE+mZmTaYpL4tbwyr3tWvST6URMUXSaGAG8AZQSfaVzMbsc6qkj4CbS6h+BnCtpI7Aa8CZuXUfSnqKNIkylV1Kdgnjx8Cjubo3kE1knClpOdkkyquB4cBfJc1LkygHAaMkbZi2+yXw8hpi/A1wo6SfA8+WsE8l2+Nznan09XIzq6cOHTrwwQcf+Jbe66CI4IMPPqBDh8KB7do16SRKAEmd0jcNOpJ9k2JwRJT/U2Gl97cN2aWFnSNiRWP10xpUVFREZeVa/dSFmbVhy5cvZ+7cuSxdurS5Q7F66NChA5///Odp3779KuUtZRIlwHBJu5Jdvx/ZyMnDN4HfkX0rw8mDmVkjat++Pdtvv31zh2FNpMkTiIg4Pf9c0jVA74JqPYBXCsquiIhSLkPk+7oFuKXsIM3MzKxOzT4zPyLOau4YzMzMrDzr4k9Zm5mZWTNr8kmU1nJJWgi81NxxtDFdyX7S3ZqOj3nT8zFveg15zLeLiNXuddDslzCsRXmp2ExbazySKn3Mm5aPedPzMW96TXHMfQnDzMzMyuYEwszMzMrmBMLyhjd3AG2Qj3nT8zFvej7mTa/Rj7knUZqZmVnZPAJhZmZmZXMCYWZmZmVzAmEASDpa0kuS/iFpaHPH09pI+oKkxyTNlvS8pB+l8gskvSVpevr7SnPH2tpImiOpKh3fylTWRdI4Sa+kx82bO87WQtKXcu/n6ZI+kjTE7/WGJekmSe9KmpUrq/V9Leln6f/3lyT9R4PE4DkQJqkd2W3EjwTmAlOA0yLihWYNrBWR1A3oFhHTJG0CTAX6A6cAiyLi8uaMrzWTNAeoiIj3c2WXAvMjYlhKmDePiJ82V4ytVfq/5S3gAOBM/F5vMJIOBhYBt0TE7qms6Ps63cByFLA/sA3wd2CniKhemxg8AmGQvan+ERGvRcQnwB3Acc0cU6sSEfNq7jwbEQuB2cDnmjeqNu04YGRaHkmWzFnDOxx4NSLeaO5AWpuImAjMLyiu7X19HHBHRCyLiNeBf5D9v79WnEAYZCeyf+aez8Unt0YjqTuwN/BsKvqhpJlpSNJD6Q0vgEckTZU0OJVtHRHzIEvugK2aLbrW7VSyT741/F5vXLW9rxvl/3gnEAagImW+ttUIJHUC7gWGRMRHwJ+BHYCewDzg980XXavVOyL2AY4BzkpDv9bIJG0AHAvcnYr8Xm8+jfJ/vBMIgywb/ULu+eeBt5spllZLUnuy5OG2iLgPICLeiYjqiFgBXE8DDCvaqiLi7fT4LnA/2TF+J81LqZmf8m7zRdhqHQNMi4h3wO/1JlLb+7pR/o93AmGQTZrsIWn79KnhVGB0M8fUqkgScCMwOyL+kCvvlqt2PDCrcFurP0kbp0mrSNoYOIrsGI8GzkjVzgAebJ4IW7XTyF2+8Hu9SdT2vh4NnCppQ0nbAz2AyWvbmb+FYQCkr1T9L9AOuCkifte8EbUukvoATwBVwIpU/HOy/2R7kg0nzgG+V3MN09aepC+SjTpAdvfh2yPid5K2AO4CtgXeBE6OiMIJaVZPkjqSXXP/YkQsSGW34vd6g5E0CuhLdtvud4BfAw9Qy/ta0i+AbwGfkl1C/etax+AEwszMzMrlSxhmZmZWNicQZmZmVjYnEGZmZlY2JxBmZmZWNicQZmZmVjYnEGZNTFJ1uhvhLEkPSdpsDfUvkHTuGur0TzfMqXl+oaQjGiDWEZJOWtt2yuxzSPoaYIshaef0mj0naYeCdXMkPVFQNr3mLomSKiRd2QAxdM/febFg3Q3517+xSdpa0u2SXks/Ef60pOObqn9rGZxAmDW9JRHRM91Bbz5wVgO02R9YeQKJiF9FxN8boN0mle7eOARoUQkE2fF9MCL2johXi6zfRNIXACTtkl8REZURcU6pHaVjUJaI+E5T3T03/SjaA8DEiPhiROxL9uNzn2/kftdvzPatfE4gzJrX06Sb2kjaQdLY9InuCUk7F1aW9F1JUyTNkHSvpI6Svkx2z4HL0iffHWpGDiQdI+mu3PZ9JT2Ulo9KnxynSbo73aejVumT9v+kbSol7SPpb5JelfT9XPsTJd0v6QVJ10paL607TVJVGnm5JNfuojRi8izwC7LbDT8m6bG0/s+pv+cl/aYgnt+k+KtqjpekTpJuTmUzJZ1Y6v5K6inpmbTd/ZI2Tz+yNgT4Tk1MRdwFDEjLhb/A2FfSmDXElj8GvST9OB2nWZKG5PpZX9LItO09NSM1kh6XVFHCcb4kvb/+Lmn/tN1rko5NddpJuiy9x2ZK+l6RfT0M+CQirq0piIg3IuKqutpIx+HxFPeLkm5LyQiS9pU0IcX2N332c8yPp/fcBOBHkvpJelbZSNDfJW1dy+thTSEi/Oc//zXhH7AoPbYju9HQ0en5eKBHWj4AeDQtXwCcm5a3yLVzEXB2Wh4BnJRbNwI4iezXF98ENk7lfwa+TvbrdRNz5T8FflUk1pXtkv164A/S8h+BmcAmwJbAu6m8L7AU+GLav3Epjm1SHFummB4F+qdtAjgl1+ccoGvueZfc8Xoc2DNXr2b//xO4IS1fAvxvbvvNy9jfmcAhafnCmnbyr0GRbeYAOwFPpefPkY0GzcodkzG1xVZ4DIB9yX6xdGOgE/A82d1bu6d6vVO9m/jsffE4UFHCcT4mLd8PPAK0B/YCpqfywcAv0/KGQCWwfcH+ngP8sY73d9E20nFYQDZSsR5Z8twnxfAUsGXaZgDZr+HW7NefCl7Lmh9A/A7w++b+99yW/zwkZNb0NpI0neyEMBUYlz4Nfxm4O30og+w/30K7S7oI2Izs5PK3ujqKiE8ljQX6SboH+Crw38AhZCe5J1N/G5D9h74mNfdIqQI6RcRCYKGkpfpsLsfkiHgNVv7cbh9gOfB4RLyXym8DDiYbCq8mu8lYbU5Rdhvu9YFuKe6Zad196XEqcEJaPoJsSL3mGHwo6Wtr2l9JnYHNImJCKhrJZ3eSXJP5wIeSTgVmA4trqbdabGkxfwz6APdHxMcprvuAg8iO/T8j4slU7y9kJ/PLc+3vR+3H+RNgbKpXBSyLiOWSqsjei5DdK2RPfTbvpTPZfRNer23HJV2TYv4kIvaro41PyN4bc9N201O//wZ2J/t3AFmimP+J6ztzy58H7kwjFBvUFZc1PicQZk1vSUT0TCesMWRzIEYA/46InmvYdgTZJ8oZkgaRfapbkztTH/OBKRGxMA0dj4uI08qMfVl6XJFbrnle8/9J4e/jB8VvJ1xjaURUF1uh7MY/5wL7pURgBNChSDzVuf5VJIb67m857gSuAQbVUadYbLDqMajrWBU7toXt12Z5pI/u5F6/iFihz+YXiGxUp67E9HngxJUBRJwlqSvZSEOtbUjqy6rvmZrXTMDzEdGrlv4+zi1fBfwhIkan9i6oI05rZJ4DYdZMIrvJ0DlkJ8glwOuSToZsopqkvYpstgkwT9mtwQfmyhemdcU8DuwDfJfPPs09A/SWtGPqr6OkndZuj1baX9mdXdcjG46eBDwLHCKpq7JJgqcBE2rZPr8vm5KdQBak693HlND/I8APa55I2pwS9je9Hh9KOigVfaOOGIu5H7iUukeFisVWaCLQP8W4MdmdK2u+5bGtpJoT7WlkxzavnONczN+AH6T3F5J2SjHkPQp0kPSDXFl+0mspbeS9BGxZs1+S2kvarZa6nYG30vIZtdSxJuIEwqwZRcRzwAyyYe2BwLclzSD7lHdckU3OJztJjANezJXfAZynIl8zTJ9sx5CdfMeksvfIPimPkjST7AS72qTNenoaGEZ2u+bXyYbj5wE/Ax4j299pEVHbLbSHA3+V9FhEzCCbU/A82TX/J2vZJu8iYPM0iXAGcGgZ+3sG2WTUmWR3jrywhP4AiIiFEXFJRHxSTmxF2plGNtI0mey1viG9TyC7PHJGiq8L2ZyW/LblHOdibgBeAKYp+8rodRSMVKdRjP5kicrrkiaTXe75aaltFLT3Cdk8mUvSMZlOdjmvmAvILvM9Abxfxn5ZI/DdOM2swaRh5XMj4mvNHIqZNTKPQJiZmVnZPAJhZmZmZfMIhJmZmZXNCYSZmZmVzQmEmZmZlc0JhJmZmZXNCYSZmZmV7f8DZafuTpYv5W4AAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.6213884782770915, pvalue=5.262179018489625e-12)\n", "Slope and P-value = PearsonRResult(statistic=-0.3362474524326795, pvalue=0.0006253836257539709)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/mosesayoola/.local/lib/python3.10/site-packages/scipy/stats/_stats_py.py:4424: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n", " warnings.warn(stats.ConstantInputWarning(msg))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "Slope and P-value = PearsonRResult(statistic=0.848570567012974, pvalue=7.588025376900079e-29)\n", "Slope and P-value = PearsonRResult(statistic=0.8653755759719297, pvalue=3.6407174594226867e-31)\n", "Slope and P-value = PearsonRResult(statistic=-0.18806063380282723, pvalue=0.06096936970592015)\n", "Slope and P-value = PearsonRResult(statistic=0.4957061351821719, pvalue=1.5722072055295422e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.718062358113876, pvalue=4.163648168160238e-17)\n", "Slope and P-value = PearsonRResult(statistic=0.7344219098582088, pvalue=0.0011958444433141481)\n", "Slope and P-value = PearsonRResult(statistic=0.8205286873307193, pvalue=1.5292480187036916e-25)\n" ] } ], "source": [ "print(\"Median:\",poultry.Moisture.median())\n", "\n", "poultry.loc[poultry['Moisture'] < poultry.Moisture.median(), 'new_Moisture'] = 0\n", "poultry.loc[poultry['Moisture'] >= poultry.Moisture.median(), 'new_Moisture'] = 1\n", "print(\"Total sample count:\", poultry.Moisture.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_Moisture.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_Moisture','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_Moisture'],axis='columns'),sample.new_Moisture,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"Moisture_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_Moisture']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"Moisture in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Moisture','SampleType'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Moisture_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (27) TotalC" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 7.909\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 818\n", "0.0 817\n", "Name: new_TotalC, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 653\n", "0.0 45\n", "Name: new_TotalC, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.2949075182657483, pvalue=0.002896258396921753)\n", "Slope and P-value = PearsonRResult(statistic=-0.6359110925802056, pvalue=0.014505845021457102)\n", "Slope and P-value = PearsonRResult(statistic=-0.7232098411381666, pvalue=1.941023090490515e-17)\n", "Slope and P-value = PearsonRResult(statistic=-0.8415912522729246, pvalue=1.9488767956646437e-14)\n", "Slope and P-value = PearsonRResult(statistic=-0.8515475450359992, pvalue=3.0929248256739834e-29)\n", "Slope and P-value = PearsonRResult(statistic=-0.3611356581864716, pvalue=0.0542600480270422)\n", "Slope and P-value = PearsonRResult(statistic=-0.4587696274688947, pvalue=1.5837486781624198e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.3121173072074166, pvalue=0.04159039629975407)\n", "Slope and P-value = PearsonRResult(statistic=-0.6249341951860907, pvalue=3.673395090168507e-12)\n", "Slope and P-value = PearsonRResult(statistic=0.29891894477954045, pvalue=0.0025194291783716765)\n", "0.0 623\n", "1.0 72\n", "Name: new_TotalC, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.5370020404783258, pvalue=8.424209931693283e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.8472887484546139, pvalue=1.1101520185262722e-28)\n", "Slope and P-value = PearsonRResult(statistic=0.8030663434789747, pvalue=9.21210316530533e-24)\n", "Slope and P-value = PearsonRResult(statistic=0.8458572526137573, pvalue=1.6910273571942933e-28)\n", "Slope and P-value = PearsonRResult(statistic=0.5319216911186864, pvalue=0.002979500425616636)\n", "Slope and P-value = PearsonRResult(statistic=-0.12049430758875347, pvalue=0.23241913104571701)\n", "Slope and P-value = PearsonRResult(statistic=0.9218710467821151, pvalue=1.584449753568666e-10)\n", "Slope and P-value = PearsonRResult(statistic=-0.38681669404986996, pvalue=7.024865077600194e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.49538834618562533, pvalue=1.605656085334004e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.724774123258319, pvalue=1.5340093608078384e-17)\n" ] } ], "source": [ "print(\"Median:\",poultry.TotalC.median())\n", "\n", "poultry.loc[poultry['TotalC'] < poultry.TotalC.median(), 'new_TotalC'] = 0\n", "poultry.loc[poultry['TotalC'] >= poultry.TotalC.median(), 'new_TotalC'] = 1 \n", "print(\"Total sample count:\", poultry.TotalC.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_TotalC.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_TotalC','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_TotalC'],axis='columns'),sample.new_TotalC,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", " \n", " print(pd.value_counts(sample['new_TotalC']))\n", " \n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"TotalC in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_TotalC','SampleType'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"TotalC_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (28) TotalN" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 0.535\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 818\n", "0.0 817\n", "Name: new_TotalN, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 614\n", "0.0 84\n", "Name: new_TotalN, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.9790395199061515, pvalue=1.5458242449843434e-69)\n", "Slope and P-value = PearsonRResult(statistic=-0.9727124870449724, pvalue=5.461948418924877e-64)\n", "Slope and P-value = PearsonRResult(statistic=-0.0584160876818164, pvalue=0.563730152823844)\n", "Slope and P-value = PearsonRResult(statistic=-0.40145981479678045, pvalue=3.478065990433423e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.8422990968346006, pvalue=6.202566006719856e-14)\n", "Slope and P-value = PearsonRResult(statistic=-0.5041657807949624, pvalue=2.9962571208693295e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.8878119020458928, pvalue=8.416054616383505e-35)\n", "Slope and P-value = PearsonRResult(statistic=-0.6915483952198219, pvalue=1.6477075974422557e-15)\n", "Slope and P-value = PearsonRResult(statistic=-0.9215923249974238, pvalue=4.601346450500594e-42)\n", "Slope and P-value = PearsonRResult(statistic=-0.8487627310609072, pvalue=3.9850408522838166e-24)\n", "0.0 595\n", "1.0 100\n", "Name: new_TotalN, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.6603315060123813, pvalue=7.785044160829846e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.454937940760688, pvalue=6.643791355769736e-06)\n", "Slope and P-value = PearsonRResult(statistic=0.7545633865572974, pvalue=1.2462623281743047e-19)\n", "Slope and P-value = PearsonRResult(statistic=-0.9457730331264642, pvalue=1.1775749957254106e-49)\n", "Slope and P-value = PearsonRResult(statistic=0.9298981887367856, pvalue=2.333592890367631e-44)\n", "Slope and P-value = PearsonRResult(statistic=0.9473720258694934, pvalue=2.8236928230910305e-50)\n", "Slope and P-value = PearsonRResult(statistic=0.7539210206895369, pvalue=1.3925074798254702e-19)\n", "Slope and P-value = PearsonRResult(statistic=0.6529108960514471, pvalue=1.8219312813040267e-13)\n", "Slope and P-value = PearsonRResult(statistic=0.951943492849466, pvalue=3.672081178656057e-52)\n", "Slope and P-value = PearsonRResult(statistic=0.33294684152364634, pvalue=0.0007125087844425886)\n" ] } ], "source": [ "print(\"Median:\",poultry.TotalN.median())\n", "\n", "poultry.loc[poultry['TotalN'] < poultry.TotalN.median(), 'new_TotalN'] = 0\n", "poultry.loc[poultry['TotalN'] >= poultry.TotalN.median(), 'new_TotalN'] = 1 \n", "print(\"Total sample count:\", poultry.TotalN.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_TotalN.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_TotalN','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_TotalN'],axis='columns'),sample.new_TotalN,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", " \n", " print(pd.value_counts(sample['new_TotalN']))\n", " \n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"TotalN in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_TotalN','SampleType'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"TotalN_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (29) CNRatio" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 12.16717791411043\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 818\n", "0.0 817\n", "Name: new_CNRatio, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "0.0 373\n", "1.0 325\n", "Name: new_CNRatio, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.38930020386432174, pvalue=6.249764376462456e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.8772900669850212, pvalue=5.235152876440665e-33)\n", "Slope and P-value = PearsonRResult(statistic=0.8476459821759187, pvalue=9.988037131904033e-29)\n", "Slope and P-value = PearsonRResult(statistic=0.8363726011494342, pvalue=2.477409045845415e-27)\n", "Slope and P-value = PearsonRResult(statistic=0.6329175526157246, pvalue=1.6079089873174683e-12)\n", "Slope and P-value = PearsonRResult(statistic=0.04547978476749519, pvalue=0.6532062242448322)\n", "Slope and P-value = PearsonRResult(statistic=-0.4152662526278404, pvalue=1.738149524063037e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.9351400830979035, pvalue=5.8812866489508985e-46)\n", "Slope and P-value = PearsonRResult(statistic=0.929890759211458, pvalue=2.345318240813469e-44)\n", "Slope and P-value = PearsonRResult(statistic=0.7502637001326913, pvalue=2.6023593453858034e-19)\n", "1.0 376\n", "0.0 319\n", "Name: new_CNRatio, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.8869659001211818, pvalue=1.1908184249766694e-34)\n", "Slope and P-value = PearsonRResult(statistic=-0.8760732130090032, pvalue=8.235811765062433e-33)\n", "Slope and P-value = PearsonRResult(statistic=-0.9672602319973088, pvalue=3.608364935048694e-60)\n", "Slope and P-value = PearsonRResult(statistic=0.6077582385477664, pvalue=2.0105472671409597e-11)\n", "Slope and P-value = PearsonRResult(statistic=0.6636845782816885, pvalue=5.2598482573648164e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.822342290884873, pvalue=9.740868270186475e-26)\n", "Slope and P-value = PearsonRResult(statistic=-0.8813050541890186, pvalue=1.1337933974997882e-33)\n", "Slope and P-value = PearsonRResult(statistic=-0.8496788986730601, pvalue=5.445094598056892e-29)\n", "Slope and P-value = PearsonRResult(statistic=-0.6336608442079794, pvalue=1.487052566006935e-12)\n", "Slope and P-value = PearsonRResult(statistic=-0.9261946366121326, pvalue=2.6571530068808507e-43)\n" ] } ], "source": [ "print(\"Median:\",poultry.CNRatio.median())\n", "\n", "poultry.loc[poultry['CNRatio'] < poultry.CNRatio.median(), 'new_CNRatio'] = 0\n", "poultry.loc[poultry['CNRatio'] >= poultry.CNRatio.median(), 'new_CNRatio'] = 1 \n", "print(\"Total sample count:\", poultry.CNRatio.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_CNRatio.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_CNRatio','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_CNRatio'],axis='columns'),sample.new_CNRatio,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"CNRatio_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_CNRatio']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"CNRatio in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_CNRatio','SampleType'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"CNRatio_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (30) Al" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 654.682\n", "Total sample count: 818 \n", "\n", "Distribution:\n", "1.0 409\n", "0.0 409\n", "Name: new_Al, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (0, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "0.0 369\n", "1.0 329\n", "Name: new_Al, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.6257796100964803, pvalue=3.3694277047884633e-12)\n", "Slope and P-value = PearsonRResult(statistic=-0.32709401445318104, pvalue=0.0008947576751324364)\n", "Slope and P-value = PearsonRResult(statistic=0.42049808249521203, pvalue=1.3256378031388672e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.6221893219092421, pvalue=4.853792616645103e-12)\n", "Slope and P-value = PearsonRResult(statistic=0.9321738809303924, pvalue=4.895366362725472e-45)\n", "Slope and P-value = PearsonRResult(statistic=0.552026717888934, pvalue=2.6258169810193547e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.6385270732654068, pvalue=8.869131266193153e-13)\n", "Slope and P-value = PearsonRResult(statistic=0.6890457542691464, pvalue=2.284796944529741e-15)\n", "Slope and P-value = PearsonRResult(statistic=0.917366770198724, pvalue=5.432484875591208e-41)\n", "Slope and P-value = PearsonRResult(statistic=0.9631552357554449, pvalue=1.0675191630219986e-57)\n" ] } ], "source": [ "print(\"Median:\",poultry.Al.median())\n", "\n", "poultry.loc[poultry['Al'] < poultry.Al.median(), 'new_Al'] = 0\n", "poultry.loc[poultry['Al'] >= poultry.Al.median(), 'new_Al'] = 1\n", "print(\"Total sample count:\", poultry.Al.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_Al.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_Al','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_Al'],axis='columns'),sample.new_Al,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"Al_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_Al']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"Al in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Al','SampleType'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Al_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (31) B" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 6.348\n", "Total sample count: 818 \n", "\n", "Distribution:\n", "1.0 409\n", "0.0 409\n", "Name: new_B, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (0, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "0.0 369\n", "1.0 329\n", "Name: new_B, dtype: int64\n" ] }, { "data": { "image/png": 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mZtZUnSpoiIiT868lXQ0cWJKtL/BSSdoVEXFDW/bNzMxsXdepgoZSEXFmpftgZmbWUXSahZBmZmbWMg4azMzMrBAHDWZmZlaIgwYzMzMrxEGDmZmZFeKgwczMzApx0GBmZmaFdOr7NHR2NQsW02fkxEp3w8w6mHmjmvS8P1uPeKTBzMzMCnHQYGZmZoWs10GDpLGSjmtB+T9J2ryRPBdJOixtz5PUq7ntlWtX0tL0u0963LaZmVlFeE1DAyLiSwXyXFCJds3MzNrbOj/SIOl8Sc9LmiRpvKRzW1DXTyV9L/f6Z5LOltRb0lRJsyTNlXRw2j9PUq90lf+cpGslPSPpAUkbpzyloxnnSXoq/eyW8hwp6UlJT0v6q6RtU3oPSTdIqpE0R9Kx+XYbOI5hkq7KvZ4gaYikLqk/c1Od3y9TdrikaknVtcsWN/dUmplZJ7ROBw2SqoBjgb2BY4CqFlY5Bjgt1b0BcCJwE3Ay8JeIGAgMAGaVKdsXuDoi9gLeS/0q518RsR9wFfDrlPYo8OmI2Bu4BfhhSj8fWBwR/SPiU8BDLTg2gIHA9hHRLyL6A//2OO+IGB0RVRFR1WWTni1szszMOpN1fXriIOCeiFgOIOm+llQWEfMkvS1pb2Bb4OmIeFvSdOB6SV2BuyNiVpnir+bSZwB96mlmfO73r9L2DsCtknoDGwGvpvTDyAKXuv6926wD+8grwMclXQlMBB5oYX1mZmZrrNMjDYDaoM7rgGHA14HrASJiKvAZYAFwo6RTy5T7ILddS/0BV5TZvhK4Kl39fwvontJVkr+oD1n7b9cd1gQdA4ApwJlkx2pmZtYq1vWg4VHgSEndJfUAWuOOIncBhwP7An8BkLQz8GZEXEs2hTGoBfWfkPv9eNruSRaQQJoeSR4Avlv3QtIWBduYBwyUtIGkHYH9UvlewAYRcQfZ1EdLjsPMzGwt6/T0RERMl3QvMBt4DagGmrp673eSfp22X4+IwZImA+9FRG1KH0K2gHEVsBQoN9JQVDdJT5IFZCeltAuB2yQtAJ4AdknpFwNXp69S1gI/Ae4s0MZjZFMcNcBcYGZK3x64Ia3XAPjvFhyHmZnZWhTRnNHx9iOpR0QslbQJMBUYHhEzGyvXQH0bkH3IfjUiXmqtfnZEVVVVUV1dXelumJnZOkTSjIgo+8WDdX16AmC0pFlkH/R3tDBg2BP4G/BgZw8YzMzMmmqdnp4AiIiT868lXQ0cWJKtL1AaBFwREWt95TAingU+3uqdNDMz6wTW+aChVEScWek+mJmZdUYdYXrCzMzM1gEOGszMzKwQBw1mZmZWiIMGMzMzK8RBg5mZmRXioMHMzMwKcdBgZmZmhXS4+zRY66lZsJg+IydWuhtmndq8Ua3xHD6z9uGRBjMzMyvEQYOZmZkVsk4FDZJqJc2SNFfSbenJlkXLDpN0VT37pjVSto+kk3OvqyT9pnjP15SbJ6kmHUONpKOaWkeq5yuSRqbtCyWdm7bHSjquOXWamZm11DoVNADLI2JgRPQDVgLfzu+U1KU5lUbEAY1k6QOsCRoiojoizm5OW8ChETEQOA5ocuCR2r83IkY1s30zM7M2sa4FDXmPALtJGiJpsqSbgRpJ3SXdkK7kn5Z0aK7MjpLul/SCpB/XJUpamn5L0mVpJKNG0gkpyyjg4DRC8P3U5oRUpkeuvTmSji3Y/48B7+b6cLekGZKekTQ8l364pJmSZkt6MKXVO2qSKzdPUq+0XSVpSto+JB3HrHR+NispN1xStaTq2mWLCx6KmZnZOvrtCUkbAl8E7k9J+wH9IuJVSecARER/SXsAD0jaPZ8PWAZMlzQxIqpzVR8DDAQGAL1SnqnASODciPhyan9Irsz5wOKI6J/2bdFI9ydLEtkjuI/PpZ8eEe9I2ji1ewdZ0HYt8Jl0bFsWOD2NORc4MyIek9QDWJHfGRGjgdEA3Xr3jVZoz8zMOol1baRhY0mzgGrg78CYlP5URLyatg8CbgSIiOeB14C6oGFSRLwdEcuBO1PevIOA8RFRGxFvAA8D+zbSp8OAq+teRMS7DeSFbHqiH9AfuCp9cAOcLWk28ASwI9AX+DQwte7YIuKdRuou4jHgl5LOBjaPiA9boU4zM7N1bqRheVoPsEZ20c77+aQGypdeOZe+bqhsfVSmnkZFxMuS3gD2TAs6DwMGR8SyNJXQvbl1Jx/yUdDXPdfuKEkTgS8BT0g6LAVXZmZmLbKujTQUMRU4BSBNS+wEvJD2fV7SlmkKYCjZVXdp2RMkdZG0NfAZ4ClgCbAZ5T0AfLfuRYHpibp82wC7kI2E9ATeTQHDHmQjDACPA4dI2iWVacr0xDxgn7S9Zp2FpF0joiYiLiEbsdmjCXWamZnVqyMGDb8FukiqAW4FhkXEB2nfo2RTF7OAO0rWMwDcBcwBZgMPAT+MiH+ktA/TYsTvl5S5GNgiLZ6cDRxKwyanKZbJwMg0DXI/sKGkOcBPyaYoiIh/AsOBO1PdtzbhPPwEuELSI0BtLn1Erq/LgT83oU4zM7N6KcJr4TqrqqqqqK4ujavMzKwzkzQjIqrK7euIIw1mZmZWAevaQsgOQdKTQLeS5K9FRE0l+mNmZtYeHDQ0Q0TsX+k+mJmZtTdPT5iZmVkhDhrMzMysEAcNZmZmVoiDBjMzMyvEQYOZmZkV4qDBzMzMCnHQYGZmZoX4Pg2dWM2CxfQZObHS3TDrtOaNOqLSXTBrEo80mJmZWSEOGszMzKyQDh00SPq0pCclzZL0nKQLG8k/RNKEevZdJ2nPNumomZnZeqCjr2kYBxwfEbMldQE+0dyKIuKM1uuWmZnZ+qfiIw2Szpf0vKRJksZLOrcJxbcBFgFERG1EPJvqvFDSjZIekvSSpG/myvSQdHtq8yZJSmWmSKpK20sl/UzSbElPSNo2pe+aXk+XdJGkpSldki6TNFdSjaQTUvoQSQ9L+qOkFyWNknSKpKdSvl1Tvp0lPShpTvq9U0ofK+k3kqZJekXScSm9R8o3M9VzVErfVNLE1O+5df0oOd/DJVVLqq5dtrgJp9rMzDq7igYN6UP6WGBv4BigqolV/Ap4QdJdkr4lqXtu36eAI4DBwAWStkvpewMjgD2BjwMHlql3U+CJiBgATAXqgo4rgCsiYl9gYS7/McBAYABwGHCZpN5p3wDge0B/4GvA7hGxH3AdcFbKcxXw+4j4FHAT8Jtc3b2Bg4AvA6NS2grg6IgYBBwK/CIFP4cDCyNiQET0A+4vPbCIGB0RVRFR1WWTnmUO3czMrLxKjzQcBNwTEcsjYglwX1MKR8RFZIHGA8DJrP0hWVfvW8BkYL+U/lREzI+I1cAsoE+ZqlcCdWsfZuTyDAZuS9s3lxzH+DTa8QbwMLBv2jc9IhZFxAfAy6mvADUl9dbVd2Oqr87dEbE6jaJsm9IE/K+kOcBfge3TvhrgMEmXSDo4IjyUYGZmrabSQYNaWkFEvBwR1wCfAwZI2qpuV2nW9PuDXFot5dd1rIqIaCRPXkPHkW9vde716gbqzfc9X76unVOArYF9ImIg8AbQPSJeBPYhCx5+LumCRvptZmZWWKWDhkeBIyV1l9SDbDqhMElH1K1JAPqSfcC/l14flerdChgCTG+F/j5BNp0CcGIufSpwgqQukrYGPgM81YR6p+XqO4XsvDSkJ/BmRKySdCiwM0CaglkWEX8ALgcGNaEPZmZmDarotyciYrqke4HZwGtANdCUIfWvAb+StAz4EDglImpTHPEUMBHYCfhpRCyUtHsLuzwC+IOkc1LddX29i2yKYTbZKMEPI+IfkvYoWO/ZwPWSzgP+CXy9kfw3AfdJqiabYnk+pfcnW0+xGlgFfKehSvpv35Nq35HOzMwK0kej8BXqgNQjIpZK2oTsin14RMxsYZ0XAksj4vLW6GOu3k2A5RERkk4EToqIo1qzjfZUVVUV1dXVle6GmZmtQyTNiIiyX0xYF+7TMDrdVKk7MK6lAUMb2we4Kk2JvAecXtnumJmZtZ+KBw0RcXL+taSr+fevQfYFXipJuyIibqinzgtbrYNr1/sI2VcozczMOp2KBw2lIuLMSvfBzMzM/l2lvz1hZmZmHYSDBjMzMyvEQYOZmZkV4qDBzMzMCnHQYGZmZoU4aDAzM7NC1rmvXFr7qVmwmD4jJ1a6G2adzjzfvt06KI80mJmZWSEOGszMzKyQdS5okDRW0quSZkmaLelzle4TZA/BknRu2r5I0mFp+7r07AwkLW2ltqal330kzU3bQyRNaI36zczMmmNdXdNwXkTcLulQYDTZsyfWGRFxQW77jDao/4DWrtPMzKyl2mSkQdL5kp6XNEnS+Lor9GZ4HNg+1TlM0lW5NiZIGpK2l0q6RNIMSX+VtJ+kKZJekfSVXPm7Jd2XRjK+K+kHkp6W9ISkLVO+XSXdn+p6RNIeZY5vrKTj0vYUSVW5fb+QNFPSg5K2TmnflDQ9jZzckR6xjaRtJd2V0mdLOqDueBo5v2tGPdLruWlUYlNJE1NdcyWd0KyzbmZmVkarBw3pA/RYYG/gGKDsM7kLOhy4u0C+TYEpEbEPsAS4GPg8cDRwUS5fP+BkYD/gZ8CyiNibLDg5NeUZDZyV6joX+G0T+rspMDMiBgEPAz9O6XdGxL4RMQB4DvhGSv8N8HBKHwQ804S2yjkcWBgRAyKiH3B/aQZJwyVVS6quXba4hc2ZmVln0hbTEwcB90TEcgBJ9zWjjsskXQpsA3y6QP6VfPQBWQN8EBGrJNUAfXL5JkfEEmCJpMXAfbkyn5LUAzgAuE1SXZluTej3auDWtP0H4M603U/SxcDmQA/gLyn9s6RgJSJqgZZ+itcAl0u6BJiQHuW9logYTRYY0a1332hhe2Zm1om0xfSEGs/SqPOA3YAfAeNS2oes3d/uue1VEVH3Abga+AAgIlazdmD0QW57de51Xb4NgPciYmDu55MtOI66Po0FvhsR/YGflPS9Ocqei4h4EdiHLHj4uaQLypQ1MzNrlrYIGh4FjpTUPV25N+suJukD/wpgA0n/AcwDBkraQNKOZFMMrSoi/gW8KumrAMoMaEIVGwDHpe2Tyc4FwGbAIkldgVNy+R8EvpPa6iLpYwXbmUc2nYGkQcAuaXs7simXPwCX1+UxMzNrDa0+PRER0yXdC8wGXgOqaeawe0REGtb/IXAY8CrZVfRcYGbr9PjfnAJcI+lHQFfgFrJjKeJ9YC9JM8iOuW4h4vnAk2Tno4YsiAD4HjBa0jeAWrIA4vEC7dwBnCppFjAdeDGl9yeb2lkNrEr1mZmZtQp9NKrfipVKPSJiafqWwFRgeES01Ye8NVNVVVVUV1dXuhtmZrYOkTQjIsp+iaGt7tMwOt3wqDswzgGDmZlZx9cmQUNEnJx/Lelq4MCSbH2Bl0rSroiIG9qiT2ZmZtYy7XJHyIg4sz3aMTMzs7azzj17wszMzNZNDhrMzMysEAcNZmZmVoiDBjMzMyvEQYOZmZkV4qDBzMzMCnHQYGZmZoW0y30abN1Us2AxfUZOrHQ3zDqMeaOa9fw9s/WGRxrMzMysEAcNZmZmVkiHChokfVrSk5JmSXpO0oUpfYikA1pQ7xBJE5pYZp6kXs1t08zMrKPpaGsaxgHHR8RsSV2AT6T0IcBSYFqlOmZmZra+a/eRBknnS3pe0iRJ4yWd24Ti2wCLACKiNiKeldQH+Dbw/TQCcbCkI9OIxNOS/ipp29T2hZJulPSQpJckfTNXdw9Jt6e+3aTM5yTdlev75yXdWeaYfiBpbvoZkUs/VdIcSbMl3ZjSdpb0YEp/UNJOKX1bSXelvLPrRk7qqWOspONy7SxNv3tLmprOw1xJB5fp63BJ1ZKqa5ctbsKpNzOzzq5dRxokVQHHAnuntmcCM5pQxa+AFyRNAe4HxkXEPEn/ByyNiMtTO1sAn46IkHQG8EPgnFTHp4BPA5sCT0uq+/rA3sBewELgMbJHeT8EXC1p64j4J/B1YK1Hd0vaJ6XvDwh4UtLDwErgf4ADI+ItSVumIlcBv4+IcZJOB34DDE2/H46Io9MoSg9Je9VTR31OBv4SET9LdWxSmiEiRgOjAbr17huN1GdmZrZGe480HATcExHLI2IJcF9TCkfERUAV8ADZB+T99WTdAfiLpBrgPLJgoE5d+28Bk4H9UvpTETE/IlYDs4A+ERHAjcB/StocGAz8ucwx3RUR70fEUuBO4GDgs8DtqR0i4p2UfzBwc9q+MZUn5b8m5a2NiMUN1FGf6cDX01qP/ukcm5mZtYr2DhrU0goi4uWIuAb4HDBA0lZlsl0JXBUR/YFvAd3zVZRWmX5/kEur5aNRmBuA/wROAm6LiA9Lytd3TCrTVjkN5amvjg9JfztJAjYCiIipwGeABcCNkk4t0L6ZmVkh7R00PAocKam7pB5Ak+6UIumI9CEJ0Jfsw/09YAmwWS5rT7IPToDTSqo5KrW/FdkCyukNtRkRC8mmLH4EjC2TZSowVNImkjYFjgYeAR4Ejq8LanJTC9OAE9P2KWTnhJT/OylvF0kfa6COecA+dccDdE37dwbejIhrgTHAoIaOzczMrCnadU1DREyXdC8wG3gNqAaashrva8CvJC0ju9o+JSJqJd0H3C7pKOAs4ELgNkkLgCeAXXJ1PAVMBHYCfhoRCyXt3ki7NwFbR8SzZY5ppqSxqV6A6yLiaQBJPwMellQLPA0MA84Grpd0HlC3TgLge8BoSd8gC4a+ExGP11PHtcA9kp4iCyzeT3UMAc6TtIrs2yQeaTAzs1ajbNq+HRuUekTEUkmbkF2lD4+Ime3U9oXkFkw2odxVwNMRMaZNOlYhVVVVUV1dXelumJnZOkTSjIioKrevEvdpGC1pT7J1BuPaK2BoLkkzyK7kz2ksr5mZ2fqs3YOGiDg5/1rS1WRfb8zrC7xUknZFRNxAC0TEhc0os0/juczMzNZ/Fb8jZEScWek+mJmZWeM61LMnzMzMrHIcNJiZmVkhDhrMzMysEAcNZmZmVoiDBjMzMyvEQYOZmZkV4qDBzMzMCqn4fRqscmoWLKbPyImV7oZZxcwb1aRn5pl1eh5pMDMzs0IcNJiZmVkhnTpoUOZHkl6S9KKkyZL2akY920m6PW0PkTQhbQ9LT8hszT5Pa836zMzMiursaxrOBA4ABkTEMklfAO6VtFdErChaSUQsBI5rq06WtHVAaZqkLhFR2x7tm5lZ59XhRxoknS/peUmTJI2XdG4Tiv8XcFZELAOIiAeAacApkrpIGitprqQaSd9P7e0m6a+SZkuaKWlXSX0kzW2knztLelDSnPR7p5T+1dTGbElTU9owSfdIul/SC5J+nKtnafo9JI2M3AzUpLS7Jc2Q9Iyk4fX0Y7ikaknVtcsWN+FUmZlZZ9ehRxokVQHHAnuTHctMYEbBsh8DNo2Il0t2VQN7AQOB7SOiX8q/edp/EzAqIu6S1J0s8NqmQJNXAb+PiHGSTgd+AwwFLgD+IyIW5NoA2A/oBywDpkuaGBHVJXXuB/SLiFfT69Mj4h1JG6cyd0TE2/kCETEaGA3QrXffKNBvMzMzoOOPNBwE3BMRyyNiCXBfK9QpIIBXgI9LulLS4cC/JG1GFkjcBRARK+pGKQoYDNyctm9MfQd4DBgr6ZtAl1z+SRHxdkQsB+7M5c97KhcwAJwtaTbwBLAj0Ldg38zMzBrV0YMGNbdgRPwLeF/Sx0t2DQKejYh3gQHAFLK1D9e1pL1yXUj9+DbwI7IP+VmStsrvL81f4v26DUlDgMOAwRExAHga6N6K/TUzs06uowcNjwJHSuouqQfQ1Du1XAb8Jg3nI+kwsiv6myX1AjaIiDuA84FBKdCYL2loyt9N0iYF25oGnJi2T0l9R9KuEfFkRFwAvEUWPAB8XtKWqW9DyUYkGtITeDct6NwD+HTBfpmZmRXSodc0RMR0SfcCs4HXyNYjNGV135XAFkCNpFrgH8BREbFc0u7ADZLqAqv/Tr+/BvxO0kXAKuCrwOoCbZ0NXC/pPOCfwNdT+mWS+pKNYjyYjmUgWVBxI7AbcHOZ9Qyl7ge+LWkO8ALZFIWZmVmrUUTHXgsnqUdELE1X/FOB4RExs9L9aglJw4CqiPhuW7ZTVVUV1dWNxSJmZtaZSJoREVXl9nXokYZktKQ9yebvx3X0gMHMzGxd1eGDhog4Of9a0tXAgSXZ+gIvlaRdERE3tGXfmisixgJjK9wNMzOztXT4oKFURJxZ6T6YmZmtj9a7oMHMzNrHqlWrmD9/PitWFL7rvq1Dunfvzg477EDXrl0Ll3HQYGZmzTJ//nw222wz+vTpg9Sat7GxthYRvP3228yfP59ddtmlcLmOfp8GMzOrkBUrVrDVVls5YOiAJLHVVls1eZTIQYOZmTWbA4aOqzl/OwcNZmZmVojXNJiZWavoM3Jiq9Y3b1SxJwPcddddHHPMMTz33HPsscceAEyZMoXLL7+cCRMmrMk3bNgwvvzlL3PccccxZMgQFi1aRPfu3dloo4249tprGThwIACLFy/mrLPO4rHHsrv3H3jggVx55ZX07NkTgBdffJERI0bw4osv0rVrV/r378+VV17Jtttu2+xjfeeddzjhhBOYN28effr04Y9//CNbbLHFWnleeOEFTjjhhDWvX3nlFS666CJGjBgBwJVXXslVV13FhhtuyBFHHMGll15KTU0Nv/jFLxg7dmyz+5bnoKETq1mwuNX/kZut64p+EFnHMX78eA466CBuueUWLrzwwsLlbrrpJqqqqrjhhhs477zzmDRpEgDf+MY36NevH7///e8B+PGPf8wZZ5zBbbfdxooVKzjiiCP45S9/yZFHHgnA5MmT+ec//9mioGHUqFF87nOfY+TIkYwaNYpRo0ZxySWXrJXnE5/4BLNmzQKgtraW7bffnqOPPnpNH+655x7mzJlDt27dePPNNwHo378/8+fP5+9//zs77bRTs/tXx9MTZmbWYS1dupTHHnuMMWPGcMsttzSrjsGDB7NgwQIA/va3vzFjxgzOP//8NfsvuOACqqurefnll7n55psZPHjwmoAB4NBDD6Vfv34tOo577rmH0047DYDTTjuNu+++u8H8Dz74ILvuuis777wzANdccw0jR46kW7duAGyzzTZr8h555JHNPjelHDSYmVmHdffdd3P44Yez++67s+WWWzJzZtOfJHD//fczdOhQAJ599lkGDhxIly5d1uzv0qULAwcO5JlnnmHu3Lnss88+jda5ZMkSBg4cWPbn2Wef/bf8b7zxBr179wagd+/ea0YK6nPLLbdw0kknrXn94osv8sgjj7D//vtzyCGHMH369DX7qqqqeOSRRxrtcxGenjAzsw5r/Pjxa+b0TzzxRMaPH8+gQYPq/WZAPv2UU07h/fffp7a2dk2wERFly9aXXp/NNttszVRCa1u5ciX33nsvP//5z9ekffjhh7z77rs88cQTTJ8+neOPP55XXnkFSWyzzTYsXLiwVdrulEFD7vkUGwG7kD1KGuDiiLi9QPmLgKkR8dd69lcBp0bE2ZIuBJZGxOUt7PNQ4MWI+PcQ1cysE3r77bd56KGHmDt3LpKora1FEpdeeilbbbUV77777lr533nnHXr16rXm9U033cSAAQMYOXIkZ555JnfeeSd77bUXTz/9NKtXr2aDDbLB+NWrVzN79mw++clP8uabb/Lwww832rclS5Zw8MEHl9138803s+eee66Vtu2227Jo0SJ69+7NokWL1ppeKPXnP/+ZQYMGrbWGYocdduCYY45BEvvttx8bbLABb731FltvvTUrVqxg4403brTPRXTK6YmIODMiBgJfAl6OiIHpp0jA0CUiLqgvYEj1V0fE2a3YZYChwJ6NZcqT1CmDQjPrHG6//XZOPfVUXnvtNebNm8frr7/OLrvswqOPPkrfvn1ZuHAhzz33HACvvfYas2fPXvMNiTpdu3bl4osv5oknnuC5555jt912Y++99+biiy9ek+fiiy9m0KBB7Lbbbpx88slMmzaNiRM/WkR+//33U1NTs1a9dSMN5X5KAwaAr3zlK4wbNw6AcePGcdRRR9V73OPHj19ragJg6NChPPTQQ0A2VbFy5co1AdKLL77Y4jUXdTr0h4qk84FTgNeBt4AZzb2ilzQEODcivpxeXwVUR8RYSfOA64EvAFdJOhyYEBG3S9oXuALYFPgA+BywT74uYICkh4AdgUsj4lpJPYB7gC2ArsCPIuKe1PapwLlAAHOAa4CvAIdI+hFwbKr3amBrYBnwzYh4XtJY4B1gb2AmcE7JcQ4HhgN0+djWzTlVZmZltfc3U8aPH8/IkSPXSjv22GO5+eabOfjgg/nDH/7A17/+dVasWEHXrl257rrr1nxtMm/jjTfmnHPO4fLLL2fMmDGMGTOGs846i912242IYPDgwYwZM2ZN3gkTJjBixAhGjBhB165d+dSnPsUVV1zRomMZOXIkxx9/PGPGjGGnnXbitttuA2DhwoWcccYZ/OlPfwJg2bJlTJo0id/97ndrlT/99NM5/fTT6devHxtttBHjxo1bM50yefJkjjiidf42iohWqai9pSmA64DBZMHPTOB3TQkaJPUh+/DvVyBo+G1EXJr2jQUmAPcCzwMnRMR0SR8j+wA/qK6uND1xNPBpssDiaWB/4E1gk4j4l6RewBNkj/DeE7gTODAi3pK0ZUS8U9dm3WiIpAeBb0fES5L2B34eEZ9N+XoBR0VEbUPH36133+h92q+Lni6z9YK/ctl6nnvuOT75yU9WuhvWgA8++IBDDjmERx99lA03/PdxgnJ/Q0kzIqKqXH0deaThIOCeiFgOIOm+Nm7v1jJpnwAWRcR0gIj4V+pLab66fi6XNBnYD5gI/K+kzwCrge2BbYHPArdHxFupzndKK0ujFAcAt+Xa6pbLcltjAYOZma3//v73vzNq1KiyAUNzdOSgobVveP4ha6/x6F6y//16+lBkqKY0T5BNq2wN7BMRq9JoRveCdW4AvJfWZZRTrq9mZtbJ9O3bl759+7ZafR15IeSjwJGSuqcr75aOOb4G7Cmpm6SeZGsTGvM8sF1a14CkzepZfHhU6udWwBBgOtATeDMFDIcCO6e8DwLHp7xI2jKlLwE2gzUjGq9K+mrKI0kDmn7IZmYt01GnuK15f7sOO9KQ1hDcC8wm+8CvBha3oL7XJf2RbOHhS2RrDxors1LSCcCVkjYGlgOHlcn6FNl0xE7ATyNioaSbgPskVQOzyAIQIuIZST8DHpZUm/oxDLgFuFbS2cBxZCMV16SFkV3T/tlNOeb+2/ek2vO7ZtZM3bt35+233/bjsTugiODtt9+me/fSQfWGddiFkJDN7UfEUkmbAFOB4RHR9NuBdVJVVVVRXV1d6W6YWQe1atUq5s+fz4oVKyrdFWuG7t27s8MOO9C1a9e10tfXhZAAoyXtSbYWYJwDBjOz9tO1a1d22WWXSnfD2lGHDhoi4uT869ydHvP6kk035F0RETe0Zd/MzMzWNx06aCgVEWdWug9mZmbrq4787QkzMzNrRx16IaS1jKQlfPSwLmsfvchueW7tx+e8/fmct7/WPOc7R0TZ5wysV9MT1mQv1LdC1tqGpGqf8/blc97+fM7bX3udc09PmJmZWSEOGszMzKwQBw2d2+hKd6AT8jlvfz7n7c/nvP21yzn3QkgzMzMrxCMNZmZmVoiDBjMzMyvEQUMnJelwSS9I+pukkZXuz/pI0o6SJkt6TtIzkr6X0i+UtEDSrPTzpUr3dX0iaZ6kmnRuq1PalpImSXop/d6i0v1cX0j6RO69PEvSvySN8Pu8dUm6XtKbkubm0up9X0v67/T/+wuS/qPV+uE1DZ2PpC7Ai8DngfnAdOCkiHi2oh1bz0jqDfSOiJmSNgNmAEOB44GlEXF5Jfu3vpI0D6iKiLdyaZcC70TEqBQkbxER/1WpPq6v0v8tC4D9ga/j93mrkfQZYCnw+4jol9LKvq/TgxzHA/sB2wF/BXaPiNqW9sMjDZ3TfsDfIuKViFgJ3AIcVeE+rXciYlHdk1cjYgnwHLB9ZXvVaR0FjEvb48iCN2t9nwNejojXKt2R9U1ETAXeKUmu7319FHBLRHwQEa8CfyP7f7/FHDR0TtsDr+dez8cfZm1KUh9gb+DJlPRdSXPSkKOHyltXAA9ImiFpeErbNiIWQRbMAdtUrHfrtxPJrnDr+H3etup7X7fZ//EOGjonlUnzPFUbkdQDuAMYERH/Aq4BdgUGAouAX1Sud+ulAyNiEPBF4Mw0rGttTNJGwFeA21KS3+eV02b/xzto6JzmAzvmXu8ALKxQX9ZrkrqSBQw3RcSdABHxRkTURsRq4FpaadjQMhGxMP1+E7iL7Py+kdaY1K01ebNyPVxvfRGYGRFvgN/n7aS+93Wb/R/voKFzmg70lbRLujo4Ebi3wn1a70gSMAZ4LiJ+mUvvnct2NDC3tKw1j6RN06JTJG0KfIHs/N4LnJaynQbcU5kertdOIjc14fd5u6jvfX0vcKKkbpJ2AfoCT7VGg/72RCeVvv70a6ALcH1E/KyyPVr/SDoIeASoAVan5P+P7D/XgWTDhfOAb9XNS1rLSPo42egCZE/xvTkifiZpK+CPwE7A34GvRkTpojJrJkmbkM2hfzwiFqe0G/H7vNVIGg8MIXsE9hvAj4G7qed9Lel/gNOBD8mmRv/cKv1w0GBmZmZFeHrCzMzMCnHQYGZmZoU4aDAzM7NCHDSYmZlZIQ4azMzMrBAHDWZtTFJtesrfXEn3Sdq8kfwXSjq3kTxD00Np6l5fJOmwVujrWEnHtbSeJrY5In1lb50haY/0N3ta0q4l++ZJeqQkbVbd0wclVUn6TSv0oU/+iYYl+67L//3bmqRtJd0s6ZV0e+7HJR3dXu3busNBg1nbWx4RA9OT6d4BzmyFOocCaz40IuKCiPhrK9TbrtJTEUcA61TQQHZ+74mIvSPi5TL7N5O0I4CkT+Z3RER1RJxdtKF0DpokIs5or6fSppuU3Q1MjYiPR8Q+ZDeE26GN292wLeu35nHQYNa+Hic9OEbSrpLuT1duj0jaozSzpG9Kmi5ptqQ7JG0i6QCye/xflq5wd60bIZD0RUl/zJUfIum+tP2FdIU4U9Jt6ZkY9UpX1P+bylRLGiTpL5JelvTtXP1TJd0l6VlJ/ydpg7TvJEk1aYTlkly9S9PIyJPA/5A9uneypMlp/zWpvWck/aSkPz9J/a+pO1+Seki6IaXNkXRs0eOVNFDSE6ncXZK2SDc+GwGcUdenMv4InJC2S++EOETShEb6lj8HgyX9IJ2nuZJG5NrZUNK4VPb2uhEZSVMkVRU4z5ek99dfJe2Xyr0i6SspTxdJl6X32BxJ3ypzrJ8FVkbE/9UlRMRrEXFlQ3Wk8zAl9ft5STelAARJ+0h6OPXtL/roVshT0nvuYeB7ko6U9KSyEZ+/Stq2nr+HtZeI8I9//NOGP8DS9LsL2cN8Dk+vHwT6pu39gYfS9oXAuWl7q1w9FwNnpe2xwHG5fWOB48jugvh3YNOUfg3wn2R3kZuaS/8v4IIyfV1TL9ld/L6Ttn8FzAE2A7YG3kzpQ4AVwMfT8U1K/dgu9WPr1KeHgKGpTADH59qcB/TKvd4yd76mAJ/K5as7/v8XuC5tXwL8Old+iyYc7xzgkLR9UV09+b9BmTLzgN2Baen102SjPnNz52RCfX0rPQfAPmR3Dd0U6AE8Q/ZE1D4p34Ep3/V89L6YAlQVOM9fTNt3AQ8AXYEBwKyUPhz4UdruBlQDu5Qc79nArxp4f5etI52HxWQjEhuQBcwHpT5MA7ZOZU4guytt3XH9tuRvWXcTwjOAX1T633Nn//Hwj1nb21jSLLIPgRnApHTVewBwW7r4guw/3FL9JF0MbE72gfKXhhqKiA8l3Q8cKel24Ajgh8AhZB9sj6X2NiL7T7wxdc8kqQF6RMQSYImkFfpobcZTEfEKrLnV7UHAKmBKRPwzpd8EfIZsmLuW7CFe9Tle2SOtNwR6p37PSfvuTL9nAMek7cPIhsvrzsG7kr7c2PFK6glsHhEPp6RxfPSExsa8A7wr6UTgOWBZPfn+rW9pM38ODgLuioj3U7/uBA4mO/evR8RjKd8fyD7AL8/Vvy/1n+eVwP0pXw3wQUSsklRD9l6E7Nkcn9JH61h6kj2n4NX6DlzS1anPKyNi3wbqWEn23pifys1K7b4H9CP7dwBZcJi/vfStue0dgFvTSMRGDfXL2oeDBrO2tzwiBqYPqQlkaxrGAu9FxMBGyo4lu3KcLWkY2dVbY25NbbwDTI+IJWlYeFJEnNTEvn+Qfq/Obde9rvv/o/Re9EH5R/PWWRERteV2KHu4zrnAvunDfyzQvUx/anPtq0wfmnu8TXErcDUwrIE85foGa5+Dhs5VuXNbWn99VkW6RCf394uI1fpovYDIRm8aCkafAY5d04GIMyX1IhtRqLcOSUNY+z1T9zcT8ExEDK6nvfdz21cCv4yIe1N9FzbQT2sHXtNg1k4ie5DP2WQfisuBVyV9FbLFZpIGlCm2GbBI2SO2T8mlL0n7ypkCDAK+yUdXbU8AB0raLbW3iaTdW3ZEa+yn7ImpG5ANNT8KPAkcIqmXsoV+JwEP11M+fywfI/vQWJzmr79YoP0HgO/WvZC0BQWON/093pV0cEr6WgN9LOcu4FIaHv0p17dSU4GhqY+bkj0Rsu7bGTtJqvtwPYns3OY15TyX8xfgO+n9haTdUx/yHgK6S/pOLi2/cLVIHXkvAFvXHZekrpL2qidvT2BB2j6tnjzWjhw0mLWjiHgamE02ZH0K8A1Js8mu5o4qU+R8sg+GScDzufRbgPNU5iuB6Qp2AtkH7oSU9k+yK+LxkuaQfaj+28LLZnocGEX26ONXyYbaFwH/DUwmO96ZEVHf46hHA3+WNDkiZpOtEXiGbA7/sXrK5F0MbJEWAs4GDm3C8Z5GtqB0DtkTGS8q0B4AEbEkIi6JiJVN6VuZemaSjSg9Rfa3vi69TyCb+jgt9W9LsjUq+bJNOc/lXAc8C8xU9vXO31EyAp1GK4aSBSevSnqKbCrnv4rWUVLfSrJ1L5ekczKLbKqunAvJpvAeAd5qwnFZG/FTLs2s2dKQ8bkR8eUKd8XM2oFHGszMzKwQjzSYmZlZIR5pMDMzs0IcNJiZmVkhDhrMzMysEAcNZmZmVoiDBjMzMyvk/wfTLBov3jPE1gAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.9436475257002398, pvalue=7.35988825126165e-49)\n", "Slope and P-value = PearsonRResult(statistic=0.6771356263022256, pvalue=1.0360531262327596e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.93491952426022, pvalue=6.9085848507503636e-46)\n", "Slope and P-value = PearsonRResult(statistic=-0.5646343377216486, pvalue=9.434048665154043e-10)\n", "Slope and P-value = PearsonRResult(statistic=-0.021327896561599387, pvalue=0.8331826617552404)\n", "Slope and P-value = PearsonRResult(statistic=0.46608626781129703, pvalue=1.0237415119988392e-06)\n", "Slope and P-value = PearsonRResult(statistic=0.9161757884173174, pvalue=1.0637745289098108e-40)\n", "Slope and P-value = PearsonRResult(statistic=-0.014714918832850053, pvalue=0.884468480534651)\n", "Slope and P-value = PearsonRResult(statistic=-0.9536949358775136, pvalue=6.210914411877648e-53)\n", "Slope and P-value = PearsonRResult(statistic=-0.7025930033509815, pvalue=3.7394365775684335e-16)\n" ] } ], "source": [ "print(\"Median:\",poultry.B.median())\n", "\n", "poultry.loc[poultry['B'] < poultry.B.median(), 'new_B'] = 0\n", "poultry.loc[poultry['B'] >= poultry.B.median(), 'new_B'] = 1\n", "print(\"Total sample count:\", poultry.B.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_B.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_B','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_B'],axis='columns'),sample.new_B,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"B_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_B']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"B in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_B','SampleType'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"B_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (32) Ca" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 3059.65\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 818\n", "0.0 817\n", "Name: new_Ca, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 588\n", "0.0 110\n", "Name: new_Ca, dtype: int64\n" ] }, { "data": { 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.25768315460551827, pvalue=0.009645076642596947)\n", "Slope and P-value = PearsonRResult(statistic=-0.5004984977061888, pvalue=0.009212550403984113)\n", "Slope and P-value = PearsonRResult(statistic=-0.3775576208095896, pvalue=0.00010774417276196087)\n", "Slope and P-value = PearsonRResult(statistic=-0.7387199606824388, pvalue=1.748446610562448e-18)\n", "Slope and P-value = PearsonRResult(statistic=-0.5762571478324066, pvalue=3.4412980950244055e-09)\n", "Slope and P-value = PearsonRResult(statistic=-0.7486246241060062, pvalue=3.4320889894156137e-19)\n", "Slope and P-value = PearsonRResult(statistic=-0.659815882469193, pvalue=8.265241081600431e-14)\n", "Slope and P-value = PearsonRResult(statistic=-0.25319010976821776, pvalue=0.10570045145268993)\n", "Slope and P-value = PearsonRResult(statistic=-0.20762087956272768, pvalue=0.2798206351861446)\n", "Slope and P-value = PearsonRResult(statistic=0.33885135213278894, pvalue=0.0005636592667966793)\n", "0.0 575\n", "1.0 120\n", "Name: new_Ca, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.8996710620179893, pvalue=4.7341832287201495e-37)\n", "Slope and P-value = PearsonRResult(statistic=0.4922940744265709, pvalue=3.598189637992095e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.47909612959420417, pvalue=4.594122758594951e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.9608415900468013, pvalue=1.9967204098783016e-56)\n", "Slope and P-value = PearsonRResult(statistic=-0.17992592908096947, pvalue=0.07324722382582553)\n", "Slope and P-value = PearsonRResult(statistic=0.9482920118002931, pvalue=1.2166473149320647e-50)\n", "Slope and P-value = PearsonRResult(statistic=0.950310929515502, pvalue=1.8145136393498186e-51)\n", "Slope and P-value = PearsonRResult(statistic=-0.7372093098592769, pvalue=2.2268773829289632e-18)\n", "Slope and P-value = PearsonRResult(statistic=0.774525157124613, pvalue=6.171872199564401e-13)\n", "Slope and P-value = PearsonRResult(statistic=-0.8271653462032673, pvalue=2.8619987276335457e-26)\n" ] } ], "source": [ "print(\"Median:\",poultry.Ca.median())\n", "\n", "poultry.loc[poultry['Ca'] < poultry.Ca.median(), 'new_Ca'] = 0\n", "poultry.loc[poultry['Ca'] >= poultry.Ca.median(), 'new_Ca'] = 1 \n", "print(\"Total sample count:\", poultry.Ca.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_Ca.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_Ca','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_Ca'],axis='columns'),sample.new_Ca,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"Ca_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_Ca']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"Ca in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Ca','SampleType'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Ca_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (33) Cd" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 878)\n", "Soil (695, 878) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 534\n", "0.0 164\n", "Name: new_Cd, dtype: int64\n" ] }, { "data": { "image/png": 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GRMkbYRrznRXDlH0hWAUwwomImZmZtaQGk5GIOK64TNLlZLew5vUA/l1UdnFEXNv88MzMzGxV16xv84yIU1o6EDMzM2ub/KA8MzMzKysnI2ZmZlZWTkbMzMysrJyMmJmZWVk5GTEzM7OycjJiZmZmZdWsW3vN6lP71iwqB99b7jDMzBab7kdUrNDcM2JmZmZl5WTEzMzMysrJSBNJGi5prqTOubKLJYWkrs1ob2NJtzUzlqvSc4Pq3SZpeiE2SXOacywzM7PlxXNGmuc/wGHA9ZJWA/YF3mpOQxHxNtC3mfueVKpcUru6tpmZma1o2mTPiKQhkl6UNEbSSEmDmtjESKBfWu4NjAe+SG1XSpqWO9YgSeek5a0kPShpiqRJkrbM15e0naRnJE2WNFVSj7T9RUkjUtltkjqm+uMkVaflOZLOlfQ00Cu/rY5r0FvS6Nz6ZZL6p+Xpkn4v6UlJNZJ2lnS/pFck/biJ18rMzKxebS4ZSW/QRwI7AUcAdb5h1+PfwAaS1gWOBW5q5H43AJdHxI7AHsDMou0/JnvScVWKa0Yq/yowLCJ2AD4B/qdE22sB0yJi94h4vCknU4c3I6IX8BgwnKz35uvAuaUqSxqQEpeahXNntcDhzcysrWhzyQiwF3BXRMyLiNnAPc1s5w7gGGB3sjfseqU5JptExCiAiJgfEXOLqj0J/ErS/wJfiYh5qfzNiBiflq9P51BsIXB700+jTnen37XA0xExOyLeA+ZLWqe4ckQMi4jqiKhu17FLC4ZhZmaruraYjKiF2rkJ+C0wJiIW5cq/YOnrWtHY40bEjcChwDzgfkn7FTYVVy2x+/yIWNiYwBuIs2BB+r0ot1xY91wjMzNrMW0xGXkcOERShaROQLO+CSci3gDOBP5atOkdYENJ60taEzg41f8EmCGpD4CkNQtzPwokbQG8GhGXkPVM7JA2dZfUKy0fm85hWb0ObJvi6ALs3wJtmpmZNVmbS0YiYgLZG/0UsqGWGqBZkxwi4u8R8UpR2edk8yqeBkYDL+Y2nwCcJmkq8ATw/4qa7AdMkzQZ2Aa4LpW/AJyY9lsPuKI58RbF+SZwCzCVbC7Ls8vappmZWXMoolSP/6pNUqeImJN6Jh4FBkTEpHLHVYqkSmB0RGxf7lgaq7q6OmpqasodhpmZrUAkTYyIkjeNtNWx/2HpC8EqgBEraiJiZmbWFrTJZCQijsuvS7oc2LOoWg+yW3jzLo6Ia5dnbMUiYjqw0vSKmJmZNVWbTEaKRcQp5Y7BzMysrWpzE1jNzMxsxeJkxMzMzMrKyYiZmZmVlZMRMzMzKysnI2ZmZlZWTkbMzMysrJyMmJmZWVn5e0asxdW+NYvKwfeWOwyzNmP60GY979NsheGeETMzMysrJyOrGEmbSrpL0r8lvSLpYklrSKqS9O1cvXMkDSpnrGZmZuBkZJUiScAdwJ0R0QPYGugE/A6oAr5d995NPla7lmrLzMzaNicjq5b9gPmFh/lFxELgdOAk4I9AP0mTJfVL9beVNE7Sq5JOKzQi6buSnkl1/15IPCTNkXSupKeBXq16ZmZmtspyMrJq2Q6YmC+IiE+A6cB5wM0RURURN6fN2wD/BewGnC2pvaSvAf2APSOiClgIHJ/qrwVMi4jdI+Lx/HEkDZBUI6lm4dxZy+fszMxsleS7aVYtAqIJ5fdGxAJggaR3gY2A/YFdgAnZqA8dgHdT/YXA7aUOHBHDgGEAa3brUepYZmZmJTkZWbU8BxyZL5C0NrAZWSJRbEFueSHZ60HAiIj4ZYn689PQj5mZWYvxMM2q5SGgo6TvweJJpn8GhgPvAJ0b2UZfSRumNtaT9JXlE66ZmZmTkVVKRARwOHCUpH8DLwPzgV8BY8kmrOYnsJZq43ng18ADkqYCY4Buyz14MzNrszxMs4qJiDeBQ0psWgDsWs9+2+eWbwZuLlGnU0vEaGZmludkxFpcz026UOOvpzYzs0byMI2ZmZmVlZMRMzMzKysnI2ZmZlZWTkbMzMysrJyMmJmZWVk5GTEzM7OycjJiZmZmZeVkxMzMzMrKyYiZmZmVlZMRMzMzKyt/Hby1uNq3ZlE5+N5yh2H2JdP9mAKzFZJ7RszMzKysnIyYmZlZWbWJZETScEmvSZqcfk5rhWNOl9Q1LT+RfveWNLoF2q6S9O1m7LexpNuW9fhmZmYtqS3NGfl5RJTljTgi9mjhJquAauCfjd1B0uoR8TbQt4VjMTMzWyYrTc+IpCGSXpQ0RtJISYOWsb2zJE2QNE3SMElK5VtKuk/SREmPSdomlW8kaZSkKelnj1R+Z6r7nKQBdRxrTm517dTO85L+Jmm1VOcKSTWpnd/k9t1V0hPpmM9I6gKcC/RLvTz9JK0l6Zp0Ps9KOizt21/SrZLuAR6QVClpWm7bZbnjjJbUuxCvpPPTeT0oaTdJ4yS9KunQOs5xQIq/ZuHcWc37o5iZWZu0UiQjkqqBI4GdgCPIegWa6k+5YZqewGURsWtEbA90AA5O9YYBp0bELsAg4K+p/BLgkYjYEdgZeC6V/yDVrQZOk7R+A3HsBpwB9AS2TOcDcGZEVAM7APtI2kHSGsDNwE/TcQ8APgXOAm6OiKqIuBk4E3g4InYF9k3nulZqtxdwYkTs14RrtRYwLp3XbOA84JvA4WSJ0JdExLCIqI6I6nYduzThUGZm1tatLMM0ewF3RcQ8gPRJv6mWGqaRdKSkXwAdgfWA5ySNBfYAbk0dJQBrpt/7Ad8DiIiFQOHj/2mSDk/LmwE9gA/qieOZiHg1xTAyndttwNGpZ2V1oBuwLRDAzIiYkI77SdqvuM0DgUNzvUUVQPe0PCYiPqwnnlI+A+5Ly7XAgoj4XFItUNnEtszMzOq1siQjX3r3XabGpAqyHo/qiHhT0jlkb+CrAR9HRFUj2+lN1lvRKyLmShqX2qlPFK9L2pysF2bXiPhI0vDUjkrULxkKcGREvFQU3+5kPSmlfMHSPWP5uD+PiMJxFwELACJikaSV5TVjZmYriZVimAZ4HDhEUoWkTsCyfnNR4Y33/dReX1jc8/CapKMAlNkx1X0IODmVt5O0NtAF+CglItsAX2/EsXeTtHmaK9IvndvaZEnDLEkbAQelui8CG0vaNR23c0oGZgOdc23eD5yam/eyUyPimA5USVpN0mZkw0dmZmatbqVIRtIwxd3AFOAOoIYlwyTNae9j4EqyIYg7gQm5zccDP5Q0hWxeyGGp/KfAvmmoYiKwHdlQxuqSpgK/BZ5qxOGfBIYC04DXgFERMQV4Nh3vGmB8ivMzsoTl0hTPGLJEaiywbWECazp2e2BqmqD620bEMT4dvxa4AJjUiH3MzMxanJb0xq/YJHWKiDmSOgKPAgMiwm+gK6Dq6uqoqakpdxhmZrYCkTQx3ajxJSvT+P8wSduS9QyMcCJiZma2alhpkpGIOC6/LulyYM+iaj2AfxeVXRwR1y7P2MzMzKz5VppkpFhEnFLuGMzMzGzZrRQTWM3MzGzV5WTEzMzMysrJiJmZmZWVkxEzMzMrKycjZmZmVlZORszMzKysnIyYmZlZWa203zNiK67at2ZROfjecodhbcT0ocv63EwzKzf3jJiZmVlZORkxMzOzsnIysgwkrS7pfUl/KCqfLqlrUVmlpGmtG2HzSNpY0m3ljsPMzNoGJyPL5kDgJeBoSVpeB5HUqnN7IuLtiOjbmsc0M7O2q00nI5KGSHpR0hhJIyUNamITxwIXA28AXy/RfgdJ90n6UVH5FpKelbSrpC1TnYmSHpO0TaozXNKFksYC56f1KySNlfSqpH0kXSPpBUnDc21fIalG0nOSfpMrny7pN5ImSarNHWcfSZPTz7OSOud7cdLyY2m/SZL2qONaDkjHrVk4d1YTL6OZmbVlbTYZkVQNHAnsBBwBVDdx/w7A/sBoYCRZYpLXCbgHuDEirszt91XgduD7ETEBGAacGhG7AIOAv+ba2Bo4ICLOSOvrAvsBp6e2LwK2A3pKqkp1zoyIamAHYB9JO+Taez8idgauSMci/T4lIqqAvYF5RefxLvDNtF8/4JJS1yMihkVEdURUt+vYpVQVMzOzktpsMgLsBdwVEfMiYjbZm3tTHAyMjYi5ZMnF4ZLa5bbfBVwbEdflyjZI5d+NiMmSOgF7ALdKmgz8HeiWq39rRCzMrd8TEQHUAu9ERG1ELAKeAypTnaMlTQKeJUtUts3tf0f6PTFXfzxwoaTTgHUi4oui82wPXCmpFri1qD0zM7Nl1paTkWWd43EscICk6WRv7usD++a2jwcOKppLMgt4E9gzra8GfBwRVbmfr+Xqf1p0zAXp96LccmF9dUmbk/V07B8ROwD3AhUl9l9I+o6ZiBgKnAR0AJ4qDN/knA68A+xI1nu0RolrYWZm1mxtORl5HDhEUkXqoWj0NydJWpusZ6V7RFRGRCVwCksP1ZwFfMDSwy6fAX2A70k6LiI+AV6TdFRqV5J2XIZzWpssgZklaSPgoEacy5aph+V8oAYoTka6ADNTD8wJQLviNszMzJZFm/0G1oiYIOluYArwOtkbcWNnXh4BPBwR+d6Ju4A/SlozVzYQuEbSH0lJSUR8KulgYIykT4HjgSsk/ZpsSOSmFFNzzmmKpGfJhm1eJeudachASfuS9ZY8D/yLpYeK/grcnhKmsXy5t+ZLem7ShRp/K6aZmTWSsikIbZOkThExR1JH4FFgQERMKndcK7vq6uqoqakpdxhmZrYCkTQx3WDxJW22ZyQZJmlbsnkVI5yImJmZtb42nYxExHH5dUmXs2RyaUEP4N9FZRdHxLXLMzYzM7O2ok0nI8Ui4pRyx2BmZtbWtOW7aczMzGwF4GTEzMzMysrJiJmZmZWVkxEzMzMrKycjZmZmVlZORszMzKysfGuvtbjat2ZROfjecodhq7jpfuSA2SrDPSNmZmZWVk5GzMzMrKxaJRmRtFDSZEnTJN2aHkzX2H37S7qsjm1PNLBvpaTjcuvVki5pfOSL9/uBpFpJU9M5HJaLbeOmtlfPcfqkZ+WYmZm1Ga3VMzIvIqoiYnvgM+DH+Y2S2jWn0YjYo4EqlcDiZCQiaiLitKYcQ9KmwJnAXhGxA/B1YGra3B8omYw085z6AE5GzMysTSnHMM1jwFaSeksaK+lGoFZShaRrUw/Es5L2ze2zmaT7JL0k6exCoaQ56bck/Sn1WtRK6peqDAX2Tr0yp6djjk77dModb6qkI+uId0NgNjAHICLmRMRrkvoC1cANqf0OkqZLOkvS48BRkg6U9KSkSalHqFM69nRJ50t6Jv1sJWkP4FDgT6m9LSVVSXoqxTdK0rpp/60kPShpSmp7y3quAZJ+kcqmSBpaTxuLr0+qc5mk/ml5qKTnUywXNOcPb2ZmVkqr3k0jaXXgIOC+VLQbsH16cz8DICJ6StoGeEDS1vl6wFxggqR7I6Im1/QRQBWwI9A11XkUGAwMioiD0/F75/YZAsyKiJ5p27p1hD0FeAd4TdJDwB0RcU9E3CbpJ6n9mtQGwPyI2EtSV+AO4ICI+FTS/wI/A85N7X4SEbtJ+h7wl4g4WNLdwOiIuC21NxU4NSIekXQucDYwELgBGBoRoyRVkCWVdV2DKrIel90jYq6k9dLxS7WxWakLkPY5HNgmIkLSOiXqDAAGALRbe4M6LqWZmdmXtVbPSAdJk4Ea4A3g6lT+TES8lpb3Av4BEBEvAq8DhWRkTER8EBHzyN7g9ypqfy9gZEQsjIh3gEeAXRuI6QDg8sJKRHxUqlJELAS+BfQFXgYuknROPe3enH5/nWzIZXw69xOBr+Tqjcz97lXciKQuwDoR8UgqGgF8Q1JnYJOIGJXimx8Rc6n7GhwAXJvqEBEf1tNGXT4B5gNXSTqCLClcSkQMi4jqiKhu17FLPU2ZmZktrbV6RuZFRFW+IPUifJovqmf/aGC9vn3rohLtlD54RADPAM9IGgNcC5xTR/XCOYksiTq2rmbrWG5IXedaX3ljr9cXLJ2gVgBExBeSdgP2B44BfgLs16hozczMGrAi3dr7KHA8QBqe6Q68lLZ9U9J6kjqQDTmML7FvP0ntJG0AfIMseZgNdK7jeA+QvamSjllymEbSxpJ2zhVVkfXa0ED7TwF7StoqtdMxN+wE0C/3+8ni9iJiFvCRpL3TthOARyLiE2CGpD6p3TWV3Z1U1zV4APhBqoOk9epp43Vg27TehSz5IM116RIR/yQbJqqq45zNzMyabEX6Bta/An+TVEv2Cb1/RCxIPSiPkw3hbAXcWDRfBGAU2VDHFLJegF9ExP9J+gD4QtIUYDjwbG6f84DLJU0DFgK/IRsCKtYeuEDZLbzzgfdYcjfQ8BTzPIqGWiLivTT5c6SkNVPxr8mGegDWlPQ0WUJY6D25CbhS0mlkw0InpvY7Aq8C30/1TgD+nuaRfA4cVdc1AO6TVAXUSPoM+Cfwq1JtRMSrkm4hu1vo37nr1Rm4K80tEXB6ietkZmbWLMpGIKw1SZoOVEfE++WOZXmorq6OmprifNHMzNoySRMjorrUthVpmMbMzMzaoBVpmKbs0rDJmkXFJ0REbUseJyIqW7I9MzOzlZmTkZyI2L3cMZiZmbU1HqYxMzOzsnIyYmZmZmXlZMTMzMzKysmImZmZlZWTETMzMysrJyNmZmZWVk5GzMzMrKz8PSPW4mrfmkXl4HvLHYatIqYP/U65QzCz5cw9I2ZmZlZWTkbMzMysrMqWjEhaKGmypGmSbpXUsQn79pd0WR3bnmhg30pJx+XWqyVd0vjIF+83XVLXpu5X1MbAxpy3pDnLcpxcO0+k35WSpqXl3pJGt0T7ZmZmzVHOnpF5EVEVEdsDnwE/zm+U1K45jUbEHg1UqQQWJyMRURMRpzXnWC1gINDoJGxZNeLamJmZtboVZZjmMWCr9Cl9rKQbgVpJFZKulVQr6VlJ++b22UzSfZJeknR2obDQi6DMn1LPS62kfqnKUGDv1Ctzer5nQFKn3PGmSjqyKSchaTdJT6RYn5D01VTeTtIFuXZPlXQasDEwVtLYVO/YVGeapPOL2v6zpEmSHpK0QSr7kaQJkqZIur3QyyJpI0mjUvkUSXvkr0098Z8jaVBufVrqRVlL0r2prWm5a5nfd4CkGkk1C+fOasplMzOzNq7sd9NIWh04CLgvFe0GbB8Rr0k6AyAiekraBnhA0tb5esBcYIKkeyOiJtf0EUAVsCPQNdV5FBgMDIqIg9Pxe+f2GQLMioieadu6TTydF4FvRMQXkg4Afg8cCQwANgd2StvWi4gPJf0M2Dci3pe0MXA+sAvwUTrXPhFxJ7AWMCkizpB0FnA28BPgjoi4MsV6HvBD4FLgEuCRiDg89TB1auJ5FPsW8HZEfCcdq0txhYgYBgwDWLNbj1jG45mZWRtSzp6RDpImAzXAG8DVqfyZiHgtLe8F/AMgIl4EXgcKyciYiPggIuYBd6S6eXsBIyNiYUS8AzwC7NpATAcAlxdWIuKjJp5TF+DWNB/jImC7XLt/i4gvUrsflth3V2BcRLyX6t0AfCNtWwTcnJavZ8m5bi/pMUm1wPG54+0HXJGOtTAilrWrohY4QNL5kvZugfbMzMwWK2fPyLyIqMoXSAL4NF9Uz/7Fn76L1+vbty4q0U5T/BYYm3okKoFxTWi3KfEW2hoO9ImIKZL6A72b0EYpX7B0gloBEBEvS9oF+DbwB0kPRMS5y3gsMzMzYMWZM1KXR8k+8ZOGZ7oDL6Vt35S0nqQOQB9gfIl9+6X5GhuQ9TI8A8wGOtdxvAfIhj9Ix2zqME0X4K203L+o3R+nISkkrZfK87E8DewjqWsaWjmWrDcHsr9T37R8HPB4Wu4MzJTUnnSdkoeAk9Ox2klau5HxTwd2TvvtTDa0RBpCmhsR1wMXFOqYmZm1hBU9Gfkr0C4NQ9wM9I+IBWnb42RDOJOB24vmiwCMAqYCU4CHgV9ExP+lsi/SZMzTi/Y5D1g3TdKcAuxL/aZKmpF+LgT+SNZzMB7I3w10FdlQ1NTUbuFunmHAvySNjYiZwC+BsSnmSRFxV6r3KbCdpIlkQzCFXokhZEnMGLL5KgU/BfZN120iS4ZvGnI7sF4aPjsZeDmV9wSeSeVnkl0nMzOzFqEIzzW0llVdXR01NcW5oZmZtWWSJkZEdaltK3rPiJmZma3iyn5r74pO0tPAmkXFJ0REbTniMTMzW9U4GWlAROxe7hjMzMxWZR6mMTMzs7JyMmJmZmZl5WTEzMzMysrJiJmZmZWVkxEzMzMrKycjZmZmVlZORszMzKys/D0j1uJq35pF5eB7yx2GrQKmD/1OuUMws1bgnhEzMzMrKycjZmZmVlZORspI0o6SJufWj5U0V1L7tN5T0tR69q+WdEkLxjNOUnVani6pa0u1bWZmVhcnI+VVC3xFUue0vgfwIrBTbn18XTtHRE1EnLZ8QzQzM1u+nIy0AElDJL0oaYykkZIGNWa/iFgETAAKD+PbBbicLAkh/X5C0lqSrpE0QdKzkg5Lx+0taXRa3kfS5PTzrKTOkrpJejSVTZO0d6p7oKQnJU2SdKukTg2c352SJkp6TtKAOuoMkFQjqWbh3FmNOX0zMzPAycgyS8MaR5L1ZhwBVDexiSeAPSStBSwCxrF0MjIeOBN4OCJ2BfYF/pTq5w0CTomIKmBvYB5wHHB/KtsRmJyGXn4NHBAROwM1wM8aiPEHEbFLOrfTJK1fXCEihkVEdURUt+vYpSnnb2ZmbZxv7V12ewF3RcQ8AEn3NHH/8cAZwGPAhIh4RdJWkjYAOkXEq5IOBA7N9bhUAN1LtHOhpBuAOyJihqQJwDVpDsqdETFZ0j7AtsB4SQBrAE82EONpkg5Py5sBPYAPmnieZmZmJTkZWXZaxv2fAnYlS2oKScEM4BiyXpPCMY6MiJeWOrC0UWE5IoZKuhf4NvCUpAMi4lFJ3wC+A/xD0p+Aj4AxEXFsY4KT1Bs4AOgVEXMljSNLhszMzFqEh2mW3ePAIZIq0tyLJn1LU0TMBt4E+rMkGXkSGMiSZOR+4FSlrgxJO1FE0pYRURsR55MNvWwj6SvAuxFxJXA1sDNZ8rOnpK3Sfh0lbV1PiF2Aj1Iisg3w9aacn5mZWUOcjCyjiJgA3A1MAe4gSwSaOoNzPLBmRLyZ1p8EtmBJMvJboD0wVdK0tF5sYJqkOoVsvsi/gN5k80SeJZvXcnFEvEeW+IxMtw0/BWxTT2z3Aaunur9N9c3MzFqMIqLcMaz0JHWKiDmSOgKPAgMiYlK54yqX6urqqKmpKXcYZma2ApE0MSJK3uThOSMtY5ikbcnmUoxoy4mImZlZUzkZaQERcVx+XdLlwJ5F1XoA/y4quzgirl2esZmZma3onIwsBxFxSrljMDMzW1k4GTEzsxXK559/zowZM5g/f365Q7FmqKioYNNNN6V9+/aN3sfJiJmZrVBmzJhB586dqaysJH2jga0kIoIPPviAGTNmsPnmmzd6P9/aa2ZmK5T58+ez/vrrOxFZCUli/fXXb3KvlpMRMzNb4TgRWXk152/nZMTMzMzKynNGzMxshVY5+N4WbW/60MY9tWPUqFEcccQRvPDCC2yzTfZF1ePGjeOCCy5g9OjRi+v179+fgw8+mL59+9K7d29mzpxJRUUFa6yxBldeeSVVVVUAzJo1i1NPPZXx48cDsOeee3LppZfSpUv2pPOXX36ZgQMH8vLLL9O+fXt69uzJpZdeykYbbURzffjhh/Tr14/p06dTWVnJLbfcwrrrrvulehdddBFXXXUVkujZsyfXXnstFRUVnHPOOVx55ZVssMEGAPz+97/n29/+NrW1tfz5z39m+PDhzY4tz8mItbjat2a1+H8e1rY09s3CbHkaOXIke+21FzfddBPnnHNOo/e74YYbqK6u5tprr+XnP/85Y8aMAeCHP/wh22+/Pddddx0AZ599NieddBK33nor8+fP5zvf+Q4XXnghhxxyCABjx47lvffeW6ZkZOjQoey///4MHjyYoUOHMnToUM4///yl6rz11ltccsklPP/883To0IGjjz6am266if79+wNw+umnM2jQoKX26dmzJzNmzOCNN96ge/fih8g3nYdpzMzMisyZM4fx48dz9dVXc9NNNzWrjV69evHWW28B8J///IeJEycyZMiQxdvPOussampqeOWVV7jxxhvp1avX4kQEYN9992X77bdfpvO46667OPHEEwE48cQTufPOO0vW++KLL5g3bx5ffPEFc+fOZeONN26w7UMOOaTZ16aYkxEzM7Mid955J9/61rfYeuutWW+99Zg0qelP+bjvvvvo06cPAM8//zxVVVW0a9du8fZ27dpRVVXFc889x7Rp09hll10abHP27NlUVVWV/Hn++ee/VP+dd96hW7duAHTr1o133333S3U22WQTBg0aRPfu3enWrRtdunThwAMPXLz9sssuY4cdduAHP/gBH3300eLy6upqHnvssUZfj/p4mMbMzKzIyJEjGThwIADHHHMMI0eOZOedd67zTpF8+fHHH8+nn37KwoULFycxEVFy37rK69K5c2cmT57c+BNphI8++oi77rqL1157jXXWWYejjjqK66+/nu9+97ucfPLJDBkyBEkMGTKEM844g2uuuQaADTfckLfffrtFYnAy0kIkDQJOAr4AFgJ/jojryhuVmZk11QcffMDDDz/MtGnTkMTChQuRxB//+EfWX3/9pXoHIJsk2rVr18XrN9xwAzvuuCODBw/mlFNO4Y477mC77bbj2WefZdGiRay2WjYosWjRIqZMmcLXvvY13n33XR555JEGY5s9ezZ77713yW033ngj22677VJlG220ETNnzqRbt27MnDmTDTfc8Ev7Pfjgg2y++eaLJ6keccQRPPHEE3z3u99dar7Kj370Iw4++ODF6/Pnz6dDhw4NxtwYHqZpAZJ+DHwT2C0itge+AfgmeTOzldBtt93G9773PV5//XWmT5/Om2++yeabb87jjz9Ojx49ePvtt3nhhRcAeP3115kyZcriO2YK2rdvz3nnncdTTz3FCy+8wFZbbcVOO+3Eeeedt7jOeeedx84778xWW23FcccdxxNPPMG99y6Z/H/fffdRW1u7VLuFnpFSP8WJCMChhx7KiBEjABgxYgSHHXbYl+p0796dp556irlz5xIRPPTQQ3zta18DYObMmYvrjRo1aqk5LC+//PIyz2kpcM9IImkIcDzwJvA+MDEiLmjk7r8C9o2ITwAiYhYwIrW7P3AB2bWeAJwcEQskTQduBPYF2gMDgD8AWwF/ioi/SeoE3AWsm+r8OiLuklQJ3Ac8DnwdmAJcC/wG2BA4PiKekbQecA2wBTAXGBARUyWdA3RP5d2Bv0TEJSneO4HNgAqypwoPk9QOuBqoBgK4JiIuKrp+A9I50G7tDRp52czMGtbad1eNHDmSwYMHL1V25JFHcuONN7L33ntz/fXX8/3vf5/58+fTvn17rrrqqsW35+Z16NCBM844gwsuuICrr76aq6++mlNPPZWtttqKiKBXr15cffXVi+uOHj2agQMHMnDgQNq3b88OO+zAxRdfvEznMnjwYI4++miuvvpqunfvzq233grA22+/zUknncQ///lPdt99d/r27cvOO+/M6quvzk477cSAAQMA+MUvfsHkyZORRGVlJX//+98Xtz127Fi+852W+dsoIlqkoZWZpGrgKqAXWdIwCfh7Y5IRSZ2BNyLiSzduS6oA/g3sHxEvS7oOmBQRf0nJyPkRcYWki4D9gT3JkoDnImJDSasDHSPiE0ldgaeAHsBXgP8AOwHPkSU5U4AfAocC34+IPpIuBd6PiN9I2g+4MCKqUjJyIFki1Bl4Cfh/EfG5pPUi4kNJHVK7+wCVwNCI+GY6r3Ui4uO6rsma3XpEtxP/0tClM6uTb+1t21544YXFn8xtxbRgwQL22WcfHn/8cVZf/cv9GqX+hpImRkR1qfY8TJPZC7grIuZFxGzgnibsK7LeglK+CrwWES+n9RFkQzgFd6fftcDTETE7It4D5ktaJ7X9e0lTgQeBTYDCAN5rEVEbEYvIEpKHIsssa8mSh8J5/QMgIh4G1pdUSN/vjYgFEfE+8G6u3dMkTSFLfDYjS35eBbaQdKmkbwGfNP7ymJnZquaNN95g6NChJROR5nAykmn2/I40NPOppC2a0e6C9HtRbrmwvjrZsNEGwC4RUQW8Q9ZzQon6C4r2rev4hcQpv/9CYHVJvYEDgF4RsSPwLFARER8BOwLjgFPIepHMzKyN6tGjB717926x9pyMZB4HDpFUkeZpNLWP+A/A5ZLWBpC0dppD8SJQKWmrVO8EoOHp0kt0Ad5Nwyf7kg3PNMWjZAkNKdF4vzCvpZ7jfRQRcyVtQzYfhTREtFpE3A4MAXZuYhxmZk3iKQQrr+b87TyBFYiICZLuJpt38TpQA8xqQhNXAJ2ACZI+Bz4nu7V3vqTvA7em+R8TgL81od0bgHsk1QCTyZKbpjgHuDYN88wFTmyg/n3Aj1P9l8iGaiAbHrpWUiF5/WV9jfTcpAs1HvM3s2aqqKjggw8+YP311/fTe1cyEcEHH3xARUVFw5VzPIE1kdQpIuZI6kjWozAgIpr+lXtGdXV11NTUlDsMM1tJff7558yYMYP58+eXOxRrhoqKCjbddFPat2+/VHl9E1jdM7LEMEnbks3JGOFExMysPNq3b8/mm29e7jCsFTkZSSLiuPy6pMvJbrXN60F2q27exRFx7fKMzczMbFXmZKQOEXFKuWMwMzNrC3w3jZmZmZWVJ7Bai5M0m+xuHGs9XckeY2Ctx9e89fmat76WvOZfiYiSzwvxMI0tDy/VNWPalg9JNb7mrcvXvPX5mre+1rrmHqYxMzOzsnIyYmZmZmXlZMSWh2HlDqAN8jVvfb7mrc/XvPW1yjX3BFYzMzMrK/eMmJmZWVk5GTEzM7OycjJiLUrStyS9JOk/kgaXO55VkaTNJI2V9IKk5yT9NJWfI+ktSZPTz7fLHeuqRNJ0SbXp2taksvUkjZH07/R73XLHuaqQ9NXca3mypE8kDfTrvGVJukbSu5Km5crqfF1L+mX6//0lSf/VYnF4zoi1FEntgJeBbwIzgAnAsRHxfFkDW8VI6gZ0i4hJkjoDE4E+wNHAnIi4oJzxraokTQeqI+L9XNkfgQ8jYmhKvteNiP8tV4yrqvR/y1vA7sD38eu8xUj6BjAHuC4itk9lJV/X6WGyI4HdgI2BB4GtI2LhssbhnhFrSbsB/4mIVyPiM+Am4LAyx7TKiYiZhadKR8Rs4AVgk/JG1WYdBoxIyyPIkkJrefsDr0TE6+UOZFUTEY8CHxYV1/W6Pgy4KSIWRMRrwH/I/t9fZk5GrCVtAryZW5+B3ySXK0mVwE7A06noJ5Kmpq5XDxm0rAAekDRR0oBUtlFEzIQsSQQ2LFt0q7ZjyD6RF/h1vnzV9bpebv/HOxmxlqQSZR4HXE4kdQJuBwZGxCfAFcCWQBUwE/hz+aJbJe0ZETsDBwGnpO5tW84krQEcCtyaivw6L5/l9n+8kxFrSTOAzXLrmwJvlymWVZqk9mSJyA0RcQdARLwTEQsjYhFwJS3UfWqZiHg7/X4XGEV2fd9Jc3gKc3neLV+Eq6yDgEkR8Q74dd5K6npdL7f/452MWEuaAPSQtHn6NHMMcHeZY1rlSBJwNfBCRFyYK++Wq3Y4MK14X2seSWulycJIWgs4kOz63g2cmKqdCNxVnghXaceSG6Lx67xV1PW6vhs4RtKakjYHegDPtMQBfTeNtah0m91fgHbANRHxu/JGtOqRtBfwGFALLErFvyL7T7uKrNt0OvDfhXFfWzaStiDrDYHsaec3RsTvJK0P3AJ0B94AjoqI4smA1kySOpLNUdgiImalsn/g13mLkTQS6A10Bd4BzgbupI7XtaQzgR8AX5ANEf+rReJwMmJmZmbl5GEaMzMzKysnI2ZmZlZWTkbMzMysrJyMmJmZWVk5GTEzM7OycjJitpKStDA9tXSapHskrdNA/XMkDWqgTp/0MKzC+rmSDmiBWIdL6rus7TTxmAPTraErDEnbpL/Zs5K2LNo2XdJjRWWTC09TlVQt6ZIWiKEy/4TWom1X5f/+y5ukjSTdKOnV9DX7T0o6vLWObysOJyNmK695EVGVnrT5IXBKC7TZB1j8ZhQRZ0XEgy3QbqtKT3kdCKxQyQjZ9b0rInaKiFdKbO8saTMASV/Lb4iImog4rbEHStegSSLipNZ6ynb68r47gUcjYouI2IXsixI3Xc7HXX15tm/N42TEbNXwJOmBVZK2lHRf+qT5mKRtiitL+pGkCZKmSLpdUkdJe5A9A+RP6RP5loUeDUkHSbolt39vSfek5QPTJ9pJkm5Nz8ypU+oB+H3ap0bSzpLul/SKpB/n2n9U0ihJz0v6m6TV0rZjJdWmHqHzc+3OST05TwNnkj3ifKyksWn7Fel4z0n6TVE8v0nx1xaul6ROkq5NZVMlHdnY85VUJemptN8oSeumLwQcCJxUiKmEW4B+abn4m0d7SxrdQGz5a9BL0s/SdZomaWDuOKtLGpH2va3QgyRpnKTqRlzn89Pr60FJu6X9XpV0aKrTTtKf0mtsqqT/LnGu+wGfRcTfCgUR8XpEXFpfG+k6jEtxvyjphpTYIGkXSY+k2O7Xkq80H5dec48AP5V0iKSnlfVQPShpozr+HtZaIsI//vHPSvgDzEm/25E9ROxbaf0hoEda3h14OC2fAwxKy+vn2jkPODUtDwf65rYNB/qSfevoG8BaqfwK4Ltk39r4aK78f4GzSsS6uF2yb808OS1fBEwFOgMbAO+m8t7AfGCLdH5jUhwbpzg2SDE9DPRJ+wRwdO6Y04GuufX1ctdrHLBDrl7h/P8HuCotnw/8Jbf/uk0436nAPmn53EI7+b9BiX2mA1sDT6T1Z8l6qablrsnoumIrvgbALmTf0rsW0Al4juwJz5Wp3p6p3jUseV2MA6obcZ0PSsujgAeA9sCOwORUPgD4dVpeE6gBNi8639OAi+p5fZdsI12HWWQ9KKuRJeJ7pRieADZI+/Qj+xbownn9tehvWfjSz5OAP5f733Nb/3F3ldnKq4OkyWRvLhOBMelT+h7ArenDImT/kRfbXtJ5wDpkb1T313egiPhC0n3AIZJuA74D/ALYh+wNc3w63hpkbw4NKTyzqBboFBGzgdmS5mvJ3JdnIuJVWPyV1XsBnwPjIuK9VH4D8A2y7v6FZA8PrMvRkgaQvbl2S3FPTdvuSL8nAkek5QPIhg0K1+AjSQc3dL6SugDrRMQjqWgES54425APgY8kHQO8AMyto96XYkuL+WuwFzAqIj5Ncd0B7E127d+MiPGp3vVkicEFufZ3pe7r/BlwX6pXCyyIiM8l1ZK9FiF7ds8OWjJPqAvZc0xeq+vEJV2eYv4sInatp43PyF4bM9J+k9NxPwa2J/t3AFnSmf+a+Jtzy5sCN6eekzXqi8tah5MRs5XXvIioSm9+o8nmjAwHPo6Iqgb2HU72SXeKpP5knzYbcnM6xofAhIiYnbrHx0TEsU2MfUH6vSi3XFgv/L9U/KyKoPQjzAvmR8TCUhuUPdRrELBrSiqGAxUl4lmYO75KxNDc822Km4HLgf711CkVGyx9Deq7VqWubXH7dfk8UpcCub9fRCzSkvkYIuttqi/JfQ44cnEAEadI6krWA1JnG5J6s/RrpvA3E/BcRPSq43if5pYvBS6MiLtTe+fUE6e1As8ZMVvJRfYAsdPI3mznAa9JOgqySYKSdiyxW2dgpqT2wPG58tlpWynjgJ2BH7HkU+ZTwJ6StkrH6yhp62U7o8V2U/YE6NXIutwfB54G9pHUVdkEzWOBR+rYP38ua5O9Gc1K8wMOasTxHwB+UliRtC6NON/09/hI0t6p6IR6YixlFPBH6u+tKhVbsUeBPinGtciecFu4W6e7pMKb9rFk1zavKde5lPuBk9PrC0lbpxjyHgYqJJ2cK8tPOG5MG3kvARsUzktSe0nb1VG3C/BWWj6xjjrWipyMmK0CIuJZYApZ1/3xwA8lTSH79HlYiV2GkL3hjAFezJXfBPxcJW49TZ+4R5O9kY9OZe+RfYIfKWkq2Zv1lybMNtOTwFCyR8S/RjbkMBP4JTCW7HwnRcRddew/DPiXpLERMYVsDsZzZHMkxtexT955wLppAucUYN8mnO+JZBOBp5I9YfbcRhwPgIiYHRHnR8RnTYmtRDuTyHrAniH7W1+VXieQDQGdmOJbj2wOUH7fplznUq4CngcmKbuN+O8U9cSn3pU+ZEnPa5KeIRvS+t/GtlHU3mdk84rOT9dkMtmQZSnnkA1lPga834TzsuXET+01sxVO6jofFBEHlzkUM2sF7hkxMzOzsnLPiJmZmZWVe0bMzMysrJyMmJmZWVk5GTEzM7OycjJiZmZmZeVkxMzMzMrq/wODM5zKb7yEVAAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.4883630919857232, pvalue=6.528679220999625e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.7973068224342603, pvalue=3.256455091798984e-23)\n", "Slope and P-value = PearsonRResult(statistic=-0.44981008837555736, pvalue=2.9991010405578696e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.8888993842560893, pvalue=5.364876399991966e-35)\n", "Slope and P-value = PearsonRResult(statistic=-0.7731071833527072, pvalue=4.338442520827486e-21)\n", "Slope and P-value = PearsonRResult(statistic=-0.6613022445685011, pvalue=6.9531968015579e-14)\n", "Slope and P-value = PearsonRResult(statistic=-0.9723450502071986, pvalue=1.0427524407950407e-63)\n", "Slope and P-value = PearsonRResult(statistic=-0.7928476560966231, pvalue=8.420313419817915e-23)\n", "Slope and P-value = PearsonRResult(statistic=0.5859298039025379, pvalue=1.5134283773552189e-10)\n", "Slope and P-value = PearsonRResult(statistic=0.17861596311022865, pvalue=0.07539879289052596)\n", "1.0 418\n", "0.0 277\n", "Name: new_Cd, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.793087504449635, pvalue=8.005605126792964e-23)\n", "Slope and P-value = PearsonRResult(statistic=-0.9304294700810536, pvalue=1.6283499419704348e-44)\n", "Slope and P-value = PearsonRResult(statistic=-0.9771744059914408, pvalue=9.636701102769373e-68)\n", "Slope and P-value = PearsonRResult(statistic=0.7333596237420007, pvalue=4.094115873196047e-18)\n", "Slope and P-value = PearsonRResult(statistic=-0.8210157578813935, pvalue=1.3554655718306405e-25)\n", "Slope and P-value = PearsonRResult(statistic=-0.8823540050552164, pvalue=7.533442144654133e-34)\n", "Slope and P-value = PearsonRResult(statistic=0.840917862032183, pvalue=6.995528265588536e-28)\n", "Slope and P-value = PearsonRResult(statistic=-0.8456457357940577, pvalue=1.7988663090873991e-28)\n", "Slope and P-value = PearsonRResult(statistic=-0.8841117473481158, pvalue=3.7641292697577705e-34)\n", "Slope and P-value = PearsonRResult(statistic=-0.8881654115861993, pvalue=7.273829799068989e-35)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'Cd','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns')\n", "\n", "feces=sample[sample.SampleType=='Feces'].copy()\n", "feces.loc[feces['Cd'] < feces.Cd.median(), 'new_Cd'] = 0\n", "feces.loc[feces['Cd'] >= feces.Cd.median(), 'new_Cd'] = 1 \n", "\n", "soil=sample[sample.SampleType=='Soil'].copy()\n", "soil.loc[soil['Cd'] < soil.Cd.median(), 'new_Cd'] = 0 \n", "soil.loc[soil['Cd'] >= soil.Cd.median(), 'new_Cd'] = 1 \n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'Cd', 'new_Cd'],axis='columns'),sample.new_Cd,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"Cd_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_Cd']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"Cd in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Cd','Cd','SampleType'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Cd_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (34) Cr" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 878)\n", "Soil (695, 878) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "0.0 349\n", "1.0 349\n", "Name: new_Cr, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.5101989409004017, pvalue=5.882169390039043e-08)\n", "Slope and P-value = PearsonRResult(statistic=-0.7842232630262335, pvalue=4.958362011035403e-22)\n", "Slope and P-value = PearsonRResult(statistic=0.9416176535811007, pvalue=3.9683380460426825e-48)\n", "Slope and P-value = PearsonRResult(statistic=0.77570491362536, pvalue=2.6425930130445562e-21)\n", "Slope and P-value = PearsonRResult(statistic=-0.18158151569641726, pvalue=0.07059902710228211)\n", "Slope and P-value = PearsonRResult(statistic=0.1825951774894521, pvalue=0.0690161531298496)\n", "Slope and P-value = PearsonRResult(statistic=0.06799649122673541, pvalue=0.5014597800735828)\n", "Slope and P-value = PearsonRResult(statistic=0.7316497501425507, pvalue=5.347657621152483e-18)\n", "Slope and P-value = PearsonRResult(statistic=-0.5806652922884883, pvalue=2.4086147941612196e-10)\n", "Slope and P-value = PearsonRResult(statistic=-0.9586372628433468, pvalue=2.771938978158176e-55)\n", "1.0 354\n", "0.0 341\n", "Name: new_Cr, dtype: int64\n" ] }, { "data": { "image/png": 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YEhErgXnATtXEehXwt4h4gCzxT4mI91K744Fvpnqf5cR+G9lxqo+/RsRqoAJoAzycyiuA0qqVI2J0RJRFRFmbDp3r2ZWZmbVmdUn2KlRnEbEMmML6ifGTnOWxwA/SCPcXfD7tDxDVNHsr2QeDs4Ax1bQLsCpneS15rleQNIjsQ8AvKouq6TOffPGt4fNjXFJl3SqAiPgMWB0Rldt/li82MzOzhqpLsp8GHC2pRFJH8kwx11W6WO7rwBvVVOkELE7nrE/LKX8aODktn1Zlm7HAUICIeKkRse0DDANOTwkY4HngoHROvw3Z7MGTad0mwMC0fCrZcapqAbBPWj6+obGZmZk1Rq3JPiKmA/cDs4GJQDlQ35PGlefs55BNU0+spt4IsgQ7GXglp/y/gCGSpgPrzWFHxD/Jzu/njuob4gfAVsAT6SK9myJiMfBT4Amy/Z8ZEZNS/U+A3SXNIDunf2meNn8BXCXpKbLZBDMzs2anz2ePa6gkdYyIZenq+KnA4IiY2eTR1UGKqQLYOyJaxZVrZWVlUV5e3tJhmJnZBkTSjIjIexF9XW+qMzqNzGeSXQS3oST6w8hmAK5uLYnezMysvup0IVhEnJr7WtK1ZFfI5+oJvF6l7KqIaOz0ek1xPQZ0a6r2zczMikGDrvqOiCGFDsTMzMyahh+EY2ZmVuSc7M3MzIqck72ZmVmRc7I3MzMrck72ZmZmRc7J3szMrMg52ZuZmRU5P11tI1SxaAmlwx9q6TDMrAksGNXgZ42ZVcsjezMzsyLnZG9mZlbkWiTZSxoraWCVsu0l3d3E/Q6SdE09t1kgqUue8vMknZHT7vaFitPMzKyQNphz9hHxDjCw1oobiIi4PuflIGAu8E7LRGNmZla9Bo/sJY2Q9IqkyZImSBrWmEAklUqam5YHSbpP0gOS5kv6gaQfSXpR0nOStkr1pki6XNILkl6TdGAqL5E0RlJF2ubgnK62l/SwpNclXZHT/3WSyiW9JOkXVcK7KPXxgqQeqf5IScPSDEUZMF7SLEntJV0sabqkuZJGS1It8e6eymZJmiOpZ57jMzjFV752uZ/ma2ZmddegZC+pDDge6AMcR5bsCm0P4FRgP+CXwPKI6AM8C5yRU69tROwHDAUuSWVDACKiF3AKME5SSVrXGzgJ6AWcJGnHVP6ziCgD9gQOkrRnTh8fpz6uAX6fG2RE3A2UA6dFRO+IWAFcExH7RsQeQHvgqFriPY/sccC9yY7lwqoHIyJGR0RZRJS16dC52oNmZmZWVUNH9v2ASRGxIiKWAg8UMKZKT0TE0oh4D1iS00cFUJpTb2L6PSOnvB9wK0BEvAK8BeyS1j0eEUsiYiUwD9gplZ8oaSbwIrA7sFtOHxNyfvetQ+wHS3peUgVwSGqvpnifBf5H0k+AndIHBjMzs4JoaLJXQaPIb1XO8mc5rz9j/WsNKsvX5pTXFF9uu2uBtpK6A8OAQyNiT+AhoCSnXlSz/AVpBuGPwMA0s3Bjlba+EG9E3A58F1gBPCLpkJr6MDMzq4+GJvtpwNHp3HhHYEO7C8RU4DQASbsA3YBXa6i/OfAJsETSdsARVdaflPP72TzbLwU6peXKxP5+Oja1XnQo6SvAmxHxB+B+slMJZmZmBdGgq/EjYrqk+4HZZFPk5WRT7fVxg6Tfp+W3yc6tF8ofgevTNPoaYFBErErXyX1BRMyW9CLwEvAm8HSVKptKep7sw1G+OMem/laQTfPfSHa6YQEwvQ7xngScLmk18P+AS+uwjZmZWZ0oosZZ6eo3lDpGxDJJHchG0oMjYmZBo7O8ysrKory8vKXDMDOzDYikGelC8y9ozPfsR0vajWzaepwTvZmZ2Yapwck+Ik7NfS3pWuCAKtV6Aq9XKbsqIsY0tF8zMzOrn4LdQS8ihhSqLTMzMyscPwjHzMysyDnZm5mZFTknezMzsyLnZG9mZlbknOzNzMyKnJO9mZlZkXOyNzMzK3IF+569NZ+KRUsoHf5QS4dhZtVYMGpDezaYtXYe2ZuZmRU5J3szM7Mit0Eme0ljJc2XNEvSTEl9C9z+eZLOSMtTJOV9SlA927wpPRgISQskdUnLyxrbtpmZWWNsyOfsL4qIuyUdDtwA7FmohiPi+kK1ldPmOYVu08zMrBCabGQvaYSkVyRNljRB0rAGNjUV6CGpo6TH00i/QtIxOX2dLumFNBNwg6Q2qXyZpF9Kmi3pOUnbpfKRVeI5XdIzkuZK2i/V2S+VvZh+fzWVt5F0ZYphjqQLUnmNMwSS+kt6MOf1NZIGpeVRkual9q6sZvvBksolla9dvqSBh9LMzFqjJkn2KekdD/QBjgMaM01+NFABrASOjYi9gYOB3yjzNeAk4ICI6A2sBU5L224GPBcRe5F9aDi3mj42i4j9gf8E/pTKXgG+GRF9gIuBX6XywUB3oE9E7AmMb8S+IWkr4Fhg99TeZfnqRcToiCiLiLI2HTo3pkszM2tlmmoavx8wKSJWAEh6oAFt/FrSz4H3gLMBAb+S9E3gM2AHYDvgUGAfYLokgPbAu6mNT4HK0fQM4FvV9DUBICKmStpc0hZAJ2CcpJ5AAO1S3cOA6yNiTdrmwwbsW66PyT7I3CTpoZx4zczMCqKpkr0K0MZFEXH3ugazKe9tgH0iYrWkBUBJ6mtcRPw0TxurIyLS8lqq39/I8/p/gSci4lhJpcCUylDy1K+LNaw/k1ICEBFr0qmDQ4GTgR8AhzSgfTMzs7ya6pz9NOBoSSWSOgKFuMNEZ+DdlOgPBnZK5Y8DAyVtC9m0uKSdqmukGielbfsBSyJiSepvUVo/KKfuo8B5ktpW9lfHPt4CdpO0qaTOZMmddHw6R8RfgKFA73rGbmZmVqMmGdlHxHRJ9wOzyZJcOdDYq8rGAw9IKgdmkZ1TJyLmpen+RyVtAqwGhqR+6+ojSc8AmwPfT2VXkE3j/wj4W07dm4BdgDmSVgM3AtfU1kFEvC3pTmAO8DrwYlrVCZgkqXKW4of1iNvMzKxW+nyWu8ANSx0jYpmkDmQXxw2OiJlN0lkrU1ZWFuXl5S0dhpmZbUAkzYiIvBfEN+X37Eenm8yUkJ1Td6I3MzNrAU2W7CPi1NzXkq4FDqhSrSfZlHauqyJiTFPFZWZm1to02x30ImJIc/VlZmZmn9sg741vZmZmheNkb2ZmVuSc7M3MzIqck72ZmVmRc7I3MzMrck72ZmZmRc7J3szMrMg12/fsrXAqFi2hdPhDLR2GWau0YFQhnutl1rw8sjczMytyTvZmZmZFboNO9pLGSpovaZakVyRdUsC2/yJpC0mlkuYWoL3tJd2dlvtLejAtD5JU6yNwzczMmsoGneyTiyKiN9AbOFNS96oVJLWpb6MR8Z2I+Fejo/u8vXciYmCh2jMzMyuUJk/2kkakUflkSRMkDWtgUyXp9yep3QWSLpY0DThB0uGSnpU0U9JdkjpKOkLSnTmx9Jf0QM72XdKqtpLGSZoj6W5JHVKdiyVNlzRX0mhJSuU9JD0maXbqb+e6zBCkmYqBOa+Xpd9dJU1NMxhzJR2YZ9vBksolla9dvqSBh9DMzFqjJk32ksqA44E+wHFAWQOa+bWkWcBC4I6IeDdn3cqI6Ac8BvwcOCwi9gbKgR8Bk4FvSNos1T8J+HOePr4KjI6IPYGPgf9M5ddExL4RsQfQHjgqlY8Hro2IvYD9gcUN2K9cpwKPpBmMvYBZVStExOiIKIuIsjYdOjeyOzMza02aemTfD5gUESsiYinwQAPaqJzG/zfgUEn756yrTNzfAHYDnk4fDM4EdoqINcDDwNGS2gJHApPy9PF2RDydlm9LcQMcLOl5SRXAIcDukjoBO0TEvQARsTIiljdgv3JNB86SNBLolY6VmZlZQTR1slehGoqIZcAUPk/EkKb0Uz+TI6J3+tktIs5O6/4MnEiWrKdXk0ij6mtJJcAfgYER0Qu4kexUQmP2aQ3pmKdTAl9K+zYV+CawCLhV0hmN6MPMzGw9TZ3sp5GNqkskdSQbWTdIGpl/HXgjz+rngAMk9Uh1O0jaJa2bAuwNnEv+KXyAbpL6puVTUtyV1wi8n2IfCBARHwMLJQ1IfW1aeY6/DhYA+6TlY4B2qY2dgHcj4kbg5hSvmZlZQTTpHfQiYrqk+4HZwFtk59Lre3XZryX9nGwU/DgwMU8/70kaBEyQtGkq/jnwWkSsTV+DG0Q2vZ/Py2RX+t8AvA5cFxHLJd0IVJAl6ek59b8H3CDpUmA1cALwWR325UZgkqQX0r5Uzkz0By6StBpYBtQ4su+1Q2fKfRcvMzOrI0VUncEucAdSx4hYlka/U4HBETGzSTstcmVlZVFeXt7SYZiZ2QZE0oyIyHshfHPcG3+0pN3IpsXHOdGbmZk1ryZP9hFxau5rSdcCB1Sp1pNs+jzXVRExpiljMzMzaw2a/al3ETGkufs0MzNrzTaG2+WamZlZIzjZm5mZFTknezMzsyLnZG9mZlbknOzNzMyKnJO9mZlZkWv2r95Z41UsWkLp8IdaOgyzFrHAt4o2qzeP7M3MzIqck72ZmVmR2yCSvaSxkuZLmiVpZs7jZpuir5GShjVV+2ZmZhuaDSLZJxdFRG9gOHBDC8diZmZWNAqW7CWNkPSKpMmSJjRi9DwV6JHaHCVpnqQ5kq5MZdtIukfS9PRzQCpfb8Quaa6k0rT8M0mvSnoM+GpOnd6Snkvt3ytpy1Q+RdLvJE2V9LKkfSVNlPS6pMtytv9R6meupKGprDRtc6OklyQ9Kql9Wnduinl22ocOqfyE1MZsSVMbeNzMzMzyKkiyl1QGHA/0AY4D8j5Pt46OBiokbQUcC+weEXsClUn2KuB3EbFv6vOmWmLbBzg5J7Z9c1bfAvwktV8BXJKz7tOI+CZwPTAJGALsAQyStHVq9yzg68A3gHMl9Unb9gSujYjdgX+lOAEmRsS+EbEX8DJwdiq/GPj3VP7davZjsKRySeVrly+paZfNzMzWU6iv3vUDJkXECgBJDzSgjV9L+jnwHlkS/BhYCdwk6SHgwVTvMGA3SZXbbS6pUw3tHgjcGxHLU2z3p9+dgS0i4slUbxxwV85296ffFcBLEbE4bfcmsGPa53sj4pNUPjH1dT8wPyJmpe1nAKVpeY80M7AF0BF4JJU/DYyVdCcwMd9ORMRoYDTApl17Rg37a2Zmtp5CJXvVXqVWF0XE3es1Ku0HHEo2Mv8BcAjZbETfyg8WOXXXsP5MRUnOckOS46r0+7Oc5crXbal5n3PrrwXap+WxwICImC1pENAfICLOk/R14EhglqTeEfFBA2I2MzP7gkKds58GHC2pRFJHsqTVKKmdzhHxF2Ao0DutepQs8VfWqyxfAOydyvYGuqfyqcCxktqnGYCjASJiCfCRpANTve8BlaP8upgKDJDUQdJmZKccnqplm07AYkntgNNy9mHniHg+Ii4G3iebOTAzMyuIgozsI2J6mh6fDbwFlAONPbHcCZgkqYRsFP3DVH4hcK2kOWTxTwXOA+4BzpA0C5gOvJZimynpz8CsFFtuQj4TuD5dKPcm2Tn4OkntjgVeSEU3RcSLlRcFVmME8HyKoyLtI2SnMHqm/Xyc7DiamZkVhCIKc/pXUseIWJYS51RgcETMLEjjtp6ysrIoLy9v6TDMzGwDImlGROS9QL6Q98YfLWk3snPl45zozczMNgwFS/YRcWrua0nXAgdUqdYTeL1K2VURMaZQcZiZmdn6muypdxExpKnaNjMzs7rbkG6Xa2ZmZk3Ayd7MzKzIOdmbmZkVOSd7MzOzIudkb2ZmVuSc7M3MzIqck72ZmVmRa7Lv2VvTqVi0hNLhD7V0GGaNsmBUo5+XZWZ15JG9mZlZkXOyNzMzK3ItkuwlfUPS85JmSXpZ0shU3l/S/gXs5zxJZ+QpL5U0t5ZtB0m6pgAxFHSfzMzM6qulztmPA06MiNmS2gBfTeX9gWXAM3VtSFLbiFiTb11EXN/YQAugPwXcJzMzs/pq8Mhe0ghJr0iaLGmCpGH12HxbYDFARKyNiHmSSoHzgB+mEf+BkraRdI+k6enngNT3SEmjJT0K3CJpJ0mPS5qTfnfLqTcsLe8jabakZ4F1D+mRVCJpjKQKSS9KOjgnzh0lPSzpVUmX5Gxzn6QZkl6SNDin/NuSZqZ+Hm/MPuU53oMllUsqX7t8ST0OtZmZtXYNGtlLKgOOB/qkNmYCM+rRxO+AVyVNAR4GxkXEAknXA8si4srUz+3A7yJiWkrgjwBfS23sA/SLiBWSHgBuiYhxkr4P/AEYUKXPMcAFEfGkpF/nlA8BiIheknYFHpW0S1q3H7AHsByYLumhiCgHvh8RH0pqn8rvIfvgdCPwzYiYL2mrVKdB+1T1gEXEaGA0wKZde0bdD7WZmbV2DZ3G7wdMqkxKKdnWWURcKmk8cDhwKnAK2XR3VYcBu0mqfL25pE5p+f6cpNgXOC4t3wpckduIpM7AFhHxZE6dI3L25eoU1yuS3gIqk/3kiPggtTEx1S0HLpR0bKqzI9AT2AaYGhHzU1sfVrP7dd0nMzOzgmhoslftVWoWEW8A10m6EXhP0tZ5qm0C9K2aAFOi/KSm5qu8Vp6y3HV1bSck9SdL2H0jYnmanSippY9cDd0nMzOzBmnoOftpwNHpfHdHoF53x5B0pD4f2vYE1gL/ApYCnXKqPgr8IGe73tU0+Qxwclo+LcW3TkT8C1giqV9OnUpTK1+n6ftuwKtp3bckbZWm6wcATwOdgY9Sot8V+Eaq+yxwkKTuqa2tUnlD98nMzKwgGpTsI2I6cD8wG5hINrVdn6vGvkd2zn4W2ZT6aRGxFngAOLbyYjbgQqAsXXg3j+xit3wuBM6SNCe1/V956pwFXJsu0MsdVf8RaCOpAvgzMCgiVqV101J8s4B70vn6h4G2qa//BZ5Lx+Q9YDAwUdLs1BaN2CczM7OCUETDrvWS1DEilknqQDY6HhwRMwsaneVVVlYW5eXlLR2GmZltQCTNiIiyfOsa8z370ZJ2IztfPc6J3szMbMPU4GQfEafmvpZ0LXBAlWo9gderlF0VEWMa2q+ZmZnVT8HuoBcRQ2qvZWZmZs3ND8IxMzMrck72ZmZmRc7J3szMrMg52ZuZmRU5J3szM7Mi52RvZmZW5JzszczMilzBvmdvzadi0RJKhz/U0mGYNdiCUfV6dpaZNZJH9mZmZkXOyd7MzKzINUuylzRW0vz0mNdZki5M5QskdSlgP6WS5haonVNrr5l322ca27+ZmVkhNec5+4si4u5m7K8xSoFTgdvruoGkNhGxNiL2b7KozMzMGqDOI3tJIyS9ImmypAmShhUyEEn3SZoh6SVJg1PZ+ZKuyKkzSNLVaflHkuamn6E5TbWVNE7SHEl3S+qQ6l8saXqqP1qSUnkPSY9Jmi1ppqSdgVHAgWkW4oeS2kj6ddp+jqT/SNv2l/SEpNuBilS2LGfdgzmxXyNpUFpeIOlXkp6VVC5pb0mPSHpD0nnVHJ/BqW752uVLCnPQzcysVahTspdUBhwP9AGOA8oa0Nevc6bxe+VZ//2I2Ce1faGkrYG7U3+VTgL+LGkf4Czg68A3gHMl9Ul1vgqMjog9gY+B/0zl10TEvhGxB9AeOCqVjweujYi9gP2BxcBw4KmI6B0RvwPOBpZExL7Avqm/7mn7/YCfRcRu9Tweb0dEX+ApYCwwMO3LpfkqR8ToiCiLiLI2HTrXsyszM2vN6jqy7wdMiogVEbEUeKABfV2UkmfviKjIs/5CSbOB54AdgZ4R8R7wpqRvpOT/VeDpFM+9EfFJRCwDJgIHpnbejoin0/JtqS7AwZKel1QBHALsLqkTsENE3AsQESsjYnme2A4HzpA0C3ge2Bromda9EBHzG3A87k+/K4DnI2Jp2t+VkrZoQHtmZmZ51fWcvZoyCEn9gcOAvhGxXNIUoCSt/jNwIvAKWYKPyin4akTV15JKgD8CZRHxtqSRqf267peACyLikTxxf1LNNmtY/8NUSZX1q9Lvz3KWK1/7/gdmZlYwdR3ZTwOOllQiqSNQ6DtidAY+Sol+V7Lp7EoTgQHAKWSJH2AqMEBSB0mbAceSTYcDdJPUNy2fkmKvTLTvp/gHAkTEx8BCSQMAJG2azvEvBTrlxPAIcL6kdqneLqnfmrwF7Jba7AwcWrdDYWZmVlh1GkFGxHRJ9wOzyZJYOVDIq8QeBs6TNAd4lWwqv7LvjyTNA3aLiBdS2UxJY4EXUrWbIuJFSaXAy8CZkm4AXgeuSx8ibiSbMl8ATM/p+3vADZIuBVYDJwBzgDXptMJY4CqyK/RnplmF98g+gFQrzSDcmdp6HXix/ofFzMys8RRRdda7mopSx4hYlka+U4HBETGzSaOzvMrKyqK8vLylwzAzsw2IpBkRkfcC+vqcGx4taTeyKfFxTvRmZmYbhzon+4hY745ykq4FDqhSrSfZlHWuqyJiTMPCMzMzs8Zq8FXfETGkkIGYmZlZ0/BXvMzMWpnVq1ezcOFCVq5c2dKhWAOUlJTw5S9/mXbt2tV5Gyd7M7NWZuHChXTq1InS0lJqvm2JbWgigg8++ICFCxfSvXv32jdI/IhbM7NWZuXKlWy99dZO9BshSWy99db1npVxsjcza4Wc6DdeDfm3c7I3MzMrcj5nb2bWypUOf6ig7S0YVbc7qt97770cd9xxvPzyy+y6664ATJkyhSuvvJIHH1z3hHAGDRrEUUcdxcCBA+nfvz+LFy+mpKSEL33pS9x444307t0bgCVLlnDBBRfw9NPZs9AOOOAArr76ajp3zp4U+tprrzF06FBee+012rVrR69evbj66qvZbrvtGryvH374ISeddBILFiygtLSUO++8ky233PIL9X73u99x0003IYlevXoxZswYSkpKGDlyJDfeeCPbbLMNAL/61a/4zne+Q0VFBb/5zW8YO3Zsg2PL5WS/EapYtKTg/znNmkJd/+hb6zRhwgT69evHHXfcwciRI+u83fjx4ykrK2PMmDFcdNFFTJ48GYCzzz6bPfbYg1tuuQWASy65hHPOOYe77rqLlStXcuSRR/Lb3/6Wo48+GoAnnniC9957r1HJftSoURx66KEMHz6cUaNGMWrUKC6//PL16ixatIg//OEPzJs3j/bt23PiiSdyxx13MGjQIAB++MMfMmzYsPW26dWrFwsXLuQf//gH3bp1a3B8lTyNb2ZmzW7ZsmU8/fTT3Hzzzdxxxx0NaqNv374sWrQIgL///e/MmDGDESNGrFt/8cUXU15ezhtvvMHtt99O37591yV6gIMPPpg99tijUfsxadIkzjzzTADOPPNM7rvvvrz11qxZw4oVK1izZg3Lly9n++23r7Xto48+usHHpionezMza3b33Xcf3/72t9lll13YaqutmDmz/ndgf/jhhxkwYAAA8+bNo3fv3rRp02bd+jZt2tC7d29eeukl5s6dyz777FNrm0uXLqV37955f+bNm/eF+v/85z/p2rUrAF27duXdd9/9Qp0ddtiBYcOG0a1bN7p27Urnzp05/PDD162/5ppr2HPPPfn+97/PRx99tK68rKyMp5566gvtNYSn8c3MrNlNmDCBoUOHAnDyySczYcIE9t5772qvNM8tP+200/jkk09Yu3btug8JEZF32+rKq9OpUydmzZpV9x2pg48++ohJkyYxf/58tthiC0444QRuu+02Tj/9dM4//3xGjBiBJEaMGMGPf/xj/vSnPwGw7bbb8s477xQkhg0+2adH2T4YEXe3dCz1JWkQ8GhEFOZfy8ysCHzwwQf87W9/Y+7cuUhi7dq1SOKKK65g6623Xm90C9lFcF26dFn3evz48ey1114MHz6cIUOGMHHiRHbffXdefPFFPvvsMzbZJJu0/uyzz5g9ezZf+9rXePfdd3nyySdrjW3p0qUceOCBedfdfvvt7LbbbuuVbbfddixevJiuXbuyePFitt122y9s99hjj9G9e/d1F+Edd9xxPPPMM5x++unrXS9w7rnnctRRR617vXLlStq3b19rzHXhafymNQio/cSMmVkrcvfdd3PGGWfw1ltvsWDBAt5++226d+/OtGnT6NmzJ++88w4vv/wyAG+99RazZ89ed8V9pXbt2nHZZZfx3HPP8fLLL9OjRw/69OnDZZddtq7OZZddxt57702PHj049dRTeeaZZ3jooc8vbn744YepqKhYr93KkX2+n6qJHuC73/0u48aNA2DcuHEcc8wxX6jTrVs3nnvuOZYvX05E8Pjjj/O1r30NgMWLF6+rd++99653DcFrr73W6GsKKjXLyF7SCOA04G3gfWBGRFzZiPbaAKOA/sCmwLURcYOk/sCwiDgq1bsGKI+IsZL2Ba4CNgNWAYcCq4HrgDJgDfCjiHgijci/C3QAdgbujYj/Tm2eAvwPIOChiPhJiufm1E4Af0r7WgaMl7QC6AvsD1xJdtynA+dHxKp8sUXE0ir7PBgYDNBm820aeujMzL6gub81MWHCBIYPH75e2fHHH8/tt9/OgQceyG233cZZZ53FypUradeuHTfddNO6r8/lat++PT/+8Y+58sorufnmm7n55pu54IIL6NGjBxFB3759ufnmm9fVffDBBxk6dChDhw6lXbt27Lnnnlx11VWN2pfhw4dz4okncvPNN9OtWzfuuusuAN555x3OOecc/vKXv/D1r3+dgQMHsvfee9O2bVv69OnD4MGDAfjv//5vZs2ahSRKS0u54YYb1rX9xBNPcOSRhfm3UUQUpKFqO5DKgJvIkl1bYCZwQ12Tfb5p/JT4to2IyyRtCjwNnADsRJ5kD9wOvAKcFBHTJW0OLAf+C9gjIs6StCvwKLALcDJwMdCHLPm+CvQD1gLPAfsAH6X6fyBL7KMi4lup3y0i4l+SpqR4yiWVkD3+99CIeE3SLelY/DFfbBGxprpjsmnXntH1zN/X5fCZtSh/9W7D9PLLL68bWdqGadWqVRx00EFMmzaNtm2/OC7P928oaUZElOVrrzmm8fsBkyJiRRqtPlCANg8HzpA0C3ge2BroWUP9rwKLI2I6QER8nJJpP+DWVPYK8BZZsgd4PCKWRMRKYB7ZB4l9gSkR8V7afjzwTeBN4CuSrpb0beDjamKYHxGvpdfj0rbVxWZmZq3UP/7xD0aNGpU30TdEc0zjN8UNmAVcEBGPrFco9WP9DzAlOfXzTWHUFNuqnOW1ZMcqb/2I+EjSXsC/A0OAE4Hv17Gv6mIzM7NWqmfPnvTsWdMYtn6aY2Q/DThaUomkjkAh5vUeAc6X1A5A0i6SNiMbme8maVNJncnOy0M2Tb59OjeOpE6S2gJTya4lQNIuQDeyKfvqPA8cJKlLOk9/CvCkpC7AJhFxDzAC2DvVXwp0yomhVFKP9Pp7wJM1xGZm1mSa+hSuNZ2G/Ns1eVJJ56HvB2aTJeNyYEk9m7lB0u/T8tvAAUApMFPZFyjfAwZExNuS7gTmkJ0ffzHF8Kmkk4CrJbUHVgCHkZ0vv15SBdkFeoPSBXPV7ctiST8FniAbkf8lIialUf0YSZUfnn6afo9N7VdeoHcWcFdK5tOB62uIbVl1B6PXDp0p97lQM2ugkpISPvjgAz/mdiNU+Tz7kpKS2ivnaPIL9AAkdYyIZZI6kI2mB0dE/W+XZACUlZVFeXl5S4dhZhup1atXs3Dhwno/E902DCUlJXz5y1+mXbt265XXdIFec00Xj5a0G9k59HFO9GZmLaddu3Z07969pcOwZtQsyT4iTs19Lelasqn4XD3Jpt5zXRURY5oyNjMzs2LXIheCRcSQlujXzMysNfLtcs3MzIpcs1ygZ4UlaSk1f0XQCq8L2a2erfn4mDc/H/PmV8hjvlNE5L2fur/PvXF6tborLq1pSCr3MW9ePubNz8e8+TXXMfc0vpmZWZFzsjczMytyTvYbp9EtHUAr5GPe/HzMm5+PefNrlmPuC/TMzMyKnEf2ZmZmRc7J3szMrMg52W9kJH1b0quS/i5peEvHU4wk7SjpCUkvS3pJ0n+l8pGSFkmalX6+09KxFhNJCyRVpGNbnsq2kjRZ0uvp95YtHWexkPTVnPfyLEkfSxrq93lhSfqTpHclzc0pq/Z9Lemn6e/7q5L+vWBx+Jz9xkNSG+A14FvAQrLH5J4SEfNaNLAiI6kr0DUiZkrqBMwABgAnAssi4sqWjK9YSVoAlEXE+zllVwAfRsSo9OF2y4j4SUvFWKzS35ZFwNfJHsXt93mBSPom2SPLb4mIPVJZ3vd1emDcBGA/YHvgMWCXiFjb2Dg8st+47Af8PSLejIhPgTuAY1o4pqITEYsrn8wYEUuBl4EdWjaqVusYYFxaHkf2ocsK71DgjYh4q6UDKTYRMRX4sEpxde/rY4A7ImJVRMwH/k72d7/RnOw3LjsAb+e8XoiTUJOSVAr0AZ5PRT+QNCdNzXlKubACeFTSDEmDU9l2EbEYsg9hwLYtFl1xO5lsRFnJ7/OmVd37usn+xjvZb1yUp8znYZqIpI7APcDQiPgYuA7YGegNLAZ+03LRFaUDImJv4AhgSJr+tCYm6UvAd4G7UpHf5y2nyf7GO9lvXBYCO+a8/jLwTgvFUtQktSNL9OMjYiJARPwzItZGxGfAjRRoes0yEfFO+v0ucC/Z8f1nuoai8lqKd1suwqJ1BDAzIv4Jfp83k+re1032N97JfuMyHegpqXv6NH4ycH8Lx1R0JAm4GXg5In6bU941p9qxwNyq21rDSNosXQyJpM2Aw8mO7/3AmanamcCklomwqJ1CzhS+3+fNorr39f3AyZI2ldQd6Am8UIgOfTX+RiZ9Deb3QBvgTxHxy5aNqPhI6gc8BVQAn6Xi/yH7o9ibbFptAfAflefdrHEkfYVsNA/Z0zhvj4hfStoauBPoBvwDOCEiql7sZA0kqQPZOeKvRMSSVHYrfp8XjKQJQH+yR9n+E7gEuI9q3teSfgZ8H1hDdgrxrwWJw8nezMysuHka38zMrMg52ZuZmRU5J3szM7Mi52RvZmZW5JzszczMipyTvVk1JK1NT/2aK+kBSVvUUn+kpGG11BmQHnZR+fpSSYcVINaxkgY2tp169jk0fXVrgyFp1/Rv9qKknausWyDpqSplsyqfRiapTNIfChBDae4Tzqqsuyn337+pSdpO0u2S3ky3IX5W0rHN1b9tOJzszaq3IiJ6pydVfQgMKUCbA4B1f+wj4uKIeKwA7Tar9JS0ocAGlezJju+kiOgTEW/kWd9J0o4Akr6WuyIiyiPiwrp2lI5BvUTEOc31lMp0c6j7gKkR8ZWI2IfsRlxfbuJ+2zZl+9YwTvZmdfMs6YEUknaW9HAaKT0ladeqlSWdK2m6pNmS7pHUQdL+ZPcg/3UaUe5cOSKXdISkO3O27y/pgbR8eBqRzZR0V7pnf7XSCPZXaZtySXtLekTSG5LOy2l/qqR7Jc2TdL2kTdK6U5Q9V36upMtz2l2WZiKeB35G9gjOJyQ9kdZfl/p7SdIvqsTzixR/ReXxktRR0phUNkfS8XXdX0m9JT2XtrtX0pbphlNDgXMqY8rjTuCktFz1znH9JT1YS2y5x6CvpB+l4zRX0tCcftpKGpe2vbtyBkTSFElldTjOl6f312OS9kvbvSnpu6lOG0m/Tu+xOZL+I8++HgJ8GhHXVxZExFsRcXVNbaTjMCXF/Yqk8emDA5L2kfRkiu0RfX7L1ynpPfck8F+Sjpb0vLIZlsckbVfNv4c1l4jwj3/8k+eH7JnekN2t8C7g2+n140DPtPx14G9peSQwLC1vndPOZcAFaXksMDBn3VhgINld4/4BbJbKrwNOJ7vr1tSc8p8AF+eJdV27ZHc9Oz8t/w6YA3QCtgHeTeX9gZXAV9L+TU5xbJ/i2CbF9DdgQNomgBNz+lwAdMl5vVXO8ZoC7JlTr3L//xO4KS1fDvw+Z/st67G/c4CD0vKlle3k/hvk2WYBsAvwTHr9Itksy9ycY/JgdbFVPQbAPmR3WdwM6Ai8RPaExNJU74BU7098/r6YApTV4TgfkZbvBR4F2gF7AbNS+WDg52l5U6Ac6F5lfy8EflfD+ztvG+k4LCGbAdiE7INuvxTDM8A2aZuTyO7iWblff6zyb1l507ZzgN+09P/n1v7j6Raz6rWXNIvsj/cMYHIaZe4P3JUGO5D9oaxqD0mXAVuQJYJHauooItZIehg4WtLdwJHAfwMHkSWkp1N/XyL741ubymcmVAAdI2IpsFTSSn1+7cELEfEmrLulZz9gNTAlIt5L5eOBb5JNB68lezhQdU5U9mjatkDXFPectG5i+j0DOC4tH0Y2rVx5DD6SdFRt+yupM7BFRDyZisbx+RPbavMh8JGkk4GXgeXV1PtCbGkx9xj0A+6NiE9SXBOBA8mO/dsR8XSqdxtZ4r0yp/19qf44fwo8nOpVAKsiYrWkCrL3ImTPDthTn1+n0ZnsPurzq9txSdemmD+NiH1raONTsvfGwrTdrNTvv4A9yP4fQPahLvc2un/OWf4y8Oc08v9STXFZ83CyN6veiojonZLLg2Tn7McC/4qI3rVsO5ZspDZb0iCy0VJt/pz6+BCYHhFL0/Tp5Ig4pZ6xr0q/P8tZrnxd+f++6r2yg/yP2Ky0MiLW5luh7KEdw4B9U9IeC5TkiWdtTv/KE0ND97c+/gxcCwyqoU6+2GD9Y1DTscp3bKu2X53VkYbE5Pz7RcRn+vx8uMhmS2r6EPkScPy6ACKGSOpCNoKvtg1J/Vn/PVP5bybgpYjoW01/n+QsXw38NiLuT+2NrCFOawY+Z29Wi8geEHIhWTJbAcyXdAJkF0FJ2ivPZp2AxcoelXtaTvnStC6fKcDewLl8Pkp6DjhAUo/UXwdJuzRuj9bZT9kTFDchm5KdBjwPHCSpi7IL0E4Bnqxm+9x92Zzsj/2SdH72iDr0/yjwg8oXkrakDvub/j0+knRgKvpeDTHmcy9wBTXPtuSLraqpwIAU42ZkT4irvNq/m6TKpHgK2bHNVZ/jnM8jwPnp/YWkXVIMuf4GlEg6P6cs94LKurSR61Vgm8r9ktRO0u7V1O0MLErLZ1ZTx5qRk71ZHUTEi8Bssqnd04CzJc0mGz0dk2eTEWR/0CcDr+SU3wFcpDxfDUsjxgfJEuWDqew9shHoBElzyJLhFy4IbKBngVFkjzCdTzYlvRj4KfAE2f7OjIjqHis7GvirpCciYjbZOfCXyM5RP13NNrkuA7ZMF6jNBg6ux/6eSXah4xyyJ7RdWof+AIiIpRFxeUR8Wp/Y8rQzk2wG5wWyf+ub0vsEslMEZ6b4tiK7BiN32/oc53xuAuYBM5V9ze8GqszUptmBAWQfKuZLeoHslMdP6tpGlfY+Jbuu4/J0TGaRndLKZyTZqa6ngPfrsV/WRPzUO7NWKE2tDouIo1o4FDNrBh7Zm5mZFTmP7M3MzIqcR/ZmZmZFzsnezMysyDnZm5mZFTknezMzsyLnZG9mZlbk/j9ZbeueinAIugAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.8663238473126911, pvalue=2.63701722235674e-31)\n", "Slope and P-value = PearsonRResult(statistic=-0.878777636514778, pvalue=2.988937083856355e-33)\n", "Slope and P-value = PearsonRResult(statistic=-0.9486988161767308, pvalue=8.343398880495621e-51)\n", "Slope and P-value = PearsonRResult(statistic=-0.6460944644672421, pvalue=3.897801106283307e-13)\n", "Slope and P-value = PearsonRResult(statistic=-0.864478293148363, pvalue=4.928923943595929e-31)\n", "Slope and P-value = PearsonRResult(statistic=-0.7660216180494925, pvalue=1.623253924564506e-20)\n", "Slope and P-value = PearsonRResult(statistic=0.7121305959723429, pvalue=9.827022432661657e-17)\n", "Slope and P-value = PearsonRResult(statistic=0.9111711636200237, pvalue=1.612723144780922e-39)\n", "Slope and P-value = PearsonRResult(statistic=0.9463558271242061, pvalue=7.0332275253388e-50)\n", "Slope and P-value = PearsonRResult(statistic=-0.6172510575548613, pvalue=7.958707864904192e-12)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'Cr','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns')\n", "\n", "feces=sample[sample.SampleType=='Feces'].copy()\n", "feces.loc[feces['Cr'] < feces.Cr.median(), 'new_Cr'] = 0\n", "feces.loc[feces['Cr'] >= feces.Cr.median(), 'new_Cr'] = 1 \n", "\n", "soil=sample[sample.SampleType=='Soil'].copy()\n", "soil.loc[soil['Cr'] < soil.Cr.median(), 'new_Cr'] = 0 \n", "soil.loc[soil['Cr'] >= soil.Cr.median(), 'new_Cr'] = 1 \n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'Cr', 'new_Cr'],axis='columns'),sample.new_Cr,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"Cr_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_Cr']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"Cr in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Cr','Cr','SampleType'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Cr_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "# print(\"Median:\",poultry.Cr.median())\n", "\n", "# poultry.loc[poultry['Cr'] < poultry.Cr.median(), 'new_Cr'] = 'Low' \n", "# poultry.loc[poultry['Cr'] >= poultry.Cr.median(), 'new_Cr'] = 'High' \n", "# print(\"Total sample count:\", poultry.Cr.value_counts().sum(),\"\\n\")\n" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "# poultry.groupby([\"new_Cr\"])[\"SampleType\"].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (35) Cu" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 1.8357\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 818\n", "0.0 817\n", "Name: new_Cu, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 637\n", "0.0 61\n", "Name: new_Cu, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.530082174563412, pvalue=1.3257338390176418e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.2057888314379761, pvalue=0.03997029652393968)\n", "Slope and P-value = PearsonRResult(statistic=-0.9475199987093451, pvalue=2.468596544975697e-50)\n", "Slope and P-value = PearsonRResult(statistic=-0.9167953710170094, pvalue=7.508708194705049e-41)\n", "Slope and P-value = PearsonRResult(statistic=-0.7803848148820774, pvalue=1.0636544518038897e-21)\n", "Slope and P-value = PearsonRResult(statistic=-0.9090555401836262, pvalue=4.851395573027206e-39)\n", "Slope and P-value = PearsonRResult(statistic=-0.8238864296314657, pvalue=6.607846756275421e-26)\n", "Slope and P-value = PearsonRResult(statistic=-0.6789263646839245, pvalue=0.0038282327183221756)\n", "Slope and P-value = PearsonRResult(statistic=-0.7357914961439604, pvalue=2.7901768340175173e-18)\n", "Slope and P-value = PearsonRResult(statistic=-0.5006075052075765, pvalue=1.1332297302387455e-07)\n", "0.0 644\n", "1.0 51\n", "Name: new_Cu, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.36725742277431683, pvalue=0.0001708440319396762)\n", "Slope and P-value = PearsonRResult(statistic=0.9222861313714397, pvalue=3.027648830881589e-42)\n", "Slope and P-value = PearsonRResult(statistic=-0.0012102291640043164, pvalue=0.9904653959741699)\n", "Slope and P-value = PearsonRResult(statistic=0.7689987879792735, pvalue=9.377549806270912e-21)\n", "Slope and P-value = PearsonRResult(statistic=-0.6565476054989122, pvalue=1.2046137788793693e-13)\n", "Slope and P-value = PearsonRResult(statistic=0.5799281340242602, pvalue=2.568851056588636e-10)\n", "Slope and P-value = PearsonRResult(statistic=-0.39062961653857914, pvalue=5.8683668970952764e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.5971731545975032, pvalue=5.4532113565984954e-11)\n", "Slope and P-value = PearsonRResult(statistic=0.8829764917472188, pvalue=2.5659501013286338e-30)\n", "Slope and P-value = PearsonRResult(statistic=-0.4823579560425172, pvalue=3.73835648598124e-07)\n" ] } ], "source": [ "print(\"Median:\",poultry.Cu.median())\n", "\n", "poultry.loc[poultry['Cu'] < poultry.Cu.median(), 'new_Cu'] = 0\n", "poultry.loc[poultry['Cu'] >= poultry.Cu.median(), 'new_Cu'] = 1 \n", "print(\"Total sample count:\", poultry.Cu.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_Cu.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_Cu','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_Cu'],axis='columns'),sample.new_Cu,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"Cu_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_Cu']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"Cu in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Cu','SampleType'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Cu_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (36) Fe" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 76.9956\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 818\n", "0.0 817\n", "Name: new_Fe, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 661\n", "0.0 37\n", "Name: new_Fe, dtype: int64\n" ] }, { "data": { 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.16806241612806366, pvalue=0.09464163204726318)\n", "Slope and P-value = PearsonRResult(statistic=-0.9144653753093864, pvalue=2.74466613662706e-40)\n", "Slope and P-value = PearsonRResult(statistic=-0.8880428410486153, pvalue=7.651571701662719e-35)\n", "Slope and P-value = PearsonRResult(statistic=-0.637905009736806, pvalue=2.8608063391739795e-09)\n", "Slope and P-value = PearsonRResult(statistic=-0.9350573588537439, pvalue=6.247763205165488e-46)\n", "Slope and P-value = PearsonRResult(statistic=-0.7058555854981938, pvalue=2.3816217332127e-16)\n", "Slope and P-value = PearsonRResult(statistic=-0.8885027311143697, pvalue=1.3642066562753645e-09)\n", "Slope and P-value = PearsonRResult(statistic=-1.0, pvalue=1.0)\n", "Slope and P-value = PearsonRResult(statistic=-0.4324873553119114, pvalue=7.003273198616538e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.3640256821719597, pvalue=0.00019680937980913783)\n", "0.0 663\n", "1.0 32\n", "Name: new_Fe, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.8131118846804525, pvalue=1.1557049978429842e-11)\n", "Slope and P-value = PearsonRResult(statistic=0.6687521885387553, pvalue=0.21712532353109745)\n", "Slope and P-value = PearsonRResult(statistic=0.8767633725304769, pvalue=6.373183187072947e-33)\n", "Slope and P-value = PearsonRResult(statistic=0.6742590730497817, pvalue=1.4770034593469506e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.9037050732121046, pvalue=7.001216815330801e-38)\n", "Slope and P-value = PearsonRResult(statistic=0.7377053451554358, pvalue=0.023282758202152476)\n", "Slope and P-value = PearsonRResult(statistic=0.8081889774258277, pvalue=0.001467189862618162)\n", "Slope and P-value = PearsonRResult(statistic=0.7553517242640319, pvalue=1.0870969702225547e-19)\n", "Slope and P-value = PearsonRResult(statistic=-0.6928549666419951, pvalue=1.3873642648859557e-15)\n", "Slope and P-value = PearsonRResult(statistic=0.8252565164540641, pvalue=2.8068246545623986e-17)\n" ] } ], "source": [ "print(\"Median:\",poultry.Fe.median())\n", "\n", "poultry.loc[poultry['Fe'] < poultry.Fe.median(), 'new_Fe'] = 0\n", "poultry.loc[poultry['Fe'] >= poultry.Fe.median(), 'new_Fe'] = 1\n", "print(\"Total sample count:\", poultry.Fe.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_Fe.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_Fe','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_Fe'],axis='columns'),sample.new_Fe,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"Fe_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_Fe']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"Fe in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Fe','SampleType'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Fe_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (37) K" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 1681.44\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 818\n", "0.0 817\n", "Name: new_K, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 683\n", "0.0 15\n", "Name: new_K, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.44878820793611257, pvalue=0.012861112637413992)\n", "Slope and P-value = PearsonRResult(statistic=-0.8897574525343723, pvalue=3.7481929633267414e-35)\n", "Slope and P-value = PearsonRResult(statistic=-0.6119826161082084, pvalue=4.4289086363037155e-11)\n", "Slope and P-value = PearsonRResult(statistic=-0.8921971762438186, pvalue=1.72254478813416e-17)\n", "Slope and P-value = PearsonRResult(statistic=-0.36956977824621584, pvalue=0.012470510272935125)\n", "Slope and P-value = PearsonRResult(statistic=-0.7086830325195077, pvalue=1.6028740873070593e-16)\n", "Slope and P-value = PearsonRResult(statistic=-0.9053693481006042, pvalue=5.376265049374785e-30)\n", "Slope and P-value = PearsonRResult(statistic=-0.8254475305991831, pvalue=4.446247514292358e-26)\n", "Slope and P-value = PearsonRResult(statistic=-0.7994074411282555, pvalue=2.064444177296625e-23)\n", "Slope and P-value = PearsonRResult(statistic=-0.11626349397775283, pvalue=0.38053640531776667)\n", "0.0 675\n", "1.0 20\n", "Name: new_K, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/home/mosesayoola/.local/lib/python3.10/site-packages/scipy/stats/_stats_py.py:4424: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n", " warnings.warn(stats.ConstantInputWarning(msg))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "Slope and P-value = PearsonRResult(statistic=0.8676060733964792, pvalue=1.6982238851120263e-31)\n", "Slope and P-value = PearsonRResult(statistic=0.8544676387873159, pvalue=2.1847184460448914e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.8265939310726589, pvalue=3.315467776646311e-26)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/mosesayoola/.local/lib/python3.10/site-packages/scipy/stats/_stats_py.py:4424: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n", " warnings.warn(stats.ConstantInputWarning(msg))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/mosesayoola/.local/lib/python3.10/site-packages/scipy/stats/_stats_py.py:4424: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n", " warnings.warn(stats.ConstantInputWarning(msg))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "Slope and P-value = PearsonRResult(statistic=0.705513837552105, pvalue=2.497591350749745e-16)\n", "Slope and P-value = PearsonRResult(statistic=0.8920866387102638, pvalue=1.1391468566099368e-21)\n", "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "Slope and P-value = PearsonRResult(statistic=0.8324147556646951, pvalue=0.03977392096027001)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/mosesayoola/.local/lib/python3.10/site-packages/scipy/stats/_stats_py.py:4424: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n", " warnings.warn(stats.ConstantInputWarning(msg))\n" ] } ], "source": [ "print(\"Median:\",poultry.K.median())\n", "\n", "poultry.loc[poultry['K'] < poultry.K.median(), 'new_K'] = 0\n", "poultry.loc[poultry['K'] >= poultry.K.median(), 'new_K'] = 1 \n", "print(\"Total sample count:\", poultry.K.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_K.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_K','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_K'],axis='columns'),sample.new_K,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"K_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_K']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"K in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_K','SampleType'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"K_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (38) Mg" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 736.531\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 818\n", "0.0 817\n", "Name: new_Mg, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 659\n", "0.0 39\n", "Name: new_Mg, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.9070950371175904, pvalue=1.3145808820930414e-38)\n", "Slope and P-value = PearsonRResult(statistic=-0.5634016291738035, pvalue=1.044694916584473e-09)\n", "Slope and P-value = PearsonRResult(statistic=-0.4791610173989314, pvalue=4.575417717086985e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.9236179700299728, pvalue=1.3408276159187858e-42)\n", "Slope and P-value = PearsonRResult(statistic=-0.3751267195636576, pvalue=0.0001202952281586855)\n", "Slope and P-value = PearsonRResult(statistic=-0.8257980066097038, pvalue=4.065640149968494e-26)\n", "Slope and P-value = PearsonRResult(statistic=-0.5616057558758832, pvalue=1.2111321833863216e-09)\n", "Slope and P-value = PearsonRResult(statistic=-0.983714260038989, pvalue=1.9505872895834856e-40)\n", "Slope and P-value = PearsonRResult(statistic=-0.7116527788064146, pvalue=1.0520964620837226e-16)\n", "Slope and P-value = PearsonRResult(statistic=-0.6813841706624395, pvalue=6.091592378692116e-15)\n", "0.0 651\n", "1.0 44\n", "Name: new_Mg, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.9658581912427372, pvalue=2.723270269633631e-59)\n", "Slope and P-value = PearsonRResult(statistic=0.745097363887008, pvalue=6.1815506434410305e-19)\n", "Slope and P-value = PearsonRResult(statistic=-0.40811845810486125, pvalue=2.4986449487960532e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.7917156119998932, pvalue=6.797081129752122e-08)\n", "Slope and P-value = PearsonRResult(statistic=0.5253153228628686, pvalue=0.0034310202897922253)\n", "Slope and P-value = PearsonRResult(statistic=-0.40541656385960206, pvalue=2.8599299162951612e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.9125888970322311, pvalue=7.59101215276124e-40)\n", "Slope and P-value = PearsonRResult(statistic=0.6743699061474255, pvalue=1.457068266904524e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.7402014189724263, pvalue=1.3769986306392892e-18)\n", "Slope and P-value = PearsonRResult(statistic=0.6500633084793461, pvalue=0.00870286604651678)\n" ] } ], "source": [ "print(\"Median:\",poultry.Mg.median())\n", "\n", "poultry.loc[poultry['Mg'] < poultry.Mg.median(), 'new_Mg'] = 0\n", "poultry.loc[poultry['Mg'] >= poultry.Mg.median(), 'new_Mg'] = 1 \n", "print(\"Total sample count:\", poultry.Mg.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_Mg.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_Mg','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_Mg'],axis='columns'),sample.new_Mg,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"Mg_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_Mg']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"Mg in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Mg','SampleType'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Mg_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (39) Mn" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 65.3722\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 818\n", "0.0 817\n", "Name: new_Mn, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 520\n", "0.0 178\n", "Name: new_Mn, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=-0.9504022978380812, pvalue=1.6616893135605476e-51)\n", "Slope and P-value = PearsonRResult(statistic=0.5100195703491349, pvalue=5.955851790831448e-08)\n", "0.0 505\n", "1.0 190\n", "Name: new_Mn, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.8872501771635812, pvalue=1.060055468557065e-34)\n", "Slope and P-value = PearsonRResult(statistic=0.9702329372375671, pvalue=3.651718779180612e-62)\n", "Slope and P-value = PearsonRResult(statistic=0.7940324969399668, pvalue=6.556808536070433e-23)\n", "Slope and P-value = PearsonRResult(statistic=-0.5474574703736312, pvalue=3.765932418176751e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.9648969429901649, pvalue=1.0374523487482219e-58)\n", "Slope and P-value = PearsonRResult(statistic=-0.9058842737197395, pvalue=2.4064655886400324e-38)\n", "Slope and P-value = PearsonRResult(statistic=0.45679579308400764, pvalue=1.7785250493542913e-06)\n", "Slope and P-value = PearsonRResult(statistic=0.8493341613375837, pvalue=6.038898109769242e-29)\n", "Slope and P-value = PearsonRResult(statistic=0.9781921622706703, pvalue=1.0562640236982775e-68)\n", "Slope and P-value = PearsonRResult(statistic=0.857100515789801, pvalue=5.496290106710883e-30)\n" ] } ], "source": [ "print(\"Median:\",poultry.Mn.median())\n", "\n", "poultry.loc[poultry['Mn'] < poultry.Mn.median(), 'new_Mn'] = 0\n", "poultry.loc[poultry['Mn'] >= poultry.Mn.median(), 'new_Mn'] = 1 \n", "print(\"Total sample count:\", poultry.Mn.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_Mn.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_Mn','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_Mn'],axis='columns'),sample.new_Mn,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"Mn_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_Mn']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"Mn in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Mn','SampleType'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Mn_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (40) Mo" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 878)\n", "Soil (695, 878) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 550\n", "0.0 148\n", "Name: new_Mo, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.5730735817157182, pvalue=1.388159179131337e-06)\n", "Slope and P-value = PearsonRResult(statistic=0.26064548415482264, pvalue=0.008815647608240005)\n", "Slope and P-value = PearsonRResult(statistic=-0.729074284886098, pvalue=7.965796361219398e-18)\n", "Slope and P-value = PearsonRResult(statistic=-0.9602067261979073, pvalue=4.324674775623422e-56)\n", "Slope and P-value = PearsonRResult(statistic=0.6170191201699883, pvalue=8.143990256891353e-12)\n", "Slope and P-value = PearsonRResult(statistic=0.08928937926160611, pvalue=0.3770017147607495)\n", "Slope and P-value = PearsonRResult(statistic=0.35626433537796576, pvalue=0.00027478340074276267)\n", "Slope and P-value = PearsonRResult(statistic=-0.07129299018506988, pvalue=0.48089471948290674)\n", "Slope and P-value = PearsonRResult(statistic=0.46316331668603755, pvalue=1.220168037807756e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.9022062714721262, pvalue=1.4380975095256788e-37)\n", "1.0 485\n", "0.0 210\n", "Name: new_Mo, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.8247136414816134, pvalue=5.359146792635217e-26)\n", "Slope and P-value = PearsonRResult(statistic=-0.8983183581112373, pvalue=8.825196135597715e-37)\n", "Slope and P-value = PearsonRResult(statistic=-0.8130524538369798, pvalue=9.31703626310026e-25)\n", "Slope and P-value = PearsonRResult(statistic=-0.9294949666691953, pvalue=3.0606644267983823e-44)\n", "Slope and P-value = PearsonRResult(statistic=-0.9356437031118423, pvalue=4.063493860826574e-46)\n", "Slope and P-value = PearsonRResult(statistic=-0.9878811370861544, pvalue=4.179932858383945e-81)\n", "Slope and P-value = PearsonRResult(statistic=-0.9427613160236776, pvalue=1.5474654339187353e-48)\n", "Slope and P-value = PearsonRResult(statistic=0.7688263370656886, pvalue=9.682548178939758e-21)\n", "Slope and P-value = PearsonRResult(statistic=-0.9556044128797611, pvalue=8.259406847464391e-54)\n", "Slope and P-value = PearsonRResult(statistic=-0.9200013871453474, pvalue=1.1843006360225443e-41)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'Mo','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns')\n", "\n", "feces=sample[sample.SampleType=='Feces'].copy()\n", "feces.loc[feces['Mo'] < feces.Mo.median(), 'new_Mo'] = 0\n", "feces.loc[feces['Mo'] >= feces.Mo.median(), 'new_Mo'] = 1 \n", "\n", "soil=sample[sample.SampleType=='Soil'].copy()\n", "soil.loc[soil['Mo'] < soil.Mo.median(), 'new_Mo'] = 0 \n", "soil.loc[soil['Mo'] >= soil.Mo.median(), 'new_Mo'] = 1 \n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'Mo', 'new_Mo'],axis='columns'),sample.new_Mo,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"Mo_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_Mo']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"Mo in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Mo','Mo','SampleType'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Mo_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (41) Na" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 138.332\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 818\n", "0.0 817\n", "Name: new_Na, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 661\n", "0.0 37\n", "Name: new_Na, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=-0.7523830212781536, pvalue=0.0012111848893719959)\n", "Slope and P-value = PearsonRResult(statistic=-0.6388068306229557, pvalue=8.607093669955015e-13)\n", "0.0 656\n", "1.0 39\n", "Name: new_Na, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.7900616617757824, pvalue=1.5066362575306774e-22)\n", "Slope and P-value = PearsonRResult(statistic=0.665465464489663, pvalue=4.26227196254955e-14)\n", "Slope and P-value = PearsonRResult(statistic=-0.5580492562442958, pvalue=1.6188717999931964e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.5320921211927768, pvalue=0.0029685633783614)\n", "Slope and P-value = PearsonRResult(statistic=-0.5686821627838259, pvalue=6.729357701076348e-10)\n", "Slope and P-value = PearsonRResult(statistic=0.5910085569862713, pvalue=9.590117440927222e-11)\n", "Slope and P-value = PearsonRResult(statistic=0.8191598060076024, pvalue=2.1422553135296127e-25)\n", "Slope and P-value = PearsonRResult(statistic=0.5828506495355855, pvalue=1.9880743087967224e-10)\n", "Slope and P-value = PearsonRResult(statistic=0.665570636455029, pvalue=0.03567793282715907)\n", "Slope and P-value = PearsonRResult(statistic=-0.7533709205443991, pvalue=1.5308999738186522e-19)\n" ] } ], "source": [ "print(\"Median:\",poultry.Na.median())\n", "\n", "poultry.loc[poultry['Na'] < poultry.Na.median(), 'new_Na'] = 0\n", "poultry.loc[poultry['Na'] >= poultry.Na.median(), 'new_Na'] = 1 \n", "print(\"Total sample count:\", poultry.Na.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_Na.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_Na','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_Na'],axis='columns'),sample.new_Na,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"Na_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_Na']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"Na in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Na','SampleType'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Na_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (42) Ni" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 878)\n", "Soil (695, 878) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 664\n", "0.0 34\n", "Name: new_Ni, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=-0.8477194967143897, pvalue=9.772808775017397e-29)\n", "Slope and P-value = PearsonRResult(statistic=-0.4790116349586089, pvalue=4.6185886629087476e-07)\n", "1.0 348\n", "0.0 347\n", "Name: new_Ni, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.900876128882463, pvalue=2.6978818107442055e-37)\n", "Slope and P-value = PearsonRResult(statistic=-0.9160794884520372, pvalue=1.1226859073771607e-40)\n", "Slope and P-value = PearsonRResult(statistic=-0.6751849278554397, pvalue=1.3182672304387091e-14)\n", "Slope and P-value = PearsonRResult(statistic=-0.8700862023167601, pvalue=7.154877684090777e-32)\n", "Slope and P-value = PearsonRResult(statistic=-0.7133162819819543, pvalue=8.291386074391868e-17)\n", "Slope and P-value = PearsonRResult(statistic=-0.8895480787190084, pvalue=4.0920545188968275e-35)\n", "Slope and P-value = PearsonRResult(statistic=-0.82656041072768, pvalue=3.344141293913884e-26)\n", "Slope and P-value = PearsonRResult(statistic=0.8178850854799434, pvalue=2.9247447581931662e-25)\n", "Slope and P-value = PearsonRResult(statistic=0.9122632408155086, pvalue=9.035697841758982e-40)\n", "Slope and P-value = PearsonRResult(statistic=-0.8782073208209595, pvalue=3.7086352454360014e-33)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'Ni','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns')\n", "\n", "feces=sample[sample.SampleType=='Feces'].copy()\n", "feces.loc[feces['Ni'] < feces.Ni.median(), 'new_Ni'] = 0\n", "feces.loc[feces['Ni'] >= feces.Ni.median(), 'new_Ni'] = 1 \n", "\n", "soil=sample[sample.SampleType=='Soil'].copy()\n", "soil.loc[soil['Ni'] < soil.Ni.median(), 'new_Ni'] = 0 \n", "soil.loc[soil['Ni'] >= soil.Ni.median(), 'new_Ni'] = 1 \n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'Ni', 'new_Ni'],axis='columns'),sample.new_Ni,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"Ni_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_Ni']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"Ni in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Ni','Ni','SampleType'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Ni_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (43) P" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 1077.89\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 818\n", "0.0 817\n", "Name: new_P, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 675\n", "0.0 23\n", "Name: new_P, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.6026455131160011, pvalue=0.029262459846669052)\n", "Slope and P-value = PearsonRResult(statistic=-0.5765973458819018, pvalue=3.4297473192030696e-10)\n", "Slope and P-value = PearsonRResult(statistic=-0.3052433062385325, pvalue=0.002014337528208807)\n", "Slope and P-value = PearsonRResult(statistic=-0.2082298867566767, pvalue=0.26096725660390807)\n", "Slope and P-value = PearsonRResult(statistic=-0.890989348135489, pvalue=2.2282757163923962e-35)\n", "Slope and P-value = PearsonRResult(statistic=-0.8729995318886798, pvalue=2.533181775345791e-32)\n", "Slope and P-value = PearsonRResult(statistic=-0.8303790252544383, pvalue=1.2388451409167366e-26)\n", "Slope and P-value = PearsonRResult(statistic=-0.8353018763093335, pvalue=4.022172170921918e-20)\n", "Slope and P-value = PearsonRResult(statistic=-0.9362598403784272, pvalue=1.5747281409857475e-22)\n", "Slope and P-value = PearsonRResult(statistic=-0.8063012373589966, pvalue=0.008664281784003417)\n", "0.0 676\n", "1.0 19\n", "Name: new_P, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.7859782288191053, pvalue=3.479625465914325e-22)\n", "Slope and P-value = PearsonRResult(statistic=0.01397742372709317, pvalue=0.8902217219854677)\n", "Slope and P-value = PearsonRResult(statistic=0.7950942395999142, pvalue=5.23284904461903e-23)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/mosesayoola/.local/lib/python3.10/site-packages/scipy/stats/_stats_py.py:4424: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n", " warnings.warn(stats.ConstantInputWarning(msg))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "Slope and P-value = PearsonRResult(statistic=0.9088338305026464, pvalue=5.4364331409104036e-39)\n", "Slope and P-value = PearsonRResult(statistic=0.6876325849749361, pvalue=2.743997985232017e-15)\n", "Slope and P-value = PearsonRResult(statistic=0.3241813082175623, pvalue=0.0010004832461705615)\n", "Slope and P-value = PearsonRResult(statistic=-0.6654654644896628, pvalue=4.26227196254969e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.7554671468818586, pvalue=0.13971104912097052)\n", "Slope and P-value = PearsonRResult(statistic=-0.4053900459476012, pvalue=2.8637066009566384e-05)\n" ] } ], "source": [ "print(\"Median:\",poultry.P.median())\n", "\n", "poultry.loc[poultry['P'] < poultry.P.median(), 'new_P'] = 0\n", "poultry.loc[poultry['P'] >= poultry.P.median(), 'new_P'] = 1 \n", "print(\"Total sample count:\", poultry.P.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_P.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_P','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_P'],axis='columns'),sample.new_P,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"P_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_P']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"P in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_P','SampleType'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"P_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (44) Pb" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 0.8343\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 818\n", "0.0 817\n", "Name: new_Pb, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 538\n", "0.0 160\n", "Name: new_Pb, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.20897261808825973, pvalue=0.036928892215746696)\n", "Slope and P-value = PearsonRResult(statistic=0.17095739133103838, pvalue=0.0890135797310042)\n", "Slope and P-value = PearsonRResult(statistic=-0.879126409505895, pvalue=2.618086791491587e-33)\n", "Slope and P-value = PearsonRResult(statistic=0.22449435873753376, pvalue=0.024738472008853245)\n", "Slope and P-value = PearsonRResult(statistic=-0.9217258390663119, pvalue=4.246532166431258e-42)\n", "Slope and P-value = PearsonRResult(statistic=0.5276536176187395, pvalue=1.6913786353961066e-08)\n", "Slope and P-value = PearsonRResult(statistic=-0.36371661072684613, pvalue=0.0001994749591287862)\n", "Slope and P-value = PearsonRResult(statistic=0.44620796264856294, pvalue=3.273130755064656e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.8992172790246937, pvalue=5.8399376247502525e-37)\n", "Slope and P-value = PearsonRResult(statistic=-0.842974873555643, pvalue=3.895765900215164e-28)\n", "0.0 464\n", "1.0 231\n", "Name: new_Pb, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.8722730875086541, pvalue=3.289582922018023e-32)\n", "Slope and P-value = PearsonRResult(statistic=0.9531027032883744, pvalue=1.141337952099974e-52)\n", "Slope and P-value = PearsonRResult(statistic=0.7495074107751944, pvalue=2.957605385388316e-19)\n", "Slope and P-value = PearsonRResult(statistic=-0.6285042900599419, pvalue=2.5461539119895797e-12)\n", "Slope and P-value = PearsonRResult(statistic=0.3107956441824549, pvalue=0.0016483783441793738)\n", "Slope and P-value = PearsonRResult(statistic=0.9741685963256704, pvalue=3.849745196563051e-65)\n", "Slope and P-value = PearsonRResult(statistic=0.7912959260038692, pvalue=1.165571599947312e-22)\n", "Slope and P-value = PearsonRResult(statistic=-0.8860195606120175, pvalue=1.750023534873691e-34)\n", "Slope and P-value = PearsonRResult(statistic=-0.6434242053829623, pvalue=5.223350206312095e-13)\n", "Slope and P-value = PearsonRResult(statistic=0.9639020225182308, pvalue=3.9846223164393354e-58)\n" ] } ], "source": [ "print(\"Median:\",poultry.Pb.median())\n", "\n", "poultry.loc[poultry['Pb'] < poultry.Pb.median(), 'new_Pb'] = 0\n", "poultry.loc[poultry['Pb'] >= poultry.Pb.median(), 'new_Pb'] = 1 \n", "print(\"Total sample count:\", poultry.Pb.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_Pb.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_Pb','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_Pb'],axis='columns'),sample.new_Pb,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"Pb_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_Pb']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"Pb in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Pb','SampleType'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Pb_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (45) S" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 955.479\n", "Total sample count: 818 \n", "\n", "Distribution:\n", "1.0 409\n", "0.0 409\n", "Name: new_S, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (0, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "0.0 356\n", "1.0 342\n", "Name: new_S, dtype: int64\n" ] }, { "data": { "image/png": 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fC3PtTpY0PG1/NfVbI2lChWsfIKlaUnXd4vnNvXVmZtaGFfrXd/rr/yhgTCo6AOgZEa9I+h5AROwlaTeyX8675OsBi4FJkh6IiOpc1yeSjUbsDXRNdSYAg4FBEXFMOn+fXJsLgfkRsVc6tmkj4Y+TVJe2R0TEr9P2uhFxQJqWuTgijpR0EVAVEeelvn8OPBwRZ0naBJgo6R+p/UHAZyJirqRrgGcioq+kzwE3p+v6VKyStgIuB/YD5qX71Rd4HLgBOCzd1y4tvN6LgC9FxBsp5uVExFBgKECHbj2ikb7MzMyWKSoZWV/S1LT9KDAMOBiYGBGvpPLewDUAEfGCpFeBUjIyNiLeBZB0V6qbT0Z6A6Miog54S9IjwP7Aew3EdCRwamknIuY1cg1HRMScCuV3pe+TyaaFKvkicJykQWm/I7Bd2h4bEXNz13FSiudhSZtJ2rhSrJIOA8ZHxDsAkm4FDgPqgAml+5rru7nX+zgwXNKfc9doZma2wopKRpZERK98gSSARfmiBtqX/+Vdvt9Q2/qoQj8t8X76Xkf991fASRHx4nKF0oE0fg+CyrHWd831XVd95fmyjssKI85O8R0NTJXUq5QQmpmZrYjVYc1IfSYAZwCk6ZntgNIv7y+k9RTrA33J/movb9tPUjtJm5ONEEwEFgCd6znfg8B5pZ0mTFs0R/l5/w6cr5SBSdqnnnb5e9AHmBMR79UT69PA4ZK6KlsAfBrwCPBkKt8h1S1N09R3vW9J2l3SOsAJueM7RcTTEXERMAfYtrk3wczMrJLVORn5HdBOUi1wO9A/IkqjDo8BfwKmAneWrRcBuBuYBtQADwM/iIh/p7KP0iLM75a1uQzYtLRIEziCho3TJ2/tvbmxusAepQWswP8A7YFpkqan/UouAaokTSNbfHtmfbFGxGzgR+lcNcCUiBidpm0GAHelurc3cr2DgfvJ7tvsXCxXlBbHkiVJNY1cs5mZWZMowmsNrXVVVVVFdXV5fmhmZm2ZpMkRUVXp2Oo8MmJmZmZtgD9YqwGSngY6lBV/LSJqi4jHzMxsbeRkpAERcWDRMZiZma3tPE1jZmZmhXIyYmZmZoVyMmJmZmaFcjJiZmZmhXIyYmZmZoVyMmJmZmaFcjJiZmZmhfLnjFirq31jPt0HP1B0GGa2Gpg55OiiQ7A1gEdGzMzMrFBORszMzKxQqyQZkTRI0gulx9VL+nor9TteUlXa/qukTSrUuUTSoEb66Stpj9aIKfW3j6SQ9KVG6v04t91d0vTWiqGBc24l6S9pu5ekr6zsc5qZmTVkpScjks4GvgAcEBE9gcMAtfZ5IuIrEfGfFjbvC7RaMgKcBjyWvn+KMusAP650vCGSVmidT0S8GREnp91egJMRMzMrVKPJiKQL06jGWEmjGhtlqODHwH9FxHsAETE/Ikakvi+SNCmNmAyVpFQ+XtLlkiZKmiHp0FS+vqTbJE2TdDuwfi7OmZK6pu2fSHpR0j+AXXN1vp3OVyPpTkkbSDoYOA64QtJUSTuVjbh0lTQzbe+ZYpqaYuhR4X4JOBnoD3xRUsdU3l3S85J+B0wBhgHrp75uTc3bSbpB0rOSHpS0fu5+/FzSI8B3JH1e0jOSaiX9UVKH3D34uaQnJVVL2lfS3yW9lJLCZSMwktYDLgX6pRj6lY8ipXrdJW0o6YF036ZL6lfhugekc1bXLZ7f5BeHmZlZg8lI+oV8ErAPcCJQ1ZzOJXUGOkfES/VUuTYi9k8jJusDx+SOrRsRBwADgYtT2TnA4oj4DPC/wH4VzrkfcGou5v1zh+9K59sbeB74ZkQ8AdwLfD8iejUQK8DZwNUR0YvsXsyqUOcQ4JXUz3iWH3nYFbg5IvaJiG8AS9I5z0jHewDXRcSewH/I7n3JJhFxOHAdMBzoFxF7kb0j6pxcvdcj4iDg0VTvZOCzZInHMhHxAXARcHuK4fYGrvvLwJsRsXf6WY0prxARQyOiKiKq2m2wcQNdmZmZLa+xkZHewOiIWBIRC4D7mtm/gGjg+BGSnpZUC3wO2DN37K70fTLQPW0fBtwCEBHTgGkV+jwUuDsiFqfRmHtzx3pKejSd74yy8zXFk8CPJf0Q2D4illSocxpwW9q+jeWnal6NiKca6P+ViJiatvPXDVBKFnZN9Wak/RFk96WkdL21wNMRsSAi3gGWVlpT00S1wJFptOrQiPDQh5mZtZrGkpEVWtuRkoFFknb8VMfZ9MXvgJPTX/g3AB1zVd5P3+tY/vNQGkpuGqszHDgvne9nZefL+4hP7s2yOhExkmxKZwnwd0mfyzeS1I5sNOOiNLVzDXBUGiECWNRI3O/ntsuvu9S2sZ9JqY+Py/r7mMY/VyZ/3ZCuPSU++5ElJb+QdFEj/ZiZmTVZY8nIY8CxkjpK6gS05NNrfgFcJ2kjAEkbSRrAJ7/k56S+T66vg5wJZCMaSOoJfKaeOiek9SWdgWNzxzoDsyW1L/WTLEjHSmbyyRTQsrhSUvVyRPyWbASi/PxHAjURsW1EdI+I7YE7yRbIVvJhiqU5XgC6S9o57X8NeKSZfZRUuu59ASTtC+yQtrcimx67BbiyVMfMzKw1NJiMRMQksl+6NWTTJtVAc4forwfGAZOUvXX1EbJfbP8hGw2pBe4BJjWxr06SpgE/ACZWiHkK2ZTGVLJE4NHc4QuBp4GxZL/US24Dvp8Whe5E9gv3HElPAF1z9foB0yVNBXYDbi47/WnA3WVldwKn13M9Q4FpuQWsjYqIpcA3gDvSdNPHwO+b2r7MOGCP0gLWFGuXdH3nAKWpoL2Aian8J8BlLTyfmZnZpyii4VkPSZ0iYqGkDchGHQakX/hmFVVVVUV1dXXRYZiZ2WpE0uSIqPhGmKZ8ZsVQZR8I1hEY4UTEzMzMWlOjyUhEfGqKQdJ1ZG9hzesB/LOs7OqIuKnl4ZmZmdnarkWf5hkR57Z2IGZmZtY2+UF5ZmZmVignI2ZmZlYoJyNmZmZWKCcjZmZmVignI2ZmZlYoJyNmZmZWKCcjZmZmVqgWfc6IWUNq35hP98EPFB2GmTXDzCEteQ6qWevwyIiZmZkVysmImZmZFapNJCOS6iRNlTRd0h3pCcRNbdtf0rX1HHuikbbdJZ2e26+S9NumR76s3UxJtekaaiUd39w+Uj/HSRqcti+RNChtD5d0ckv6NDMzW1FtIhkBlkREr4joCXwAnJ0/KKldSzqNiIMbqdIdWJaMRER1RFzQknMBR0REL+BkoNkJTTr/vRExpIXnNzMzWynaSjKS9yiws6Q+ksZJGgnUSuoo6aY08vCMpCNybbaVNEbSi5IuLhVKWpi+S9IVaeSlVlK/VGUIcGga0fhuOuf9qU2n3PmmSTqpifFvBMzLxXCPpMmSnpU0IFf+ZUlTJNVIeiiV1TvKk2s3U1LXtF0laXzaPjxdx9R0fzqXtRsgqVpSdd3i+U28FDMzszb2bhpJ6wJHAWNS0QFAz4h4RdL3ACJiL0m7AQ9K2iVfD1gMTJL0QERU57o+EegF7A10TXUmAIOBQRFxTDp/n1ybC4H5EbFXOrZpI+GPkyRgR+CUXPlZETFX0vrpvHeSJZk3AIela+vShNvTmEHAuRHxuKROwNL8wYgYCgwF6NCtR7TC+czMrI1oKyMj60uaClQDrwHDUvnEiHglbfcG/gQQES8ArwKlZGRsRLwbEUuAu1LdvN7AqIioi4i3gEeA/RuJ6UjgutJORMxroC5k0zQ9gb2Aa1NCAHCBpBrgKWBboAfwWWBC6doiYm4jfTfF48BVki4ANomIj1qhTzMzszYzMrIkrbdYJhtkYFG+qIH25X/pl+831LY+qtBPoyLiJUlvAXukhbhHAgdFxOI0pdKxpX0nH/FJktoxd94hkh4AvgI8JenIlLSZmZmtkLYyMtIUE4AzANL0zHbAi+nYFyR1SVMhfclGCcrb9pPUTtLmwGHARGAB0JnKHgTOK+00YZqmVG8LYAeykZuNgXkpEdmNbEQE4EngcEk7pDbNmaaZCeyXtpetY5G0U0TURsTlZCNMuzWjTzMzs3q1lZGRpvgd8HtJtWSjA/0j4v00gvIY2RTOzsDIsvUiAHcDBwE1ZCMSP4iIf0t6F/goTaMMB57JtbkMuE7SdKAO+BnZFFB9xkmqA9oDgyPiLUljgLMlTSNLnJ4CiIh30mLWuyStA7wNfKGJ9+FnwDBJPwaezpUPTIt664DngL/V18FeW29MtT/N0czMmkgRXmtorauqqiqqq8vzNTMza8skTY6IqkrHPE1jZmZmhfI0zWpE0tNAh7Lir0VEbRHxmJmZrQpORlYjEXFg0TGYmZmtap6mMTMzs0I5GTEzM7NCORkxMzOzQjkZMTMzs0I5GTEzM7NCORkxMzOzQvmtvdbqat+YT/fBDxQdhlmbN9OPZbA1hEdGzMzMrFBORszMzKxQTkZWkKRBkl6QNF1SjaSvFx2TmZnZmsTJyAqQdDbwBeCAiOgJHAaoFfr1Wh4zM2sz2nwyIunCNLIxVtIoSYOa0fzHwH9FxHsAETE/Ikakfj8v6RlJtZL+KKlDKp8p6XJJE9PXzql8uKSrJI0DLpfUS9JTkqZJulvSpqnezpL+kUZhpkjaSZkr0uhMraR+uev7QSqrkTSkgT76SLo/1+5aSf3T9hBJz6VYrlyR+21mZlauTf8FLqkKOAnYh+xeTAEmN7FtZ6BzRLxU4VhHYDjw+YiYIelm4BzgN6nKexFxQJrS+Q1wTCrfBTgyIuokTQPOj4hHJF0KXAwMBG4FhkTE3ek86wAnAr2AvYGuwCRJE1JZX+DAiFgsqUs6T6U+tq3nOrsAJwC7RURI2qSeegOAAQDtNtq8gTtnZma2vLY+MtIbGB0RSyJiAXBfM9oKiHqO7Qq8EhEz0v4IsimcklG57wflyu9IicjGwCYR8Ui+fUqAto6IuwEiYmlELE7XMSoi6iLiLeARYH/gSOCmVIeImNtAH/V5D1gK3CjpRKBi3YgYGhFVEVHVboONG+jOzMxseW09GWnx+o40NbNI0o4t6Dfq2V7USLv6+m2ovDxhqq/uRyz/eugIEBEfAQcAd5KNsoxpJEYzM7NmaevJyGPAsZI6SuoENPcTgn4BXCdpIwBJG6XpiheA7qX1IMDXyEYrSvrlvj9Z3mlEzAfmSTo03z4lQLMk9U3n6yBpA2AC0E9SO0mbk43CTAQeBM5KdZDUpYE+XgX2SPsbA59PxzsBG0fEX8mmiXo18x6ZmZk1qE2vGYmISZLuBWrIfhlXA/Ob0cX1QCeyNRofAh8Cv4qIpZK+AdyR3hkzCfh9rl0HSU+TJYOn1dP3mcDvU6LwMvCNVP414A9pHcmHwFeBu8mme2rIRkJ+EBH/BsZI6gVUS/oA+CvZottP9RERL0v6MzAN+CfwTDpfZ2B0Wlsi4LvNuD9mZmaNUkR9yx7aBkmdImJhboRhQERMWYnnmwlURcSclXWOolVVVUV1dXXRYZiZ2WpE0uSIqKp0rE2PjCRDJe1BtkZixMpMRMzMzOzT2nwyEhGn5/clXQccUlatB9nURd7VEXFTC87XvbltzMzM1mZtPhkpFxHnFh2DmZlZW9LW301jZmZmBXMyYmZmZoVyMmJmZmaFcjJiZmZmhXIyYmZmZoVyMmJmZmaFcjJiZmZmhfLnjFirq31jPt0HP1B0GGZt1swhzX3mp1mxPDJiZmZmhXIyYmZmZoVyMrISKPNTSf+UNEPSOEl7tqCfrST9JW33kXR/2u4v6dpWjvmJ1uzPzMysqbxmZOU4FzgY2DsiFkv6InCvpD0jYmlTO4mIN4GTV1aQZec6uLxMUruIqFsV5zczs7bLIyP1kHShpBckjZU0StKgZjT/IXB+RCwGiIgHgSeAMyS1kzRc0nRJtZK+m863s6R/SKqRNEXSTpK6S5reSJzbS3pI0rT0fbtU/tV0jhpJE1JZf0mjJY2R9KKki3P9LEzf+6SRnJFAbSq7R9JkSc9KGlBPHAMkVUuqrls8vxm3yszM2jqPjFQgqQo4CdiH7B5NASY3se1GwIYR8VLZoWpgT6AXsHVE9Ez1N0nHbwWGRMTdkjqSJYpbNOGU1wI3R8QISWcBvwX6AhcBX4qIN3LnADgA6AksBiZJeiAiqsv6PADoGRGvpP2zImKupPVTmzsj4t18g4gYCgwF6NCtRzQhbjMzM8AjI/XpDYyOiCURsQC4rxX6FBDAy8COkq6R9GXgPUmdyRKUuwEiYmlpVKUJDgJGpu0/pdgBHgeGS/o20C5Xf2xEvBsRS4C7cvXzJuYSEYALJNUATwHbAj2aGJuZmVmjnIxUppY2jIj3gEWSdiw7tC/wXETMA/YGxpOtLblxRc5XKYQUx9nAT8mSh6mSNssfL69fZlFpQ1If4EjgoIjYG3gG6NiK8ZqZWRvnZKSyx4BjJXWU1Alo7icIXQH8Nk1rIOlIshGIkZK6AutExJ3AhcC+KYGZJalvqt9B0gZNPNcTwKlp+4wUO5J2ioinI+IiYA5ZUgLwBUldUmx9yUZQGrIxMC8txN0N+GwT4zIzM2sSrxmpICImSboXqAFeJVvv0ZxVmdcAmwK1kuqAfwPHR8QSSbsAN0kqJYI/St+/BvxB0qXAh8BXgY+bcK4LgD9K+j7wDvCNVH6FpB5koy4PpWvpRZas/AnYGRhZYb1IuTHA2ZKmAS+STdWYmZm1GkV4rWElkjpFxMI0QjEBGBARU4qOa0VI6g9URcR5K/M8VVVVUV3dWI5jZmZtiaTJEVFV6ZhHRuo3VNIeZOsjRqzpiYiZmdnqyslIPSLi9Py+pOuAQ8qq9QD+WVZ2dUTctDJja6mIGA4MLzgMMzOz5TgZaaKIOLfoGMzMzNZGfjeNmZmZFcrJiJmZmRXKyYiZmZkVysmImZmZFcrJiJmZmRXKyYiZmZkVysmImZmZFcqfM2KtrvaN+XQf/EDRYZi1CTOHNPc5nmarH4+MmJmZWaGcjJiZmVmhCktGJNVJmippuqQ70tNxm9q2v6Rr6zn2RCNtu0s6PbdfJem3TY98WbuZkro2t11ZHwObct2SFq7IeXL9PJG+d5c0PW33kXR/a/RvZmbWEkWOjCyJiF4R0RP4ADg7f1BSu5Z0GhEHN1KlO7AsGYmI6oi4oCXnagUDgSYnYSuqCffGzMxslVtdpmkeBXZOf6WPkzQSqJXUUdJNkmolPSPpiFybbSWNkfSipItLhaVRBGWuSCMvtZL6pSpDgEPTqMx38yMDkjrlzjdN0knNuQhJB0h6IsX6hKRdU3k7SVfm+j1f0gXAVsA4SeNSvdNSnemSLi/r+1eSpkh6SNLmqezbkiZJqpF0Z2mURdKWku5O5TWSDs7fmwbiv0TSoNz+9DSKsqGkB1Jf03P3Mt92gKRqSdV1i+c357aZmVkbV/i7aSStCxwFjElFBwA9I+IVSd8DiIi9JO0GPChpl3w9YDEwSdIDEVGd6/pEoBewN9A11ZkADAYGRcQx6fx9cm0uBOZHxF7p2KbNvJwXgMMi4iNJRwI/B04CBgA7APukY10iYq6k/waOiIg5krYCLgf2A+ala+0bEfcAGwJTIuJ7ki4CLgbOA+6KiBtSrJcB3wSuAX4LPBIRJ6QRpk7NvI5yXwbejIij07k2Lq8QEUOBoQAduvWIFTyfmZm1IUWOjKwvaSpQDbwGDEvlEyPilbTdG/gTQES8ALwKlJKRsRHxbkQsAe5KdfN6A6Mioi4i3gIeAfZvJKYjgetKOxExr5nXtDFwR1qP8Wtgz1y/v4+Ij1K/cyu03R8YHxHvpHq3AoelYx8Dt6ftW/jkWntKelRSLXBG7nyfA65P56qLiBUdqqgFjpR0uaRDW6E/MzOzZYocGVkSEb3yBZIAFuWLGmhf/td3+X5DbeujCv00x/8A49KIRHdgfDP6bU68pb6GA30jokZSf6BPM/qo5COWT1A7AkTEDEn7AV8BfiHpwYi4dAXPZWZmBqw+a0bqM4HsL37S9Mx2wIvp2BckdZG0PtAXeLxC235pvcbmZKMME4EFQOd6zvcg2fQH6ZzNnabZGHgjbfcv6/fsNCWFpC6pPB/L08DhkrqmqZXTyEZzIPs5nZy2TwceS9udgdmS2pPuU/IQcE46VztJGzUx/pnAvqndvmRTS6QppMURcQtwZamOmZlZa1jdk5HfAe3SNMTtQP+IeD8de4xsCmcqcGfZehGAu4FpQA3wMPCDiPh3KvsoLcb8blmby4BN0yLNGuAIGjZN0qz0dRXwS7KRg8eB/LuBbiSbipqW+i29m2co8DdJ4yJiNvAjYFyKeUpEjE71FgF7SppMNgVTGpW4kCyJGUu2XqXkO8AR6b5N5pPpm8bcCXRJ02fnADNS+V7AxFT+E7L7ZGZm1ioU4bWG1rqqqqqiuro8NzQzs7ZM0uSIqKp0bHUfGTEzM7O1XOFv7V3dSXoa6FBW/LWIqC0iHjMzs7WNk5FGRMSBRcdgZma2NnMyYmZmq5UPP/yQWbNmsXTp0qJDsRbo2LEj22yzDe3bt29yGycjZma2Wpk1axadO3eme/fupc+fsjVERPDuu+8ya9Ysdthhhya38wJWMzNbrSxdupTNNtvMicgaSBKbbbZZs0e1nIyYmdlqx4nImqslPzsnI2ZmZlYorxkxM7PVWvfBD7RqfzOHHN2kenfffTcnnngizz//PLvtthsA48eP58orr+T+++9fVq9///4cc8wxnHzyyfTp04fZs2fTsWNH1ltvPW644QZ69eoFwPz58zn//PN5/PHs6SWHHHII11xzDRtvnD0IfcaMGQwcOJAZM2bQvn179tprL6655hq23HLLFl/r3Llz6devHzNnzqR79+78+c9/ZtNNl3/SyYsvvki/fv2W7b/88stceumlDBw4EIBrrrmGa6+9lnXXXZejjz6aX/7yl9TW1vKrX/2K4cOHtzi2PCcj1upq35jf6v95mNknmvrL1FbMqFGj6N27N7fddhuXXHJJk9vdeuutVFVVcdNNN/H973+fsWPHAvDNb36Tnj17cvPNNwNw8cUX861vfYs77riDpUuXcvTRR3PVVVdx7LHHAjBu3DjeeeedFUpGhgwZwuc//3kGDx7MkCFDGDJkCJdffvlydXbddVemTp0KQF1dHVtvvTUnnHDCshhGjx7NtGnT6NChA2+//TYAe+21F7NmzeK1115ju+22a3F8JZ6mMTMzK7Nw4UIef/xxhg0bxm233daiPg466CDeeCN7duq//vUvJk+ezIUXXrjs+EUXXUR1dTUvvfQSI0eO5KCDDlqWiAAcccQR9OzZc4WuY/To0Zx55pkAnHnmmdxzzz0N1n/ooYfYaaed2H777QG4/vrrGTx4MB06ZJ/9ucUWWyyre+yxx7b43pRzMmJmZlbmnnvu4ctf/jK77LILXbp0YcqUKc3uY8yYMfTt2xeA5557jl69etGu3SfPUG3Xrh29evXi2WefZfr06ey3336N9rlgwQJ69epV8eu55577VP233nqLbt26AdCtW7dlIxv1ue222zjttNOW7c+YMYNHH32UAw88kMMPP5xJkyYtO1ZVVcWjjz7aaMxN4WkaMzOzMqNGjVq2ZuLUU09l1KhR7LvvvvW+UyRffsYZZ7Bo0SLq6uqWJTERUbFtfeX16dy587Ipldb2wQcfcO+99/KLX/xiWdlHH33EvHnzeOqpp5g0aRKnnHIKL7/8MpLYYostePPNN1vl3E5GAEmDgG8BHwF1wK8i4uZmtO8FbBURf22k3iXAwoi4suXRgqS+wIyI+HQa3HC744A9ImLIipzfzGxt9u677/Lwww8zffp0JFFXV4ckfvnLX7LZZpsxb9685erPnTuXrl27Ltu/9dZb2XvvvRk8eDDnnnsud911F3vuuSfPPPMMH3/8Meusk01KfPzxx9TU1LD77rvz9ttv88gjjzQa24IFCzj00EMrHhs5ciR77LHHcmVbbrkls2fPplu3bsyePXu5aZZyf/vb39h3332XW6OyzTbbcOKJJyKJAw44gHXWWYc5c+aw+eabs3TpUtZff/1GY26KNj9NI+ls4AvAARHREzgMaHKaKmldoBfwlZUSYGV9gT0aq5Qnad2IuNeJiJlZw/7yl7/w9a9/nVdffZWZM2fy+uuvs8MOO/DYY4/Ro0cP3nzzTZ5//nkAXn31VWpqapa9Y6akffv2XHbZZTz11FM8//zz7Lzzzuyzzz5cdtlly+pcdtll7Lvvvuy8886cfvrpPPHEEzzwwCeL/8eMGUNt7fLPZC2NjFT6Kk9EAI477jhGjBgBwIgRIzj++OPrve5Ro0YtN0UD0LdvXx5++GEgm7L54IMPliVeM2bMWOE1LSVrxciIpAuBM4DXgTnA5GaMPvwYOCIi3gOIiPnAiNTvTKAqIuZIqgKujIg+aYRjK6B7Ol9vYH1JvYFfAGOBPwI7AouBARExLZ1vb0kPA9sCv4yIGyR1AkYDmwLtgZ9GxOgUw9eBQUAA04DrgeOAwyX9FDgp9XsdsHk637cj4gVJw4G5wD7AFEm16XrOS8fuj4i/pPMsjIhOkvoAPwPeIkuy7gJqge8A6wN9I+KlCj+DAcAAgHYbbd7EW29m1rhV/e6hUaNGMXjw4OXKTjrpJEaOHMmhhx7KLbfcwje+8Q2WLl1K+/btufHGG5e9PTdv/fXX53vf+x5XXnklw4YNY9iwYZx//vnsvPPORAQHHXQQw4YNW1b3/vvvZ+DAgQwcOJD27dvzmc98hquvvnqFrmXw4MGccsopDBs2jO2224477rgDgDfffJNvfetb/PWv2YD+4sWLGTt2LH/4wx+Wa3/WWWdx1lln0bNnT9Zbbz1GjBixbFpp3LhxHH106/xsFBGt0lFRUpJwI3AQWXI1BfhDU5IRSZ2B1yJi03qOz6T+ZORYoHdELJHUP9U7L7W7BpgTET+T9DngqojoldqdAHwW2BB4BjgQeBvYICLek9QVeAroQTb6cRdwSIqhS0TMrZBIPAScHRH/lHQg8IuI+Fyq1xU4PiLq8nE2kozcA+xOlsi8DNwYERdL+g6wQ0QMbOi+dujWI7qd+ZvGbr+ZtdDa/tbe559/nt13373oMKwB77//PocffjiPPfYY66776XGNSj9DSZMjoqpSf2vDyEhvYHRELAGQdF8z2opsxKEl7i2ds56YTgKIiIclbSaplDaXYl0iaRxwAPAA8HNJhwEfA1sDWwKfA/4SEXNSX3M/dQHZqMrBwB25RVAdclXuiIi6Zl7bpIiYnfp/CXgwldcCRzSzLzMzW8u89tprDBkypGIi0hJrQzLS4gcYpJGIRZJ2jIiXK1T5iE/W1XQsO7aomTFF2fd8+RlkUyz7RcSHaUSmI01LltYB/hMRveo5Xl+cy65NWRazXu7Y+7ntj3P7H7N2vGbMzGwF9OjRgx49erRaf2vDAtbHgGMldUyjBM0dv/wFcJ2kjQAkbZTWPwDMBEpv/D6pQtuSBUDn3P4EsgSDNO0xp7QmBTg+xboZ0AeYBGwMvJ0SkSOA7VPdh4BTUl0kdSk/X+r3FUlfTXUkae8mXHf+2o4nW6tiZrZaWNOXELRlLfnZrfF/5UbEJEn3AjXAq0A1ML8ZXVwPdAImSfoQ+BD4VTr2M2CYpB8DTzfQxzhgsKSpZMnNJcBNkqaRLSg9M1d3Itm0zHbA/0TEm5JuBe6TVA1MBV5I1/aspP8FHpFUR7bGpD9wG3CDpAuAk8kSn+vTgtb26XhNI9d9AzBa0kSypKehkZ5m2Wvrjaley+e0zWzl6dixI++++y6bbbaZn967hokI3n33XTp2LJ9MaNgav4AVsnUTEbFQ0gZkoxIDIqL5H5dnraKqqiqqq6uLDsPM1lAffvghs2bNYunSpUWHYi3QsWNHttlmG9q3X37AfW1fwAowVNIeZOssRjgRMTNbc7Vv354ddtih6DBsFVorkpGIOD2/L+k64JCyaj2Af5aVXR0RN63M2MzMzKxha0UyUi4izi06BjMzM2uateHdNGZmZrYGWysWsNrqRdIC4MWi42hjupI9msBWHd/zVc/3fNVrzXu+fURUfF7IWjlNY4V7sb4V07ZySKr2PV+1fM9XPd/zVW9V3XNP05iZmVmhnIyYmZlZoZyM2MowtOgA2iDf81XP93zV8z1f9VbJPfcCVjMzMyuUR0bMzMysUE5GzMzMrFBORqxVSfqypBcl/UvS4KLjWRtJ2lbSOEnPS3pW0ndS+SWS3pA0NX19pehY1yaSZkqqTfe2OpV1kTRW0j/T902LjnNtIWnX3Gt5qqT3JA3067x1SfqjpLclTc+V1fu6lvSj9P/7i5K+1GpxeM2ItRZJ7YAZwBeAWcAk4LSIeK7QwNYykroB3SJiiqTOwGSgL3AKsDAiriwyvrWVpJlAVUTMyZX9EpgbEUNS8r1pRPywqBjXVun/ljeAA4Fv4Nd5q5F0GLAQuDkieqayiq/r9EDaUcABwFbAP4BdIqJuRePwyIi1pgOAf0XEyxHxAXAbcHzBMa11ImJ26cnUEbEAeB7Yutio2qzjgRFpewRZUmit7/PASxHxatGBrG0iYgIwt6y4vtf18cBtEfF+RLwC/Ivs//0V5mTEWtPWwOu5/Vn4l+RKJak7sA/wdCo6T9K0NPTqKYPWFcCDkiZLGpDKtoyI2ZAlicAWhUW3djuV7C/yEr/OV676Xtcr7f94JyPWmlShzPOAK4mkTsCdwMCIeA+4HtgJ6AXMBn5VXHRrpUMiYl/gKODcNLxtK5mk9YDjgDtSkV/nxVlp/8c7GbHWNAvYNre/DfBmQbGs1SS1J0tEbo2IuwAi4q2IqIuIj4EbaKXhU8tExJvp+9vA3WT39620hqe0luft4iJcax0FTImIt8Cv81Wkvtf1Svs/3smItaZJQA9JO6S/Zk4F7i04prWOJAHDgOcj4qpcebdctROA6eVtrWUkbZgWCyNpQ+CLZPf3XuDMVO1MYHQxEa7VTiM3RePX+SpR3+v6XuBUSR0k7QD0ACa2xgn9bhprVeltdr8B2gF/jIj/LTaitY+k3sCjQC3wcSr+Mdl/2r3Ihk1nAv+vNO9rK0bSjmSjIZA97XxkRPyvpM2APwPbAa8BX42I8sWA1kKSNiBbo7BjRMxPZX/Cr/NWI2kU0AfoCrwFXAzcQz2va0k/Ac4CPiKbIv5bq8ThZMTMzMyK5GkaMzMzK5STETMzMyuUkxEzMzMrlJMRMzMzK5STETMzMyuUkxGzNZSkuvTU0umS7pO0SSP1L5E0qJE6fdPDsEr7l0o6shViHS7p5BXtp5nnHJjeGrrakLRb+pk9I2mnsmMzJT1aVja19DRVSVWSftsKMXTPP6G17NiN+Z//yiZpS0kjJb2cPmb/SUknrKrz2+rDyYjZmmtJRPRKT9qcC5zbCn32BZb9MoqIiyLiH63Q7yqVnvI6EFitkhGy+zs6IvaJiJcqHO8saVsASbvnD0REdURc0NQTpXvQLBHxrVX1lO304X33ABMiYseI2I/sgxK3WcnnXXdl9m8t42TEbO3wJOmBVZJ2kjQm/aX5qKTdyitL+rakSZJqJN0paQNJB5M9A+SK9Bf5TqURDUlHSfpzrn0fSfel7S+mv2inSLojPTOnXmkE4OepTbWkfSX9XdJLks7O9T9B0t2SnpP0e0nrpGOnSapNI0KX5/pdmEZyngZ+QvaI83GSxqXj16fzPSvpZ2Xx/CzFX1u6X5I6SboplU2TdFJTr1dSL0lPpXZ3S9o0fSDgQOBbpZgq+DPQL22Xf/JoH0n3NxJb/h4cJOm/032aLmlg7jzrShqR2v6lNIIkabykqibc58vT6+sfkg5I7V6WdFyq007SFek1Nk3S/6twrZ8DPoiI35cKIuLViLimoT7SfRif4n5B0q0psUHSfpIeSbH9XZ98pPn49Jp7BPiOpGMlPa1shOofkras5+dhq0pE+Mtf/loDv4CF6Xs7soeIfTntPwT0SNsHAg+n7UuAQWl7s1w/lwHnp+3hwMm5Y8OBk8k+dfQ1YMNUfj3wf8k+tXFCrvyHwEUVYl3WL9mnZp6Ttn8NTAM6A5sDb6fyPsBSYMd0fWNTHFulODZPMT0M9E1tAjgld86ZQNfcfpfc/RoPfCZXr3T9/wXcmLYvB36Ta79pM653GnB42r601E/+Z1ChzUxgF+CJtP8M2SjV9Nw9ub++2MrvAbAf2af0bgh0Ap4le8Jz91TvkFTvj3zyuhgPVDXhPh+Vtu8GHgTaA3sDU1P5AOCnabsDUA3sUHa9FwC/buD1XbGPdB/mk42grEOWiPdOMTwBbJ7a9CP7FOjSdf2u7GdZ+tDPbwG/Kvrfc1v/8nCV2ZprfUlTyX65TAbGpr/SDwbuSH8sQvYfebmeki4DNiH7RfX3hk4UER9JGgMcK+kvwNHAD4DDyX5hPp7Otx7ZL4fGlJ5ZVAt0iogFwAJJS/XJ2peJEfEyLPvI6t7Ah8D4iHgnld8KHEY23F9H9vDA+pwiaQDZL9duKe5p6dhd6ftk4MS0fSTZtEHpHsyTdExj1ytpY2CTiHgkFY3gkyfONmYuME/SqcDzwOJ66n0qtrSZvwe9gbsjYlGK6y7gULJ7/3pEPJ7q3UKWGFyZ639/6r/PHwBjUr1a4P2I+FBSLdlrEbJn93xGn6wT2pjsOSav1Hfhkq5LMX8QEfs30McHZK+NWand1HTe/wA9yf4dQJZ05j8m/vbc9jbA7WnkZL2G4rJVw8mI2ZprSUT0Sr/87idbMzIc+E9E9Gqk7XCyv3RrJPUn+2uzMbenc8wFJkXEgjQ8PjYiTmtm7O+n7x/ntkv7pf+Xyp9VEVR+hHnJ0oioq3RA2UO9BgH7p6RiONCxQjx1ufOrQgwtvd7muB24DujfQJ1KscHy96Che1Xp3pb3X58PIw0pkPv5RcTH+mQ9hshGmxpKcp8FTloWQMS5krqSjYDU24ekPiz/min9zAQ8GxEH1XO+Rbnta4CrIuLe1N8lDcRpq4DXjJit4SJ7gNgFZL9slwCvSPoqZIsEJe1doVlnYLak9sAZufIF6Vgl44F9gW/zyV+ZTwGHSNo5nW8DSbus2BUtc4CyJ0CvQzbk/hjwNHC4pK7KFmieBjxST/v8tWxE9stoflofcFQTzv8gcF5pR9KmNOF6089jnqRDU9HXGoixkruBX9LwaFWl2MpNAPqmGDcke8Jt6d0620kq/dI+jeze5jXnPlfyd+Cc9PpC0i4phryHgY6SzsmV5RccN6WPvBeBzUvXJam9pD3rqbsx8EbaPrOeOrYKORkxWwtExDNADdnQ/RnANyXVkP31eXyFJheS/cIZC7yQK78N+L4qvPU0/cV9P9kv8vtT2Ttkf8GPkjSN7Jf1pxbMttCTwBCyR8S/QjblMBv4ETCO7HqnRMToetoPBf4maVxE1JCtwXiWbI3E4/W0ybsM2DQt4KwBjmjG9Z5JthB4GtkTZi9twvkAiIgFEXF5RHzQnNgq9DOFbARsItnP+sb0OoFsCujMFF8XsjVA+bbNuc+V3Ag8B0xR9jbiP1A2Ep9GV/qSJT2vSJpINqX1w6b2UdbfB2Trii5P92Qq2ZRlJZeQTWU+CsxpxnXZSuKn9prZaicNnQ+KiGMKDsXMVgGPjJiZmVmhPDJiZmZmhfLIiJmZmRXKyYiZmZkVysmImZmZFcrJiJmZmRXKyYiZmZkV6v8Dg7b9f39wxtsAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.8120041781956535, pvalue=1.1926334011424672e-24)\n", "Slope and P-value = PearsonRResult(statistic=-0.9113196302922778, pvalue=1.4912360684936217e-39)\n", "Slope and P-value = PearsonRResult(statistic=0.8566216648327657, pvalue=6.397477174406691e-30)\n", "Slope and P-value = PearsonRResult(statistic=-0.05465806115178219, pvalue=0.5891192766628081)\n", "Slope and P-value = PearsonRResult(statistic=-0.8713476931342374, pvalue=4.578172822606874e-32)\n", "Slope and P-value = PearsonRResult(statistic=0.6625104033547864, pvalue=6.037424174513312e-14)\n", "Slope and P-value = PearsonRResult(statistic=-0.3133205619662843, pvalue=0.0015028202611292029)\n", "Slope and P-value = PearsonRResult(statistic=-0.44020787853273846, pvalue=4.583413254412689e-06)\n", "Slope and P-value = PearsonRResult(statistic=0.1611070366550119, pvalue=0.10931175467350218)\n", "Slope and P-value = PearsonRResult(statistic=0.7783348414655431, pvalue=1.5889418064556324e-21)\n" ] } ], "source": [ "print(\"Median:\",poultry.S.median())\n", "\n", "poultry.loc[poultry['S'] < poultry.S.median(), 'new_S'] = 0\n", "poultry.loc[poultry['S'] >= poultry.S.median(), 'new_S'] = 1 \n", "print(\"Total sample count:\", poultry.S.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_S.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_S','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_S'],axis='columns'),sample.new_S,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"S_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_S']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"S in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_S','SampleType'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"S_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (46) Si" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 291.454\n", "Total sample count: 818 \n", "\n", "Distribution:\n", "1.0 409\n", "0.0 409\n", "Name: new_Si, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (0, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 373\n", "0.0 325\n", "Name: new_Si, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.5145574207670818, pvalue=4.337195103521798e-08)\n", "Slope and P-value = PearsonRResult(statistic=-0.3346432326970313, pvalue=0.0006664315899471912)\n", "Slope and P-value = PearsonRResult(statistic=-0.7483327458364105, pvalue=3.604662263578837e-19)\n", "Slope and P-value = PearsonRResult(statistic=0.5639458311134429, pvalue=9.987542144245744e-10)\n", "Slope and P-value = PearsonRResult(statistic=0.66597934105462, pvalue=4.010249076683856e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.8718551868046944, pvalue=3.820369980658082e-32)\n", "Slope and P-value = PearsonRResult(statistic=0.6773627030435047, pvalue=1.0072825252847535e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.3080112428655497, pvalue=0.0018236085400905756)\n", "Slope and P-value = PearsonRResult(statistic=-0.6998870053559143, pvalue=5.411475751064859e-16)\n", "Slope and P-value = PearsonRResult(statistic=-0.7554099689319391, pvalue=1.0761556386965367e-19)\n" ] } ], "source": [ "print(\"Median:\",poultry.Si.median())\n", "\n", "poultry.loc[poultry['Si'] < poultry.Si.median(), 'new_Si'] = 0\n", "poultry.loc[poultry['Si'] >= poultry.Si.median(), 'new_Si'] = 1 \n", "print(\"Total sample count:\", poultry.Si.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_Si.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_Si','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_Si'],axis='columns'),sample.new_Si,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", " # mylist2.append([f\"Si_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_Si']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"Si in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Si','SampleType'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Si_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (47) Zn" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 27.1597\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 818\n", "0.0 817\n", "Name: new_Zn, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 598\n", "0.0 100\n", "Name: new_Zn, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.1621506143588638, pvalue=0.10700420298291151)\n", "Slope and P-value = PearsonRResult(statistic=-0.506643322849102, pvalue=7.518457436401827e-08)\n", "Slope and P-value = PearsonRResult(statistic=-0.4697214207373917, pvalue=0.00022752523959542118)\n", "Slope and P-value = PearsonRResult(statistic=-0.2899954438326629, pvalue=0.0286572444499429)\n", "Slope and P-value = PearsonRResult(statistic=-0.7913204196443056, pvalue=1.1596295685280737e-22)\n", "Slope and P-value = PearsonRResult(statistic=0.2161209376519591, pvalue=0.03080134985083897)\n", "Slope and P-value = PearsonRResult(statistic=-0.4931219830440069, pvalue=1.6289308120596076e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.9466463280796985, pvalue=5.428083803542795e-50)\n", "Slope and P-value = PearsonRResult(statistic=-0.9188892542407201, pvalue=2.2669709783256156e-41)\n", "Slope and P-value = PearsonRResult(statistic=-0.9514742130704547, pvalue=5.8452593472744354e-52)\n", "0.0 595\n", "1.0 100\n", "Name: new_Zn, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.8596140365762551, pvalue=2.4545559563558152e-30)\n", "Slope and P-value = PearsonRResult(statistic=0.8459923850209367, pvalue=1.6254623038417134e-28)\n", "Slope and P-value = PearsonRResult(statistic=0.9517096407930472, pvalue=4.632020098423073e-52)\n", "Slope and P-value = PearsonRResult(statistic=0.7998343126513997, pvalue=1.8805806627400779e-23)\n", "Slope and P-value = PearsonRResult(statistic=0.9431653302385946, pvalue=1.1043974067736542e-48)\n", "Slope and P-value = PearsonRResult(statistic=-0.4802851831536068, pvalue=4.262613410926653e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.7251798456156131, pvalue=1.4427980133381768e-17)\n", "Slope and P-value = PearsonRResult(statistic=0.9408404554030116, pvalue=7.444917037941543e-48)\n", "Slope and P-value = PearsonRResult(statistic=0.9486637041821604, pvalue=8.620555233855305e-51)\n", "Slope and P-value = PearsonRResult(statistic=0.8078262418920139, pvalue=3.1427233307734225e-24)\n" ] } ], "source": [ "print(\"Median:\",poultry.Zn.median())\n", "\n", "poultry.loc[poultry['Zn'] < poultry.Zn.median(), 'new_Zn'] = 0\n", "poultry.loc[poultry['Zn'] >= poultry.Zn.median(), 'new_Zn'] = 1 \n", "print(\"Total sample count:\", poultry.Zn.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_Zn.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_Zn','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella','Pathogen_Campylobacter','Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_Zn'],axis='columns'),sample.new_Zn,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", " \n", " \n", " print(pd.value_counts(sample['new_Zn']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"Zn in {sample_name[indexing]} Model\")\n", " prelim2_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Zn','SampleType'],axis='columns').columns)\n", " prelim2_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim2_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim2_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.show()\n", " \n", " prelim2_plot.nlargest(10).to_csv(\"prelim2.csv\")\n", " top10 = pd.read_csv('prelim2.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", "# mylist2.append([f\"Zn_{sample_name[indexing]}\", rf_auc_normal])\n", "# output2 = pd.DataFrame(mylist) \n", "# output2.to_csv('Microbiome as input vs Poultry AUC.csv', index = False, header=False) \n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", " #plt.plot(pdp[1][0], pdp[0][0],'.')\n", " #plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", " #plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Zn_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " output = pd.DataFrame(mylist)\n", " \n", " output.to_csv('Microbiome Poultry Correlation.csv', index = False, header=False)\n", " \n", "\n", " \n", " indexing+=1\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SampleProbioticsCorrelationP-ValueAUC
20Campylobacter_FECESClostridium-0.8236047.095650e-260.823524
22Campylobacter_FECESLactobacillus0.8339454.791855e-270.823524
27Campylobacter_FECESBacillus-0.7748853.092480e-210.823524
38Campylobacter_SOILStreptococcus0.9092064.489654e-390.786934
41Listeria_FECESLactobacillus0.8760978.163387e-330.768549
\n", "
" ], "text/plain": [ " Sample Probiotics Correlation P-Value AUC\n", "20 Campylobacter_FECES Clostridium -0.823604 7.095650e-26 0.823524\n", "22 Campylobacter_FECES Lactobacillus 0.833945 4.791855e-27 0.823524\n", "27 Campylobacter_FECES Bacillus -0.774885 3.092480e-21 0.823524\n", "38 Campylobacter_SOIL Streptococcus 0.909206 4.489654e-39 0.786934\n", "41 Listeria_FECES Lactobacillus 0.876097 8.163387e-33 0.768549" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "correlation = pd.read_csv(\"Microbiome Poultry Correlation.csv\")\n", "\n", "cor_probiotics = correlation[correlation['Important_Feature'].isin(['Probiotic_Bacillus',\n", " 'Probiotic_Bifidobacterium',\n", " 'Probiotic_Clostridium',\n", " 'Probiotic_Enterococcus',\n", " 'Probiotic_Lactobacillus',\n", " 'Probiotic_Pediococcus',\n", " 'Probiotic_Propionibacterium',\n", " 'Probiotic_Streptococcus'])]\n", "\n", "#Select where Correlation is >= 0.7 or <= -0.7 and P-value < 0.05\n", "cor_probiotics = cor_probiotics[((cor_probiotics.Correlation >= 0.7) & (cor_probiotics[\"P-Value\"] < 0.05) & (cor_probiotics.AUC >= 0.7)) | ((cor_probiotics.Correlation <= -0.7) & (cor_probiotics[\"P-Value\"] < 0.05) & (cor_probiotics.AUC >= 0.7))]\n", "\n", "#cor_probiotics = pd.read_csv(\"04b Probiotics vs Pathogens.csv\")\n", "cor_probiotics.head()\n", "cor_probiotics = cor_probiotics[[\"Target/SampleType\", \"Important_Feature\", \"Correlation\",\"P-Value\",\"AUC\"]]\n", "cor_probiotics.replace({'Probiotic_': ''}, regex=True, inplace=True)\n", "cor_probiotics.rename(columns={'Target/SampleType':'Sample', 'Important_Feature':'Probiotics'}, inplace = True)\n", "\n", "cor_probiotics.head()\n" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Probiotics cor_probiotics (cor) with Lab pathogens (Labpath)\n", "cor_Labpath = cor_probiotics[cor_probiotics.Sample.str.contains('Salmonella|Campylobacter|Listeria')]\n", "\n", "#Probiotics cor_probiotics (cor) with Lab pathogens (Labpath) in FECES\n", "FECES = cor_Labpath[cor_Labpath['Sample'].str.contains('FECES')].copy()\n", "FECES.replace({'_FECES': ''}, regex=True, inplace=True)\n", "FECES.rename(columns={'Sample':'Pathogens'}, inplace = True)\n", "FECES = pd.pivot_table(FECES,index=['Pathogens'],columns='Probiotics',values='Correlation',fill_value=0)\n", "\n", "#Probiotics cor_probiotics (cor) with Lab pathogens (Labpath) in SOIL\n", "SOIL = cor_Labpath[cor_Labpath['Sample'].str.contains('SOIL')].copy()\n", "SOIL.replace({'_SOIL': ''}, regex=True, inplace=True)\n", "SOIL.rename(columns={'Sample':'Pathogens'}, inplace = True)\n", "SOIL = pd.pivot_table(SOIL,index=['Pathogens'],columns='Probiotics',values='Correlation',fill_value=0)\n", "\n", "#Plots layout\n", "plt.rcParams[\"figure.figsize\"] = [10, 4]\n", "plt.rcParams[\"figure.autolayout\"] = True\n", "fig, axs = plt.subplots(ncols=2)\n", "\n", "\n", "#Making the plots\n", "sns.heatmap(FECES,cmap=\"RdYlGn\", vmin=-1, vmax=1, ax=axs[0]).set(title='FECES')\n", "\n", "sns.heatmap(SOIL,cmap=\"RdYlGn\", vmin=-1, vmax=1, ax=axs[1]).set(title='SOIL')\n", "\n", "plt.suptitle('Common Probiotics Genera Correlation with Pathogens', y=1.05, fontsize=20)\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SamplePotential_ProbioticsCorrelationP-ValueAUC
1Salmonella_FECESg__Proteus0.9277849.505330e-440.845185
29Campylobacter_FECESg__Solibacillus-0.8470211.201502e-280.823524
47Listeria_FECESg__Faecalibacterium0.8098741.960447e-240.768549
185PastureHousing_FECESg__Solibacillus-0.8907622.453382e-350.878283
195PastureHousing_SOILg__DA1010.8366492.296479e-270.878283
218FreqHousingMove_SOILg__Faecalibacterium-0.8713604.557810e-320.983010
252PaGMOFree_SOILg__DA1010.8528742.060184e-290.862388
304LayersOnFarm_FECESg__Faecalibacterium-0.9321464.991643e-450.962083
344SwineOnFarm_FECESg__Faecalibacterium-0.9141453.270622e-400.872919
400WaterSource_FECESg__Proteus-0.8939416.245071e-360.941051
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" ], "text/plain": [ " Sample Potential_Probiotics Correlation P-Value \\\n", "1 Salmonella_FECES g__Proteus 0.927784 9.505330e-44 \n", "29 Campylobacter_FECES g__Solibacillus -0.847021 1.201502e-28 \n", "47 Listeria_FECES g__Faecalibacterium 0.809874 1.960447e-24 \n", "185 PastureHousing_FECES g__Solibacillus -0.890762 2.453382e-35 \n", "195 PastureHousing_SOIL g__DA101 0.836649 2.296479e-27 \n", "218 FreqHousingMove_SOIL g__Faecalibacterium -0.871360 4.557810e-32 \n", "252 PaGMOFree_SOIL g__DA101 0.852874 2.060184e-29 \n", "304 LayersOnFarm_FECES g__Faecalibacterium -0.932146 4.991643e-45 \n", "344 SwineOnFarm_FECES g__Faecalibacterium -0.914145 3.270622e-40 \n", "400 WaterSource_FECES g__Proteus -0.893941 6.245071e-36 \n", "\n", " AUC \n", "1 0.845185 \n", "29 0.823524 \n", "47 0.768549 \n", "185 0.878283 \n", "195 0.878283 \n", "218 0.983010 \n", "252 0.862388 \n", "304 0.962083 \n", "344 0.872919 \n", "400 0.941051 " ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "cor_pot_probiotics = correlation[correlation['Important_Feature'].isin(['g__Caloramator', 'g__DA101', 'g__Dorea', 'g__Faecalibacterium','g__Parabacteroides',\n", " 'g__Proteus', 'g__Rumellibacillus', 'g__Solibacillus', 'g__Veillonella'])]\n", "\n", "#Select where Correlation is > 0.7 or <-0.7 and P-value < 0.01\n", "cor_pot_probiotics = cor_pot_probiotics[((cor_pot_probiotics.Correlation >= 0.7) & (cor_pot_probiotics[\"P-Value\"] < 0.05) & (cor_pot_probiotics.AUC >= 0.7)) | ((cor_pot_probiotics.Correlation <= -0.7) & (cor_pot_probiotics[\"P-Value\"] < 0.05) & (cor_pot_probiotics.AUC >= 0.7))]\n", "\n", "cor_pot_probiotics.head()\n", "cor_pot_probiotics = cor_pot_probiotics[[\"Target/SampleType\", \"Important_Feature\", \"Correlation\",\"P-Value\",\"AUC\"]]\n", "cor_pot_probiotics.replace({'Probiotic_': ''}, regex=True, inplace=True)\n", "cor_pot_probiotics.rename(columns={'Target/SampleType':'Sample', 'Important_Feature':'Potential_Probiotics'}, inplace = True)\n", "\n", "\n", "cor_pot_probiotics.head(10)\n" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SampleProbioticsCorrelationP-ValueAUC
481pH_FECESLactobacillus-0.9811708.507486e-720.860703
537Moisture_SOILBacillus-0.7180624.163648e-170.720098
646Cd_FECESStreptococcus-0.9723451.042752e-630.847375
647Cd_FECESLactobacillus-0.7928488.420313e-230.847375
654Cd_SOILLactobacillus-0.8210161.355466e-250.736763
777Mn_SOILStreptococcus0.8493346.038898e-290.762025
789Mo_FECESStreptococcus-0.9022061.438098e-370.880956
844P_FECESBacillus-0.8909892.228276e-350.859477
892Si_FECESLactobacillus-0.7483333.604662e-190.772877
899Si_FECESClostridium-0.7554101.076156e-190.772877
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" ], "text/plain": [ " Sample Probiotics Correlation P-Value AUC\n", "481 pH_FECES Lactobacillus -0.981170 8.507486e-72 0.860703\n", "537 Moisture_SOIL Bacillus -0.718062 4.163648e-17 0.720098\n", "646 Cd_FECES Streptococcus -0.972345 1.042752e-63 0.847375\n", "647 Cd_FECES Lactobacillus -0.792848 8.420313e-23 0.847375\n", "654 Cd_SOIL Lactobacillus -0.821016 1.355466e-25 0.736763\n", "777 Mn_SOIL Streptococcus 0.849334 6.038898e-29 0.762025\n", "789 Mo_FECES Streptococcus -0.902206 1.438098e-37 0.880956\n", "844 P_FECES Bacillus -0.890989 2.228276e-35 0.859477\n", "892 Si_FECES Lactobacillus -0.748333 3.604662e-19 0.772877\n", "899 Si_FECES Clostridium -0.755410 1.076156e-19 0.772877" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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SamplePotential_ProbioticsCorrelationP-ValueAUC
493pH_SOILg__DA101-0.9309921.108835e-440.838742
644Cd_FECESg__Faecalibacterium-0.7731074.338443e-210.847375
683Cu_FECESg__Faecalibacterium-0.9167957.508708e-410.864977
688Cu_FECESg__Parabacteroides-0.7357912.790177e-180.864977
714Fe_SOILg__Caloramator0.9037057.001217e-380.973562
764Mn_FECESg__Faecalibacterium-0.7794471.278793e-210.875669
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" ], "text/plain": [ " Sample Potential_Probiotics Correlation P-Value AUC\n", "493 pH_SOIL g__DA101 -0.930992 1.108835e-44 0.838742\n", "644 Cd_FECES g__Faecalibacterium -0.773107 4.338443e-21 0.847375\n", "683 Cu_FECES g__Faecalibacterium -0.916795 7.508708e-41 0.864977\n", "688 Cu_FECES g__Parabacteroides -0.735791 2.790177e-18 0.864977\n", "714 Fe_SOIL g__Caloramator 0.903705 7.001217e-38 0.973562\n", "764 Mn_FECES g__Faecalibacterium -0.779447 1.278793e-21 0.875669" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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TEdHuN9y6yAlyf2hVq9kIqK9ss8zKTfMVrdRmmca6huuXsrjtVjUGQ227U7Pyc0e1mLkG7WPAxyStQ+pl4IPA4aRgdtAwq/hzft5S0tJtfrBaGcv7MFxt9VDv+7diDP1lS9qS9CP5J2DX4lkIpZ4BPjXadTcpHp9bW0zvxmdlKFeTLgTbWdLypNOdn8/TGrXaOzOvr952ZwIGWkQ8m3sT+BjwFkm3kC4Guioimmv2iYgfAz/OF4xuS/osvR84T9JrIuKhITY3C1he0qIdJMmPA8tJWrD5TJmkBUhtPYu1v7Py86rAzcOsezQeJ51SX66EdY/WrPw8bKzs5vc+Iq4AdlPqtWgL0uflI6QLhf8VEf10IVkj/izd5mxCs1n5uZPfp8a6PxoRx3ZSmPy9OBo4WtLqpLNbh5DOCk8itV+3krmbt/51fX7eLv9QNNshP7e6mcWbmkfkU8zbNa17uG1PbrGedUi1sHc31WDNYWT/+m8nBaGN23Wx005E3BkRPyTt51PMOy091DJ3k340FmH4WgQ0ryu7tseiaXy3bipyNemU81gD5Dr5+awWTXS2Il3g1mxOfh7J+zjUZ2UZUg8qz5EudOy6SF31XUK6Gc9BpKY00/O0O0kXAu1Eamoxl9TsohNz4KXvzaA4gfSn7IOkK/cn0lR73CwiZkXEORHxAVLb0eUY/rN5Jel9eFsHZbqe9Du1fYtp2+cyFr9bV+bnXTpY92hcCSwracOS1j8ajX1+q9p0e1cwmu/9kCLi+Yi4PCL+m3RGATqIuT2mcQw7jasjOeYjXffLRMS9EXEqqUePv5F+85cfZjHrAifIfSoi7iNdwDKJeV2oAakvR9KpmcdIp5Wb7Shpt6Zxh5PaH18YEUO1P4bUVhHgvyQ12k81koX/JX2ufti0zCOktnkdBeCImENqF7kocGIhIW1sa6HGtiWt1eYHa1nS6cJOTuVCqv14AviMpP9o9cdD0hq5W6xGu7LLgDtIQWufpnn3If2I/5V5NdRjkmvmTiXVdH+uTRnXzl0QDWVmfp7ctOyKwHFtlmmcMl6j4wLDT0ltNj+S/zwVfYF0QcpPo9BtXgkatcKfIV2QdVXTtDeRLra8MTq/G91ojkVPi4i/kf487AYcRvqDOl8XY5Le1uZPeaOpw3DtJL+bn78hab4auKZxjVjzZUmLFeZZjNRdHrw81kwjfYc/JGm+pDp3WzcW38rPJ7X64650m/qtm8eXKVL3e5eT/mx+ukWZlpe0SB6cmZ8nN80z1Pd+PpLeqNb9ETfOTvZbW9nvkeLUtySt1zwx/968lOCO5JjnJkeXAu+U9P5WG5f02vweIOkV+Te82eKkrkRnk3rcsJK5iUV/O4yUoH0996U5g3n9IM8ltVls1Z/l74AzJJ1BugJ+E9IFOY+S2hUOKSIul/Q10um4W3K7qKdJtTYbkZLBrzctNp3UZvZcSZeQ2rzdGBG/G2JTx5D6Bt0d+Kuks0kXy6xO6irqk6Raq03y/lxL6jfyftKFD3uQ2u99db41t96v2yW9Ffg1KdH/aG6n2rjV9CbM67Xiq3mZkHQw6c/KzyX9llT73bh9+JOknkSGvXhmBA4n9c/5eeCgfHHlg6R+Ml9DOs77k/ombeca0mfnnZIuJ71nK5HewzuY1ya76ArSD9+RkpZjXlv277ZrkhIRMyUdSfrxvU7SL4B/kZLSbUjHar4fmC6bnp9XBM5tas86nXTqsjhfp+t8F/AbSeeQ/oTdExE/GWNZq3Y8qcnJSqT3tVWiczqpPeSfSQmXSLVjryNdwDTkqfWIOF/SF4DPAX+RdCbp4quVSGexriS/JxHxM0l7AO8mXRR4Jun7tyfp4rZf5Nq1xroflnQAqWegCyX9AbiJ9EdsY1LsGO7P41Blny7pKODLpD7YzyF9z5YgdZ/2JtJ3qZPa8W56D+lCxi9J2ju/FilOvIV0EexMRve9b+U/SE1xLiK1ZX4K2DCv5zFg6th3afzk2P9+0h+yWyWdS6rYWJD0J/iNpLi1fmGxTo85pAqrC4AfSjqC9Cd9Fuls68ak381tSD3rrApcKekvpLMj95I+v7uRmqod2+Z33bqt291i+DH0A1r3g9xm3qNp6jarxTyrkk6N3kP6V/kwqTuo17WY95C8vkNIX7YrSIntLFJSuF6LZU6hqZu3wrT9SAH2SdJp8ltJF20s0mLexXM572Nel3anNB2Xi1ostwApIWy0JX2adJppKrkLLVKQ+RIp8DcuOLmPdCX7LqN4j5YgtcW8kBSwXiS1I7uW9MO4VotlXk3qWu+BPP8DpNrTV4/0fe3wfV8oH5fLc9meJzUXmE46o7B8Yd7JtO6qbDlSQjQzv3//l4/jYrTplo/0w39Ffi8aXSVNGq7cpB+M80k/ns+T/ph9jaa+ZPO8F9HmO8IwXc21WUakH7cgXURWnLZKYT/m+6wMcewm5mN1V36/X/b5bXf8On1/23wHO9rnsRy/vF+NY9WuK7PDSGem7iL9YXqU1BTiUxS6ROugLLuSerJ4NH8m7s3r3bFpvgmkP+4z8vaeIX0XP0ybLrZIydqPSRdRvUD6M3cxTV1TNr9vbY77pBbTtgN+QUooX8jH7Abgm8CWTfMO9VkY6vjMt/12n8c8bXnSH/c7SN/nWblMXwQWK8w3ou99q88M6fv8I1IPQo+T4vIdwLEUugkd5nM6iaG7eWt13Nvu/zCf91M6nP+1efv35M/ko6RKl+83fy5HcszzvEsCn82f3adIf6rvJt0MawqweJ5vGdIF5xeQPr/Pk35PLiJVfLjrt3F6KL8hZmZWc5JeRfrzcllE+EIgM6stt0E2M7OGT5Bq3L9XdUHMzKrkGmQzsxqTtAapjeS6pDuV3QRsHt1tN29m1ld8kZ6ZWb29itS2/hnSxaYfcnJsZnXnGmQzMzMzswK3QTYzMzMzK3CCbGZmZmZW4ATZzMzMzKzACbKZmZmZWYETZDMzMzOzAifIZmZmZmYFTpDNzMzMzAqcIJuZmZmZFThBNjMzMzMrcIJsZmZmZlbgBNnMzMzMrMAJspmZmZlZgRNkMzMzM7MCJ8hmZmZmZgVOkM3MzMzMCpwgm5mZmZkVOEE2MzMzMytwgmxmZmZmVuAE2czMzMyswAmymZmZmVmBE2QzMzMzswInyGZmZmZmBU6QzczMzMwKnCCbmZmZmRU4QTYzMzMzK3CCbGZmZmZW4ATZzMzMzKzACbKZmZmZWYETZDMzMzOzAifIZmZmZmYFTpDNzMzMzAqcIJuZmZmZFThBNjMzMzMrcIJsZmZmZlbgBNnMzMzMrMAJspmZmZlZgRNkMzMzM7MCJ8hmZmZmZgVOkM3MzMzMCpwgm5mZmZkVOEE2MzMzMytwgmxmZmZmVuAE2czMzMyswAmyjYmkmZKelfRU4bGtpGga95SkfQvLbSXpHEmzJD0q6WpJ78vTJkua22L5bfL0DSWdL+mxvPy1knat6hiYmVXJcdis+5wgWzfsHhFLNB7A/Xn8MsXxEfFzgBxgLwAuBtYBlgc+BOxSWOf9TcsuERFX5Gm/A/4IrASsCBwBPFH6XpqZ9S7HYbMuWqDqAlgtfR2YFhFfLYy7Fnj3cAtKWgFYCzgpIl7Ioy/rfhHNzAaa47DZEFyDbONK0mLANsCvRrmKR4A7gZ9K2lPSSl0rnJlZDTgOmw3PCbJ1w5m5DdosSWcWxj9cGD9L0muAZUmfuweGWecqTcvOkrR4RASwAzAT+AbwgKRLJK1bwn6ZmfULx2GzLnITC+uGPSPiT40BSZPyyxUiYnZxxlxzMRdYGbh9iHXeHxGrtZoQEfcBh+f1rQ5MBX5MqhExM6sjx2GzLnINso2riHgGuALYu0vruxc4DtioG+szMxt0jsNmw3OCbFX4FHCIpE9KWh5A0iaSTh9uQUnLSjpG0jqSJuSLRd4PXFlymc3MBonjsNkQnCBbmWY19Z/5cYCIuBzYMT/ukvQo6fTcOYVlV2nR/+bewAvAJOBPpC6FbgGeBw4Zt70yM+sfjsNmo6DU1t7MzMzMzMA1yGZmZmZmL+ME2cxqTdLJkh6SdEub6ZJ0rKQ7Jd0kafPCtLdJuiNPO2r8Sm1mNlh6LRY7QTazujsFeNsQ03cB1s2PKcAJAJImkq7c3wXYANhf0galltTMbHCdQg/FYifIZlZrEXEJ8OgQs+wB/DiSK4FlJK0MbAXcGRF35dvtnp7nNTOzEeq1WOwE2cxsaKsC9xaG78vj2o03M7PuG9dY3LN30gsudPcaNfHXzf+96iJU4uZb51ZdhHG3z/N3aCzL60NbjzwunHjVB0mn4xqmRsTUkWy2xbgYYvxAuXObjQZun6y1ta/4btVFsHEidhh1LB5VHIa+i8U9myCbmTXThJHH9LkpAI8kCDe7D1i9MLwacD+wUJvxZmYDazRxGPovFruJhZn1DU3QiB9dcBbw3nwF9dbA4xHxAHANsK6ktSQtBOyX5zUzG1ijicP9GItdg2xmfaNLQfbl65ROAyYDK0i6D/gfYEGAiDiRdGexXYE7gWeA9+VpsyUdDpwHTAROjohbu15AM7MeUkYcht6LxU6QzaxvlBGYI2L/YaYH8OE2087h5bfmNTMbaGUlyL0Wi93EwszMzMyswDXIZtY3pHJqLszMrDN1icNOkM2sb5R1as/MzDpTlzjsBNnM+kZdArOZWa+qSxx2gmxmfaMugdnMrFfVJQ47QTazvlGXwGxm1qvqEoedIJtZ36hLYDYz61V1icNOkM2sb9QlMJuZ9aq6xGEnyGbWN+oSmM3MelVd4rATZDPrG3Xpf9PMrFfVJQ47QTazvlGXmgszs15VlzjsBNnM+kZdArOZWa+qSxx2gmxmfaMugdnMrFfVJQ47QTazvlGXwGxm1qvqEoedIJtZ36hLYDYz61V1icNOkM2sb9QlMJuZ9aq6xOEJVRfAzMzMzKyXuAbZzPpGXWouzMx6VV3icCkJsqR3DjU9In5TxnbNbLDVJTCbmfWqusThsmqQd8/PKwLbAhfk4R2AiwAnyGY2YnW5g5OZWa+qSxwuJUGOiPcBSDob2CAiHsjDKwPHlbFNMxt8dam5MDPrVXWJw2W3QZ7USI6zB4H1St6mmQ2ougRmM7NeVZc4XHaCfJGk84DTgAD2Ay4seZtmNqDqEpjNzHpVXeJwKd28SVpH0hsi4nDgRGATYFPgCuDbQyw3RdIMSTOmTj27jKKZWR+bMGHkDzMz657RxOF+jMVl1SB/G/gsQEScAZwBIGnLPG33VgtFxFRgKkBwYZRUNjPrUxNrcnGImVmvqkscLitBnhQRNzWPjIgZkiaVtE0zG3ATa3Jqz8ysV9UlDpeVIC8yxLRFS9qmmQ24smouJL0N+A4wEfhBRHylafongQPz4ALAa4BXRMSjkmYCTwJzgNkRsWUphTQz6wF1icNlJcjXSPpARJxUHCnpUODakrZpZgNuYgnt2CRNJHU/+WbgPlL8OisibmvMExFfB76e598d+FhEPFpYzQ4R8XD3S2dm1lvqEofLSpCPBM6QdCDzEuItgYWAvUrappkNuJJqLrYC7oyIuwAknQ7sAdzWZv79ST3zmJnVTl3icCnXFUbEgxGxLXAMMDM/jomIbSLin2Vs08wG30RpxI9i7zj5MaVptasC9xaG78vj5iNpMeBtwK8LowM4X9K1LdZtZjZQRhOHO4jFPReHS+0HOSIuxP0em1mXjObikGLvOG20Wmm7XnR2By5rOq33hoi4X9KKwB8l3R4Rl4y4oGZmfWC0F+kNE4t7Lg73Yc90ZlZXEzXyRwfuA1YvDK8G3N9m3v1oOq0XEffn54dIXVpuNdL9MjPrF6OJwx3E4p6Lw06QzazurgHWlbSWpIVIwfes5pkkLQ28CfhtYdzikpZsvAbeAtwyLqU2MxscPReHy77VtJlZ15TR/2ZEzJZ0OHAeqXuhkyPiVkmH5ekn5ln3As6PiKcLi69EuiAZUjz9WUSc2/VCmpn1iLrEYSfIZtY3yup/MyLOAc5pGndi0/ApwClN4+4CNimlUGZmPagucdgJspn1jbrcwcnMrFfVJQ47QTazvtHhRXdmZlaSusRhJ8hm1jfqUnPRDZLeOdT0iPjNeJXFzAZHXeKwE2Qz6xtltX0bULvn5xWBbYEL8vAOwEWAE2QzG7G6xGEnyGbWN+oSmLshIt4HIOlsYIOIeCAPrwwcV2XZzKx/1SUOO0E2s74x0T23j8akRnKcPQisV1VhzKy/1SUOO0E2s75Rl5qLLrtI0nmkO08FqQP+C4daQNIUYArAF9Zamf1WWq70QppZf6hLHHaCbGZ9oy4Xh3SDpHWAlSLicEl7AdvnSVcApw61bERMBaYC3LnNRlFqQc2sr9QlDjtBNrO+UZeaiy75NvBZgIg4AzgDQNKWedru7RY0M2unLnHYCbKZ9Y26tH3rkkkRcVPzyIiYIWlSBeUxswFQlzjsBNnM+kZdai66ZJEhpi06bqUws4FSlzjsBNnM+kZd2r51yTWSPhARJxVHSjoUuLaiMplZn6tLHHaCbGZ9oy41F11yJHCGpAOZlxBvCSwE7FVVocysv9UlDjtBNjMbQBHxILCtpB2AjfLo30fEBUMsZmZmOEE2sz5Sl4tDuikiLmSYfo/NzDpVlzjsBNkqt951x1ddhEr845WHVV2EvlOXU3tmZr2qLnHYCbKZ9Y2J9YjLZmY9qy5x2AmymfWNCTWpuTAz61V1icNOkM2sb9Sl5sLMrFfVJQ47QTazvlGT7jfNzHpWXeKwE2Qz6xt1qbkwM+tVdYnDTpDNrG9MqEvVhZlZj6pLHHaCbGZ9oy41F2ZmvaoucdgJspn1jZpUXJiZ9ay6xGEnyGbWN+pSc2Fm1qvqEodrcsNAMxsEE6QRPzoh6W2S7pB0p6SjWkyfLOlxSTfkx393uqyZ2SAZTRzuJBb3Whx2DbKZ9Y0yai4kTQSOA94M3AdcI+msiLitadZLI2K3US5rZjYQ6hKHXYNsZn1jgkb+6MBWwJ0RcVdEvACcDuzRYZHGsqyZWd8ZTRzuIBb3XBx2gmxmdbcqcG9h+L48rtk2km6U9AdJG45wWTMza6/n4rCbWJhZ35jYYZviIklTgCmFUVMjYmpxlhaLRdPwdcCaEfGUpF2BM4F1O1zWzGxgjCYOw7CxuOfisBNkM+sbo+leKAfgqUPMch+wemF4NeD+pnU8UXh9jqTjJa3QybJmZoNktN28DROLey4OO0E2s75RUvdC1wDrSloL+AewH3BAcQZJrwQejIiQtBWpedojwKzhljUzGyR1icNOkM2sb0wo4aqJiJgt6XDgPGAicHJE3CrpsDz9RGAf4EOSZgPPAvtFRAAtl+1+Kc3MekNd4rATZDPrG6Nt+zaciDgHOKdp3ImF198DvtfpsmZmg6oucdgJspn1jbrc4tTMrFfVJQ47QTazvlGXW5yamfWqusRhJ8hm1jfqUnNhZtar6hKHxyVBlrQkEBHx1Hhsz8wGU1lt38zMrDN1icOlJsiSXgv8GFguDepfwMERcUuZ2zWzwVSXmgszs15Vlzhcdg3y94GPR8SFAJImkzqJ3rbk7ZrZAKpL2zczs15VlzhcdoK8eCM5BoiIiyQtXvI2zWxATajJqT0zs15VlzhcdoJ8l6TPAT/Jw+8B7i55m2Y2oOpSc2Fm1qvqEodLuB/Ky7wfeAXwG+CM/Pp97WaWNEXSDEkzpk49u+SimVm/mSCN+GFmZt0zmjjcj7G41BrkiHgMOGIE808ltVEmuDDKKpeZ9ad+DLJmZoOkLnG4lARZ0llDTY+Id5SxXTMzMzOzsSqrBnkb4F7gNOAqoB5/N8ysVHWpuTAz61V1icNlJcivBN4M7A8cAPweOC0ibi1pe2ZWAxNU9mUTZmY2lLrE4VL2MiLmRMS5EXEwsDVwJ3CRpI+UsT0zq4c6XBhiZtbLfJHeGElaGHg7qRZ5EnAsqTcLM7NR6ccga2Y2SOoSh8u6SG8asBHwB+AY31razLqhLoHZzKxX1SUOl1WDfBDwNLAecITmHUwBERFLlbRdMxtgE0rvut3MzIZSlzhcSoIcEfU4emY2rupSc9FL7vrbi1UXwcbJgjt9rOoi2DhZc/oNo162LnG47FtNm5l1TV0Cs5lZr6pLHHaCbGZ9oy7dC5mZ9aq6xGEnyGbWN+pSc2Fm1qvqEoedIJtZ36hLYDYz61V1icNOkM2sb9QlMJuZ9aq6xGEnyGbWN+rS9s3MrFfVJQ47QTazvjGBetRcmJn1qrrE4Xr8DTCzgTBBGvGjE5LeJukOSXdKOqrF9AMl3ZQfl0vapDBtpqSbJd0gaUYXd9fMrOeMJg53Eot7LQ67BtnMak3SROA44M3AfcA1ks6KiNsKs90NvCkiHpO0CzAVeH1h+g4R8fC4FdrMbID0Yhx2gmxmfaOktm9bAXdGxF0Akk4H9gBeCswRcXlh/iuB1cooiJlZr6tLHB52LyWtLWnh/HqypCMkLVNmoczMWimpicWqwL2F4fvyuHYOBf5QGA7gfEnXSpoy4p3qkGOxmfWCkppY9Fwc7uRvwK+BOZLWAX4IrAX8rBsbNzMbidEEZUlTJM0oPJqDZ6vIHa22L2kHUmD+dGH0GyJic2AX4MOStu/Kzs7PsdjMKjfaBHmYWNxzcbiTJhZzI2K2pL2Ab0fEdyVdP9YNm5mN1GhO7UXEVFJbtXbuA1YvDK8G3N88k6SNgR8Au0TEI4X135+fH5J0BulU4SUjLujwHIvNrHKjbWIxTCzuuTjcyV6+KGl/4GDg7DxuwbFs1MxsNEpqYnENsK6ktSQtBOwHnFWcQdIawG+AgyLir4Xxi0tasvEaeAtwS5d2t5ljsZlVrqQmFj0XhzupQX4fcBjwxYi4W9JawE/HumEzs5Eqo//NXCt7OHAeMBE4OSJulXRYnn4i8N/A8sDxSoF+dkRsCawEnJHHLQD8LCLO7XohE8diM6tcXeKwIlo28Xj5TNKiwBoRccdYN9ip4MLhC2bWxy585WFVF2Hc7fjPO8YUWW979KsjjgsbLPfpgenVvopYfP4Kr3YsrolXb7Jo1UWwcbLm9BtGHRdHE4eh/2JxJ71Y7A7cAJybhzeVdNaQC5mZlWCCJoz4MSgci82sF4wmDvdjLO6kicXRpMbOFwFExA351F6pLlr5Q2VvoidNfuCEqotg42SHf55YdRH6Tqd3xhtQR1NBLDYzK6pLHO4kQZ4dEY/r5QfEp9zMbNypD2shusix2MwqV5c43EmCfIukA4CJktYFjgAuH2YZM7Oum9BRxzsDy7HYzCpXlzjcyV5+BNgQeJ7UKf3jwJEllsnMrCVpwogfA8Sx2MwqN5o43I+xeNga5Ih4BvjP/DAzq0w/XujRLY7FZtYL6hKHO+nF4o+SlikMLyvpvFJLZWbWgpgw4segcCw2s14wmjjcj7G4kzbIK0TErMZARDwmacXyimRm1lpdai7acCw2s8rVJQ53spdz8+39AJC0Jr5y2sxsvDkWm5mNk05qkP8T+LOki/Pw9sCU8opkZtZaP56m6yLHYjOrXF3icCcX6Z0raXNga0DAxyLi4dJLZmbWpC6n9lpxLDazXlCXONw2QZa0fkTcngMywP35eQ1Ja0TEdeUXz8xsnn7sKmisHIvNrJfUJQ4PVYP8cdLpu2+0mBbAjqWUyMysjbp0UN/EsdjMekZd4nDbBDkiGm3bdomI54rTJC1SaqnMzFqoS81FkWOxmfWSusThTvay1a1MfXtTMxt3EzRhxI8B4lhsZpUbTRzux1g8VBvkVwKrAotK2ox0UQjAUsBi41A2M7OXEROrLsK4cyw2s15Slzg8VBvktwKHAKuR2r41gvITwGfLLZaZ2fz6sRaiCxyLzaxn1CUOD9UGeRowTdLeEfHrcSyTmVlLdel/s8ix2Mx6SV3icCd7uYWkZRoDkpaV9P/KK5KZWWt1aPc2BMdiM6tcXdogd1LiXSJiVmMgIh4Ddi2tRGZmbUgTRvwYII7FZla50cThfozFndxqeqKkhSPieQBJiwILl1ssM7P51aX/zTYci82scnWJw50kyD8Fpkv6EalT+vcD00otlZlZC/1YC9FFjsVmVrm6xOFhE+SI+Jqkm4GdSFdPfyEiziu9ZGZmTfqxHVu3OBabWS+oSxzupAaZiPgD8IeSy2JmNqS6XD3djmOxmVWtLnF42L2UtLWkayQ9JekFSXMkPTEehTMzs8Sx2Mxs/HTyN+B7wP7A34BFgX8DvltmoczMWimrayFJb5N0h6Q7JR3VYrokHZun3yRp806X7aJRxWJJ7231KLGcZjbAyurmrdficKdNLO6UNDEi5gA/knR5NzZuZjYSZZzakzQROA54M3AfcI2ksyLitsJsuwDr5sfrgROA13e4bNeMMha/rvB6EVIb5uuAH5dRRjMbbHWJw50kyM9IWgi4QdLXgAeAxceyUTOz0Sjp4pCtgDsj4i4ASacDewDF4LoH8OOICOBKSctIWhmY1MGy3TKqWBwRHykOS1oa+EkJ5TOzGqhLHO5kLw/K8x0OPA2sDuw91AKSFpF0pKTvSfqgpI5qqs3MhlJS5/SrAvcWhu/L4zqZp5Nlu2XEsbiNZ0g1MGZmI1bSjUJ6Lg4PmbjmausvRsR7gOeAYzpc7zTgReBSUpX4BsBHh1tI0hRgCsDHl1qJ3RZbpsPNmVkdKEaxzIR5cSWbGhFTi7O0WKx5S+3m6WTZMRtDLEbS7wplmkCKx78YZpmXjtlHF1+RXRdZZhSlNrNBNJo4DMPG4p6Lw0MmyBExR9IrJC0UES+MYL0bRMRrAST9ELi6k4XygZoKcOHK63f9R8bM+lzMHfkihbjSxn2k2tiG1YD7O5xnoQ6WHbPRxGJJ6wArAf9bGD0bmAj8Y5jtvXTMzl/h1Y7FZjbPKOIwDBuLey4Od9L0YSZwmaSzSKf1AIiIbw6xzIuF+WZLrZJ7M7MRGmVgHsY1wLqS1iIljvsBBzTNcxZweG7b9nrg8Yh4QNK/Oli2W2Yyslj8beCzEXFTcaSkLfO03UsppZkNtprE4U4S5PvzYwKwZIfr3aTQP6eARfOwgIiIpUZcUjOzEgJz/hN/OHAeqXb15Ii4VdJhefqJwDnArsCdpDa87xtq2a4XMhlpLJ7UnBwDRMQMSZO6XDYzq4uaxOHh2iBvBtwK3BoRf+l0pRExcawFMzObTzk1F0TEOaTgWxx3YuF1AB/udNluG2UsXmSIaYuOvVRmVks1icNtLyuU9N/Az0lXSf9e0ge6uWEzsxGbO3fkjz43hlh8Tat5JR0KXNvFIppZnYwmDvdhLB6qBnlfYNOIeEbS8sC5wEnjUywzsxZKqrnocaONxUcCZ0g6kHkJ8ZakC1r2KqOgZlYDNYnDQyXIz0XEMwAR8Yg67FDUzKw0NQnMTUYViyPiQWBbSTsAG+XRv4+IC0oqp5nVQU3i8FAJ8tr5amlIF9cVh4mId5RaMjOzZjUJzE3GFIsj4kLgwhLLZ2Z1UpM4PFSCvEfT8P+2nMvMbLz0YTu2LnAsNrPeUZM43DZBjoiLx7MgZmbDqknNRZFjsZn1lJrEYbcrNjMzMzMr6ORGIWZmvaEmNRdmZj2rJnHYCbKZ9Y+aBGYzs55Vkzg8bIIsaUvgP4E18/yN20VvXHLZzMxeJmLOiJdRCeWogmOxmfWC0cRh6L9Y3EkN8qnAJ4GbgXr8bTCz3lSTq6fbcCw2s+rVJA53kiD/KyLOGn42M7OS1eTUXhuOxWZWvZrE4U4S5P+R9ANgOvB8Y2RE/Ka0UpmZtVKTwNyGY7GZVa8mcbiTBPl9wPrAgsw7rReAg7KZja+aBOY2HIvNrHo1icOdJMibRMRrSy+JmdlwahKY23AsNrPq1SQOd5IgXylpg4i4rfTSmJkNpSYXh7ThWGxm1atJHO4kQd4OOFjS3aR2b+5ayMyqUZOaizYci82sejWJw0MmyJIEfBC4Z3yKY2Y2hJoE5maOxWbWM2oSh4dMkCMiJH0rIrYYrwKZmbVVk8DczLHYzHpGTeLwhA7muVLS60oviZnZcObOHfljcDgWm1n1RhOH+zAWd9IGeQfgg5LuAZ7G7d7MrCo1qblow7HYzKpXkzjcSYK8S+mlMDPrRE0CcxuOxWZWvZrE4WET5Ii4B0DSisAipZcom/zACeO1qZ5y9/YfrboI404TVXURKjHpwm9XXQTrI1XFYjOzOho2QZb0DuAbwCrAQ8CawF+ADcstmplZkz5sx9YtjsVm1hNqEoc7uUjvC8DWwF8jYi1gJ+CyUktlZtbK3Bj5Y3A4FptZ9UYTh/swFneSIL8YEY8AEyRNiIgLgU3LLZaZWQvjfOW0pOUk/VHS3/Lzsi3mWV3ShZL+IulWSR8tTDta0j8k3ZAfu46hOI7FZla9CnqxqCIWd5Igz5K0BHAJcKqk7wCzR7JjZmZdMf5dCx0FTI+IdYHpebjZbOA/IuI1pBreD0vaoDD9WxGxaX6cM4ayOBabWfWq6eZt3GNx2wRZ0hr55R7AM8DHgHOB/wN272RvzMy6avxP6+0BTMuvpwF7Ns8QEQ9ExHX59ZOkdsGrjnXDDY7FZtZTqmliMe6xeKga5DPzRp4GfhkRsyNiWkQcm0/zmZmNr/GvtVgpIh6AFHyBFYeaWdIkYDPgqsLowyXdJOnkVqcFO3Bm3r5jsZlVr5oa5HGPxUMlyMW+t1413IrMzEo3iqAsaYqkGYXHlOIqJf1J0i0tHnuMpGi5+cOvgSMj4ok8+gRgbVJb4QdIvVCMlGOxmfWOUSbI/RaLh+rmLdq8NjOrxihO00XEVGDqENN3bjdN0oOSVo6IByStTOperdV8C5IC8qkR8ZvCuh8szHMScPaId8Cx2Mx6ySibS/RbLB6qBnkTSU9IehLYOL9+QtKTkp4YYjkzs3KM/2m9s4CD8+uDgd82zyBJwA+Bv0TEN5umrVwY3Au4ZRRlcCw2s95RTROLcY/FbWuQI2JiBwU2Mxs/49+X5leAX0g6FPg78C4ASasAP4iIXYE3AAcBN0u6IS/32XyV9NckbUqq+Z0JfHCkBXAsNrOeUk2fxuMei4e9k56ZWc8Yey3EiOSL4HZqMf5+YNf8+s+8vJ1wcb6DSi2gmdl4G+c4DNXEYifIZtY/KgjMZmZWUJM47ATZzPpGxMhP7bWsTjAzs1EZTRyG/ovFTpDNrH/UpObCzKxn1SQOd3KraTMzMzOz2nANspn1j5rUXJiZ9ayaxGEnyGbWP6rpXsjMzBpqEoedIJtZ/6hJzUUvWWtt/0zUxRrTv1V1Eawf1CQOO/KZWf+oSWA2M+tZNYnDTpDNrH/U5NSemVnPqkkcdoJsZv2jJjUXZmY9qyZx2AmymfWPmgRmM7OeVZM47ATZzPpHTU7tmZn1rJrEYSfIZtY/alJzYWbWs2oSh0tJkCWtHxG3S9q81fSIuK6M7ZrZgKtJYDYz61k1icNl1SB/HJgCfCMPN9fH71jSds1skNXk1J6ZWc+qSRyeUNJ6fyDplRGxQ0TsAEwDngJuAfZpt5CkKZJmSJoxderZJRXNzPrW3Lkjf5iZWfeMJg73YSwuqwb5RGBnAEnbA18GPgJsCkylTZIcEVPzdIIL6/EXxcw6FnMcFszMqlSXOFxWgjwxIh7Nr/cFpkbEr4FfS7qhpG2a2aCryak9M7OeVZM4XFqCLGmBiJgN7ERqj1z2Ns1s0NWk5sLMrGfVJA6XlayeBlws6WHgWeBSAEnrAI+XtE0zMzMzszErJUGOiC9Kmg6sDJwfEY2/GxNIbZHNzEYsanJqz8ysV9UlDpfW3CEirmwx7q9lbc/MaqAmp/bMzHpWTeKw2wObWf+Y039dBZmZDZSaxGEnyGbWN+pyas/MrFfVJQ47QTaz/lGTU3tmZj2rJnHYCbKZ9Y9xrrmQtBzwc2ASMBN4d0Q81mK+mcCTwBxgdkRsOZLlzcz6RgU1yFXE4rJuNW1m1nUxJ0b8GKOjgOkRsS4wPQ+3s0NEbNoIyKNY3sys540mDvdjLHaCbGb9Y+7ckT/GZg9gWn49DdhznJc3M+sto4nDfRiLnSCbWf+YEyN/jM1KEfEAQH5esc18AZwv6VpJxTuHdrq8mVl/GE0c7sNY7DbIZtY3RnP1dA6SxUA5NSKmFqb/CXhli0X/cwSbeUNE3C9pReCPkm6PiEtGXFgzsx432l4s+i0WO0E2s/4xilqIHICnDjF953bTJD0oaeWIeEDSysBDbdZxf35+SNIZwFbAJUBHy5uZ9Y1R1gb3Wyx2Ewsz6x/jf1rvLODg/Ppg4LfNM0haXNKSjdfAW4BbOl3ezKyvVNPEYtxjsWuQzaxvVNBB/VeAX0g6FPg78C4ASasAP4iIXYGVgDMkQYqpP4uIc4da3sysX1V0o5Bxj8VOkM2sf4zzLU4j4hFgpxbj7wd2za/vAjYZyfJmZn2rgltNVxGLnSCbWd+oyy1Ozcx6VV3isNsgm5mZmZkVuAbZzPrH2C/0MDOzsahJHHaCbGb9oyan9szMelZN4rATZDPrG1GTmgszs15VlzjsBNnM+kdNai7MzHpWTeKwE2Qz6x8VdC9kZmYFNYnDTpDNrG/UpXshM7NeVZc47ATZzPpHTdq+mZn1rJrEYSfIPWatS75TdRHG3S8XPKzqIlRiUtUF6EN1qbkwM+tVdYnDTpDNrG/U5eppM7NeVZc47ATZzPpGXWouzMx6VV3isBNkM+sbc2tSc2Fm1qvqEoedIJtZ36hLzUUZJE0EVqIQ9yPi79WVyMz6UV3isBNkM+sbMbce/W92m6SPAP8DPAg0DmIAG1dWKDPrS3WJw06Qzaxv1OXikBJ8FHh1RDxSdUHMrL/VJQ47QTazvlGXU3sluBd4vOpCmFn/q0scdoJsZjb47gIukvR74PnGyIj4ZnVFMjPrXU6Qzaxv1OXUXgn+nh8L5ceQJE0BpgB8ftLK7LfisuWWzsz6Rl3isBNkM+sbdTm1120RccwI558KTAX42+s39EE3s5fUJQ47QTazvjG3JoG5WySdNdT0iHjHeJXFzAZDXeKwE2Qz6xt1ObXXRduQLtA7DbgKULXFMbN+V5c47ATZzPpGXU7tddErgTcD+wMHAL8HTouIWystlZn1rbrE4QlVF8DMrFMxN0b8qLOImBMR50bEwcDWwJ2k3iw+UnHRzKxPjSYO92Msdg2ymfWNupza6yZJCwNvJ9UiTwKOBX5TZZnMrH/VJQ67BtnM+kbMnTvix1hIWk7SHyX9LT/P19+ZpFdLuqHweELSkXna0ZL+UZi265gKNPLyTwMuBzYHjomI10XEFyLiH+NZDjMbHKOJw/0Yi50gm1nfiDkx4scYHQVMj4h1gel5+OVlirgjIjaNiE2BLYBngDMKs3yrMT0izhlrgUboIGA90q2mL88/GE9IelLSE+NcFjMbAKOJw/0Yi93Ewsz6RgXt2PYAJufX04CLgE8PMf9OwP9FxD3lFqszEeFKEDPrqoraE497LHbwNLO+MXdujPgxRitFxAMA+XnFYebfj9SlWtHhkm6SdHKr04JmZv1kNHG4H2OxE2Qz6xujOa0naYqkGYXHlOI6Jf1J0i0tHnuMpGySFgLeAfyyMPoEYG1gU+AB4BtjOwJmZtUabROLfovFbmJhZn1jNKf2irdNbjN953bTJD0oaeWIeEDSysBDQ2xqF+C6iHiwsO6XXks6CTh7RIU3M+sxo21i0W+x2DXIZtY3Krgw5Czg4Pz6YOC3Q8y7P02n9HIgb9gLuGWsBTIzq1JFF+mNeywupQZZ0s1Aq6MhICJi4zK2a2aDrYKLQ74C/ELSocDfgXcBSFoF+EFE7JqHFyPdse6DTct/TdKmpHg4s8V0M7O+UtFFeuMei8tqYrFbfhbp1qbj2venmVk3RMQjpKuhm8ffTyGuRcQzwPIt5juo1AKamdVAFbG4lAS52K2GpOd7pcsjM+tv/Xi7UjOzQVKXOOyL9Mysb9TlFqdmZr2qLnG4rDbImxcGF20aJiKua7PcFGAKwInf/zhTpuzWajYzq6ku9KVpZmZjUJc4XFYNcrF/uX8C/5tfi9RAesdWCxW7AAkurMc7YGYdmzu36hKYmdVbXeJwWW2QdwCQtCjw78B2pMT4UlJnzWZmI1aXwGxm1qvqEofLboM8DXgCODYP7w/8GHh3yds1swFUl8BsZtar6hKHy06QXx0RmxSGL5R0Y8nbNLMBVZOmb2ZmPasucbjsBPl6SVtHxJUAkl4PXFbyNs1sQNWl5sLMrFfVJQ6XnSC/HnivpL/n4TWAvzTutOc76pnZSNQlMJuZ9aq6xOGyE+S3lbx+M6uRugRmM7NeVZc4XGqC7DvomVk31SUwm5n1qrrEYd9Jz8z6Rl0Cs5lZr6pLHHaCbGZ9oy6B2cysV9UlDjtBNrO+UZfAbGbWq+oSh50gm1nfqEtgNjPrVXWJwxOqLoCZmZmZWS9xDbKZ9Y2ImtzCycysR9UlDjtBNrO+UZdTe2ZmvaoucdgJspn1jboEZjOzXlWXOOwE2cz6Rl0Cs5lZr6pLHHaCbGZ9oy6B2cysV9UlDjtBNrO+UZfAbGbWq+oSh50gm1nfqEtgNjPrVXWJw06Qzaxv1CUwm5n1qrrEYSfIZtY35taj+00zs55VlzjsBNnM+kZdai7MzHpVXeKwbzVtZn1j7tyRP8ZC0rsk3SpprqQth5jvbZLukHSnpKMK45eT9EdJf8vPy46tRGZm1RpNHO7HWOwE2cz6xngHZeAW4J3AJe1mkDQROA7YBdgA2F/SBnnyUcD0iFgXmJ6Hzcz6VhUJMhXEYifIZtY3xjsoR8RfIuKOYWbbCrgzIu6KiBeA04E98rQ9gGn59TRgz7GVyMysWlUkyFXE4p5tgyx2UGXblqZExNSqtl+FKvf53S8O95kvj9/r/nJA3DHiuCBpCjClMGpql/d/VeDewvB9wOvz65Ui4gGAiHhA0opd3O64WPeqWyuLxVXp5++IjYzf65EbTRyG/ovFrkFubcrwswycOu4z1HO/a7XPETE1IrYsPF4WkCX9SdItLR57tFtnk1Y/FjW5zntg1eo7UnN+r8dJv8Xinq1BNjMbDxGx8xhXcR+wemF4NeD+/PpBSSvnGouVgYfGuC0zs4HUa7HYNchmZmNzDbCupLUkLQTsB5yVp50FHJxfHwz8toLymZnVQVdjsRPk1urYHqmO+wz13O867vOoSNpL0n3ANsDvJZ2Xx68i6RyAiJgNHA6cB/wF+EVE3JpX8RXgzZL+Brw5D1vv83ekPvxe94EqYrEi3FTOzMzMzKzBNchmZmZmZgVOkM3MzMzMCpwgm5mZmZkVOEFuQdKykjauuhxmZnUi6RRJd0u6IT+OGIdtzpS0Qn59eX6eLOnsLqx7U0m7jmK5VST9aqzb7wdN7/l1krbp0npfel/HuJ7PdqM8hfVd3mb8KZL26ea2bGycIGeSLpK0lKTlgBuBH0n6ZtXlKpukjSW9Q9I7G4+qy1Q2SW+QtHh+/R5J35S0ZtXlKpOkj+bPtyT9MP8QvaXqcpm18MmI2DQ/jh3PDUfEtl1e5abAiBJkSQtExP0RUadk6ZMRsSlwFPD9ThbIsWw8cpgRJ8iSJrabVsJnzEriBHmepSPiCeCdwI8iYgtgrJ1W9zRJJwMnA3sDu+fHbpUWanycADwjaRPgU8A9wI+rLVLp3p8/328BXgG8D3c5ZiWQ9DlJt0v6o6TTJH1ijOv7b0nX5DtqTZWkPH5tSedKulbSpZLWz+NXknSGpBvzY9s8/sw87635lrettvVUYXCpvJ7bJJ3YSMYknSBpRl7PMYVlXyfp8rzNqyUtDXwe2DfXju4raXFJJ+f9uV75DmGSDpH0S0m/A86XNEnSLYVp3yts52xJkxvllfTVvF9/krRVruy5S9I7xnLcR6KL7/klwDqSlpA0Pf+Rv7lwnCZJ+ouk44HrgNXbvR/ZJ/N7cbWkdfI6dpd0VT7+f5K0Uh6/hKQf5e3dJGlvSV8BFs3v36l5vvfk9d0g6fuNZDi/F5+XdBWwjaSPa96d4I4sHKun8rMkfS9/vn4PrFiYZwtJF+f39TylG1sg6Yg8/02STh/lMbZORYQfqau7m4GVgfOB1+VxN1VdrpL3+baqy1DRfl+Xn/8bOLQ4blAfjc8y8B1gr/z6+qrL5cdgPYAtgRuARYElgb8BnxjB8qcAd+d13AC8FliuMP0nwO759XRg3fz69cAF+fXPgSPz64mkyg8a68lluwVYPg/PBFbIr5/Kz5OB54BX5XX8EdinaT0TgYuAjYGFgLsKvx1Lke5UewjwvUL5vwS8J79eBvgrsHie777CuicBt+TXzes4G5icXwewS359Rv79WhDYBLihj97zxrF9F3BVPnZL5XErAHeSbiM8CZgLbF1Yfr73o/C+/md+/V7g7Px6WeZ1cftvwDfy668C3y6sd9niZyK/fg3wO2DBPHw88N7Ce/Hu/HoLUk6xOLAEcCuwWdNn7J35czURWAWYBeyT37/LgVfk+fYFTs6v7wcWbnx+qv6+D/rDt5qe5/OkzqX/HBHXSHoV6Ys+yK6QtEFE3FZ1QcbZk5I+A7wH2D7XACxYcZnKdq2k84G1gM9IWpL0Q2PWTdsBv42IZwFyjehIfTIiXmp/m2vyPgUsBiwH3CrpQmBb4JdKFcoAC+fnHUkJERExB3g8jz9C0l759erAusAjQ5Tj6oi4K5fhtLxvvwLenWugFyBVqmxASo4eiIhr8nafyMs1r/MtwDsKNayLAGvk13+MiEeHKE8rLwDn5tc3A89HxIuSbiYlk+OhG+/51yX9F/Av4FBSMvwlSduT4tSqwEp53nsi4srCsq3ej5vytNMKz9/Kr1cDfp5rZRci/SGDdMZ4v8ZKI+KxFuXciZT8XpPf20WZd8viOcCv8+vtgDMi4mkASb8B3ghcX1jX9sBp+TN6v6QL8vhXAxsBf8zbmAg8kKfdBJwq6UzgzBblsy5ygpxFxC+BXxaG7yI1PRhk00hJ8j+B50lBKSJi0C9Q3Bc4gFR7/E9JawBfr7hMZTuU1B7yroh4RtLypGYWZt00X0Y4ppVJi5Bq6baMiHslHU1KKicAsyK1W+1kPZNJCdA2+fN/UV7PUJrvohWS1gI+QaopfkzSKXk9ajF/y6IAe0fEHU3lez3wdJtlZvPy5pDFcr8YuTqRlEg+DxARcyWN1+97N97z5j9Fh5Cagm2RE/6ZzNvvpwvztXs/GqLF6+8C34yIs/Ln4ujCfgz3HgqYFhGfaTHtuZzsNubrRKvtCbg1IlpdrPh2UmL9DuBzkjaMdPc4K4HbIGe57dHJzY+qy1Wyk4GDgLcxr/3x7pWWaBxExD8j4psRcWke/ntEDHob5O1Ip/o2zrUyG5JO8Zp105+B3SUtImkJ0g/6WDSSnYfz+vaBl2po75b0LnipPecmed7pwIfy+ImSlgKWBh7LyfH6wNYdbHsrSWsptT3eN+/bUqQE7fHcdnWXPO/twCqSXpe3u2ROUJ8kNTtoOA/4iPRSO+rNOijHTGBTSRMkrQ5s1cEy46nb7zmk9+uhnBzvALS7iLrd+9Gwb+H5isK6/5FfH1yY93zSbYqB1JtVfvmipMYZxunAPpJWzPMsp9YXeF8C7ClpMaULwvcCLm0xz375M7oysEMefwfwCuXePCQtKGnD/DlcPSIuJF07swwppltJXIM8T7FLn0VIH+j7KyrLePl7RJxVdSHGm6QnmffPfSFS84qnImLp6kpVuk8WXi9C+pG9lnQ62qwrcvO0s0g9Ad0DzGBeE4fRrG+WpJNIzQdmAtcUJh8InJBPzS8InJ63+1FgqqRDSae9P0RqhnCYpJtICUjxFH07V5AuZH0tKZk5I9fMXk9qU3oXcFku5wuS9gW+K2lR4FlSjfWFwFGSbgC+DHwB+DZwU06SZzL8hdGXkZoB3ExqO31dB2UfN91+z7NTgd9JmkFq33x7m23f2Or9KFhY6aK5CcD+edzRpKY5/yB9DtbK4/8fcJzSxZFzgGOA3wBTSe/XdRFxYP68nZ8T1heBD+f9LpbrulybfXUe9YOIKDavgNRmfEfS+/pX4OK87AtK3b0dq3Sh5wKkz8xfgZ/mcQK+FRGzWh4964pGQ3Vrkj/8f4qIgU0glK4EXoZ00cHzjfER8ZuqylQFSXsCW0VEV/u77GW5JuprEbH/sDObjYCkJSLiKUmLkRLLKRHRU0mddZffcxtErkFub13mXTwxqBYlJcbF/nCD9K+5NiLiTElHVV2OcXYf6UIQs26bKmkD0pmKaU6UasHvuQ0cJ8hZ4bR7o6H+P4FPV1qokkVELS/S0stvhjKB1E3RQJ9KkfRd5u3jBNIFezdWViAbWBFxQHFY0nHAG5pmW5f5ewn6TkT8qMyyWTn8ntsgchOLGpP0I1okhhHx/gqKM27yfjfMJrUDPCkiHmq9RP+TVLwYZTYwMyKa2+uZmZkZTpCRtPlQ0wf5VJGkYjd2L12YGBFHVFQkK0m+kvqlboiU+n5eOCKeqbZkZmZmvccJcupwvp0Y5Iv0mg36hYlNzQzmM8h/DCRdCewcEY3bnC4BnB8R21ZbMjMzs95T+zbIEbHD8HPVxqBfmDij6gJUaJFGcgxQuOLczLpE0hxSt10LAH8BDm53libfpOKFiLh8lNtaBjggIo7Pw6sAx0bEPkMsM4l0y+WWF+jmMv2W1GXaIsDpEXHMCMp0Sl7/r5rGb0m6JXPbSojm4yHpMOCZGvRRbz2q9gly0wVb8xnkLs/qdmFiREyrugwVelrS5o0mQ5K2IPXVambd82zj7n6STgUOA77ZZt7JwFPAqBJkUhed/0660yARcT/5RipjdGlE7JabZd0g6eyIuLYxUdICI717W0TMYPgKiskUjkdEnDiyYpt1V+0TZIa+c9xAd3kWEUsOP9fgkPQ7hm5i8Y5xLM54O5LUOX7j5jcrM+8uU2bWfZeS7ly5HOmupa8CngGmAE+Qkuc5kt4DfIR0M4wTmXcW78iIuEzp9tpr5OXXAL4dEceSbmKydr4JyR+B48i1w7mm+CfA4nldh4+0pjoinpZ0bd7G7sAqwCTSXQ0/k/fpFcC/gPdFxN/zojtL+iiwEvDxiDg71w5/IifenR6PnUg3cPpfSevkY/MK0k083pWX/TnpbnoLAB9q3B3VrBtqnyDXsaszSetHxO3tLlAc4AsT/7fqAlQl3+1qfeDVpDMGt0fEixUXy2wg5dtM70K6g98xwPURsaekHYEfR8Smkk4kJ4B5mZ+R7o72Z0lrkG5L/Zq8yvVJtyJeErhD0gnAUcBGhRrrSYUiPAS8OSKek7QucBqpO8uR7MPypFtyfwHYANgC2C4ins2VDT+OiGmS3g8cC+yZF50EvAlYG7gwJ7dFnR6PnQrLnAp8JSLOkLQIqavKDwHnRcQX80XHbjJmXVX7BFnSeyLip5I+3mp6RLQ7PdbP/gP4APCNFtOCAb39cERcXHUZqiLpw8CpEXFLHl5W0v6N9otm1hWL5hpdSDXIPwSuAvYGiIgLJC2fbxfcbGdgg3QHagCWktQ4y/f7iHgeeF7SQ6Ta2aEsCHxP0qakGtf1RrAPb8y3b55LSkpvlfQu4KyIaDTL2gZoNE/8CfC1wvK/iIi5wN8k3UVK7ou2o7PjAUA+BqtGxBl5mefy+GuAkyUtCJwZETeMYB/NhlX7BJl5p6Bq09wgIj6Qn2t1gaKkX0TEuyXdzMubWojUY8nGFRVtPHwgIo5rDETEY5I+QG6/aGZd8VIb5AYVMt6CVk29JgDbFJLQxvKQ7njaMIfhf7s/BjwIbJLX+9ww8xddGhG7tRj/9BDLRJvXrYY7PR5DzU9EXCJpe+DtwE8kfd0X9Fk31T5Bjojv5+eOr9TtdzW+MPGj+blV8B90EyQpcr+O+ZTkQhWXyawOLgEOBL6Q2+I+HBFP5IuklyrMdz5wOPB1AEmbDlMr+iTtK3aWBu6LiLn5JkETx7QH87sc2I9Ue3wg8OfCtHdJmgasRWpnfAepqUZDp8cDgDztPkl7RsSZkhbO+/MK4B8RcVK+oHBzwAmydc2EqgvQKyRNy93mNIaXlXRyhUUq0+75cSjpFOCB+fED4D0VlqtUEfFAfjkBeDAi7omIe0jt9VrWUgyQ84BfSNopt/s7jdQ+0szKdTSwpaSbSBfWNe5q+TtgL0k3SHojcERjPkm3kS5aaysiHgEuk3SLpK83TT4eODj3f74eQ9f+jsYRwPvyPh3EvMoHSAnxxcAfgMMaTSIKjqaz41F0EHBEXuZy4JWkXi9uyM1B9ga+06V9MwN8o5CXSLo+IjYbbtwgkXQ26dT7A3l4ZeC4iBiyhrnfSZoBbBsRL+ThhYDLIuJ11ZasPEo3gfkg6cpwkWqrftC4s56ZmZnNU/smFgUTJC0bEY8B5K5oBv34TCrUqkJqszaSizn61QKN5BggIl7ISfLAyqdaf0g6FRrAHU6OzczMWhv0BHAkvgFcLqlxB6B3AV+ssDzj4SJJ55FOtwepTdlQt94eFP+S9I6IOAtA0h7AwxWXqVS5rd80YCapBnl1SQdHxCUVFsvMKiDprcBXm0bfHRF7VVEes17kJhYFkjYk9TUpYHpE3FZxkUqXL9hrtPe6pNGVziCTtDapX81VSO/1vaTboN5ZacFKlDv8PyAi7sjD6wGnRcQW1ZbMzMys9zhBbiJpRdI96AEo3B3IBoykJUjfgSerLkvZJN3U3I1dq3FmZmbmJhYvkfQOUjOLVUi9GqwJ/AXYsMpylUHSnyNiu9ytTqv+gOframfQSHo76b1dpNFNaUR8vtJClWtGboP8kzx8IHBtheUxMzPrWa5BziTdSLqD3J8iYjNJOwD7R8SUiotmXZZvaboYqTnND4B9gKsj4tBKC1ai3Hfoh0l3sRKpL9Lj8925zMzMrMAJciZpRkRsmRPlzfJV/1dHxFZVl60MuduvmyJio6rLMt4aTQsKz0sAv4mIt1RdtrLkjvSfa/RckW8UsnBEPFNtyczMzHqPbxQyz6ycKF0CnCrpO8DsistUmoiYC9woaY2qy1KBxq1cn5G0CvAi6a5Pg2w6sGhheFHgTxWVxczMrKe5DfI8e5ASp4+R2mcuDQxym1SAlYFbJV1N4U5LEfGO6oo0Ls7Od038OnAdqR32SZWWqHyLRMRTjYGIeErSYlUWyMzMrFe5iUULklYAHokBPziS3tRqfERcPN5lqUpum7tIRDxedVnKJOky4CMRcV0e3gL4XkRsU23JzMzMek/ta5AlbU26H/yjwBdIV/mvQLqz3nsj4twqy1eyXSPi08URkr4KDHSCLGkR4N9JF6wF8GdJJ0TEc9WWrFRHAr+UdH8eXhnYt7rimJmZ9a7a1yBLmgF8ltSkYiqwS0RcKWl90o0UNqu0gCWSdF1EbN40buD7xpX0C+BJ4Kd51P7AshHxrupKVT5JCwKvJvVicXtEvFhxkczMzHpS7WuQgQUi4nwASZ+PiCsBIuL2Rv+4g0bSh0g1qGtLuqkwaUngsmpKNa5eHRGbFIYvzL2XDKycHH8I2D6PukjS950km5mZzc8JMswtvH62adqgVq//DPgD8GXgqML4JyPi0WqKNK6ul7R148+QpNcz+H8MTgAWBI7Pwwflcf9WWYnMzMx6lJtYSHNIPTiI1PVVo19YkS7eWrCqspVN0trAfRHxvKTJwMbAjyNiVpXlKoukm0l/ehpNDRq3EV8DuG2Q+4SWdGNTrXnLcWZmZuYaZCJiYtVlqNCvgS0lrQP8EDiLVLu8a6WlKs9uVRegQnMkrR0R/wcg6VXAnIrLZGZm1pNqnyDX3NyImC3pncC3I+K7kq6vulBliYh7Gq8lbQK8MQ9eGhED3QYZ+CSprfVdpLMjawLvr7ZIZmZmvckJcr29KGl/4L3A7nncwDYpaZD0UeADwG/yqJ9KmhoR362wWGX7M7AuhV4sqi2OmZlZ76p9G+Q6k7QBcBhwRUScJmktYN+I+ErFRStV7rljm4h4Og8vTjoGA9u9XZsu/eYbZ2ZmZq5BrrWIuE3SJ4D1JG0E3DHoyXEmXt7+dk4eN3AkvRJYFVhU0mbM28+lAN9q2szMrAUnyDWWe66YBswkJU6rSzo4Ii6psFjj4UfAVZLOyMN7AidXV5xSvRU4BFgN+GZh/JOkG+SYmZlZEzexqDFJ1wIHRMQdeXg90t0Dt6i2ZOWTtDnpVtMCLomIgb04EUDS3hHx66rLYWZm1g9cg1xvCzaSY4CI+Gu+49pAk/STiDgIuK7FuEF1kaRjSX8KgnTR3ucj4pFqi2VmZtZ7JlRdAKvUDEk/lDQ5P04Crq26UONgw+KApInAoNeanw78C9gb2Ce//nmlJTIzM+tRbmJRY5IWBj5MoakBcHxEPF9pwUoi6TOkdrfNd0x8AZgaEZ+pqmxlk3Rtc9MZSTMiYsuqymRmZtarnCBb7Uj68iAnw61I+l9gBvCLPGofYMOI+J/qSmVmZtabnCDXkKSbSe1QWxrk/oABJO0FXBARj+fhZYDJEXFmleUqk6QngcWBuaT3fiLwdJ4cEbFUVWUzMzPrNU6Qa0jSusBKwL1Nk9YE7o+IO8e/VONH0g0RsWnTuOsjYrOKimRmZmY9xBfp1dO3gCci4p7ig9Qu91sVl208tPrcD3SPLkreI+lzeXh1SVtVXS4zM7Ne5AS5niZFxE3NIyNiBjBp/Isz7mZI+qaktSW9StK3GPzeO44HtgEOyMNPAcdVVxwzM7Pe5QS5nhYZYtqi41aK6nyE1HPFz4FfAs+RevMYZK+PiA+T9pWIeAxYqNoimZmZ9aaBPq1sbV0j6QMRcVJxpKRDGfyaVCLiaeCoqssxzl7M/T0HgKRXkC7YMzMzsya+SK+GJK0EnEGqRW0kxFuSahT3ioh/VlW28ZCTw0+RbhjyUm16ROxYWaFKJulAYF9gc2AaqZu3/4qIX1ZaMDMzsx7kBLnGJO0AbJQHb42IC6osz3iRdD6pecUngMOAg4F/RcSnKy1YSSRNALYGHgV2It0cZXpE/KXSgpmZmfUoJ8hWO427ykm6qdHns6SLI+JNVZetLJKuiIhtqi6HmZlZP/BFelZHL+bnByS9XdJmwGpVFmgcnC9pb0mquiBmZma9zjXIVjuSdgMuBVYHvgssBRwdEb+rtGAlKtxJbzapJwvhO+iZmZm15ATZDJB0ZER8u+pymJmZWfXcxMIs+XjVBSiTpOmdjDMzMzP3g2zWMJBtcyUtAiwGrCBpWebt51LAKpUVzMzMrIc5QTZLBrWt0QeBI0nJcPEmME/iW02bmZm15ATZaiNfqNYqERaDe4vty4FfAPtExHclHQzsDcwEflZlwczMzHqVL9IzG2CSrgN2johHJW0PnA58BNgUeE1E7FNl+czMzHqRa5DNBtvEiHg0v94XmBoRvwZ+LemG6oplZmbWu9yLhdlgmyip8Ud4J6B4O3H/QTYzM2vBP5Bmg+004GJJDwPPkm6QgqR1gMerLJiZmVmvchtkswEnaWtgZeD8iHg6j1sPWCIirqu0cGZmZj3ICbKZmZmZWYHbIJuZmZmZFThBNjMzMzMrcIJsHZE0R9INkm6R9EtJi41g2UMkfa/NtMuHWXaSpAMKw1tKOrbzkpuZmZmNjBNk69SzEbFpRGwEvAAcVpwoaeJoVhoR2w4zyyTgpQQ5ImZExBGj2ZaZmZlZJ5wg22hcCqwjabKkCyX9DLhZ0iKSfiTpZknXS9qhsMzqks6VdIek/2mMlPRUfpakr+ca6psl7Ztn+Qrwxlx7/bG8zbPzMksUtneTpL0lTZR0SmE9HxunY2JmZmYDwv0g24jkm07sApybR20FbBQRd0v6D4CIeK2k9YHzc3diL80HPANcI+n3ETGjsOp3km5/vAmwQp7nEuAo4BMRsVve/uTCMp8DHo+I1+Zpy+Z1rJprupG0TNd23szMzGrBNcjWqUXzrYlnAH8HfpjHXx0Rd+fX2wE/AYiI24F7gEaC/MeIeCQingV+k+ct2g44LSLmRMSDwMXA64Yp087AcY2BiHgMuAt4laTvSnob8MSI99TMzMxqzTXI1qlnI2LT4ghJAE8XRw2xfHOH283DQy3bjprXExGPSdoEeCvwYeDdwPtHsW4zMzOrKdcgWzddAhwIL92pbQ3gjjztzZKWk7QosCdwWYtl981tiF8BbA9cDTwJLNlme+cDhzcGJC0raQVgQkT8mtQEY/Nu7JiZmZnVh2uQrZuOB06UdDMwGzgkIp7PNc1/JjW/WAf4WVP7Y4AzgG2AG0m1wp+KiH9KegSYLelG4BTg+sIy/w84TtItwBzgGOD/gB9Javz5+0z3d9PMzMwGmW81bWZmZmZW4CYWZmZmZmYFTpDNzMzMzAqcIJuZmZmZFThBNjMzMzMrcIJsZmZmZlbgBNnMzMzMrMAJspmZmZlZgRNkMzMzM7OC/w+Bt4/mlk44WQAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "\n", "cor_physicochem = cor_probiotics[~cor_probiotics.Sample.str.contains('Campylobacter|Salmonella|Listeria|AvgNumBirds_|AvgNumFlocks_|YearsFarming_|EggSource_|BroodBedding_|BroodFeed_|BrGMOFree_|BrSoyFree_|BrMedicated_|BroodCleanFrequency_|AvgAgeToPasture_|PastureHousing_|FreqHousingMove_|AlwaysNewPasture_|PastureFeed_|PaGMOFree_|PaSoyFree_|PaMedicated_|LayersOnFarm_|CattleOnFarm_|SwineOnFarm_|GoatsOnFarm_|SheepOnFarm_|WaterSource_|FreqBirdHandling_|AnyABXUse_|LengthFeedRestrixProcess_|Seasons_|FlockAgeDays_|Breed_|FlockSize_|AnimalSource_|ProcessingType_|SkinOnOff_|ChillingMethod_|ScalderTempC_|RinseWaterSource_|RinseWaterChlor_|TransportTime_|StorageTempC_|StorageTimeD_')]\n", "cor_physicochem2 = cor_pot_probiotics[~cor_pot_probiotics.Sample.str.contains('Campylobacter|Salmonella|Listeria|AvgNumBirds_|AvgNumFlocks_|YearsFarming_|EggSource_|BroodBedding_|BroodFeed_|BrGMOFree_|BrSoyFree_|BrMedicated_|BroodCleanFrequency_|AvgAgeToPasture_|PastureHousing_|FreqHousingMove_|AlwaysNewPasture_|PastureFeed_|PaGMOFree_|PaSoyFree_|PaMedicated_|LayersOnFarm_|CattleOnFarm_|SwineOnFarm_|GoatsOnFarm_|SheepOnFarm_|WaterSource_|FreqBirdHandling_|AnyABXUse_|LengthFeedRestrixProcess_|Seasons_|FlockAgeDays_|Breed_|FlockSize_|AnimalSource_|ProcessingType_|SkinOnOff_|ChillingMethod_|ScalderTempC_|RinseWaterSource_|RinseWaterChlor_|TransportTime_|StorageTempC_|StorageTimeD_')]\n", "\n", "display(cor_physicochem.head(10))\n", "display(cor_physicochem2.head(10))\n", "\n", "FECES = cor_physicochem[cor_physicochem['Sample'].str.contains('FECES')].copy()\n", "FECES.replace({'_FECES': ''}, regex=True, inplace=True)\n", "FECES.rename(columns={'Sample':'Farm Practices'}, inplace = True)\n", "FECES = pd.pivot_table(FECES,index=['Farm Practices'],columns='Probiotics',values='Correlation',fill_value=0)\n", "\n", "#Probiotics correlation (cor) with Farm Practices (FarmPractice) in FECES\n", "FECES2 = cor_physicochem2[cor_physicochem2['Sample'].str.contains('FECES')].copy()\n", "FECES2.replace({'_FECES': ''}, regex=True, inplace=True)\n", "FECES2.rename(columns={'Sample':'Farm Practices'}, inplace = True)\n", "FECES2 = pd.pivot_table(FECES2,index=['Farm Practices'],columns='Potential_Probiotics',values='Correlation',fill_value=0)\n", "\n", "#Plots layout\n", "plt.rcParams[\"figure.figsize\"] = [10, 6]\n", "plt.rcParams[\"figure.autolayout\"] = True\n", "fig, axs = plt.subplots(ncols=2)\n", "\n", "\n", "#Making the plots\n", "sns.heatmap(FECES,cmap=\"RdYlGn\", vmin=-1, vmax=1, ax=axs[0]).set(title='FECES')\n", "sns.heatmap(FECES2,cmap=\"RdYlGn\", vmin=-1, vmax=1, ax=axs[1]).set(title='FECES')\n", "\n", "plt.suptitle('Probiotics Correlation with Physicochemicals in Feces', y=1.05, fontsize=20)\n", "plt.savefig(\"Figure 5.jpg\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.6" } }, "nbformat": 4, "nbformat_minor": 4 }