The prognostic value MEK pathway-associated estrogen receptor signaling activity in female cancers¶

Chun Wai Ng, Yvonne T.M. Tsang, David M. Gershenson, Kwong-Kwok Wong

In [1]:
import sys
print("Python version:", sys.version)
!pip list | grep 'pandas\|numpy\|matplotlib\|rpy2\|sklearn\|scipy\|kaplanmeier\|seaborn\|gseapy'
import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import kaplanmeier as km
from statsmodels.stats.multitest import multipletests
from scipy import stats
from scipy.stats import chi2_contingency, chisquare, spearmanr, ttest_ind
import math
import json
import gseapy as gp
from gseapy import gseaplot, heatmap, dotplot
import seaborn as sns
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import rpy2.robjects as ro
from rpy2.robjects.packages import importr
from rpy2.robjects import pandas2ri
from rpy2.robjects.conversion import localconverter
from rpy2.robjects import Formula
import rpy2.robjects as robjects
rinstalled = robjects.globalenv.find("installed.packages")
rversion = robjects.globalenv.find("R.Version")
rpkgs = rinstalled()
rvers = rpkgs.rx(robjects.StrVector(["GSVA","DESeq2"]), robjects.StrVector(["Version"]))
print(rversion().rx2("version.string"))
print("GSVA Version:", rvers[0])
print("DESeq2 Version:", rvers[1])

Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0]
geopandas                 0.13.2
gseapy                    1.0.4
kaplanmeier               0.1.9
matplotlib                3.8.1
matplotlib-inline         0.1.6
matplotlib-scalebar       0.8.1
numpy                     1.26.2
numpy-groupies            0.10.2
numpyro                   0.13.2
pandas                    2.1.3
rpy2                      3.5.5
scipy                     1.11.2
seaborn                   0.12.2
[1] "R version 4.3.2 (2023-10-31)"

GSVA Version: 1.48.3
DESeq2 Version: 1.40.2
In [2]:
def get_cBioportal_survival(pfs_path, os_path, dfs_path, dss_path):
    data_pfs = pd.read_table(pfs_path, index_col=1, header=0).drop_duplicates().dropna()
    data_os = pd.read_table(os_path, index_col=1, header=0).drop_duplicates().dropna()
    data_dfs = pd.read_table(dfs_path, index_col=1, header=0).drop_duplicates().dropna()
    data_dss = pd.read_table(dss_path, index_col=1, header=0).drop_duplicates().dropna()

    data_pfs = data_pfs[~data_pfs.index.duplicated(keep="first")]
    data_pfs["PFS_STATUS"] = [s[0] for s in data_pfs["PFS_STATUS"]]
    data_os["OS_STATUS"] = [s[0] for s in data_os["OS_STATUS"]]
    data_dfs["DFS_STATUS"] = [s[0] for s in data_dfs["DFS_STATUS"]]
    data_dss["DSS_STATUS"] = [s[0] for s in data_dss["DSS_STATUS"]]
    data_dfs = data_dfs[~data_dfs.index.duplicated(keep="first")]
    data_dss = data_dss[~data_dss.index.duplicated(keep="first")]
    data_pfs = data_pfs[~data_pfs.index.duplicated(keep="first")]
    data_os = data_os[~data_os.index.duplicated(keep="first")]
    return data_pfs, data_os, data_dfs, data_dss

def load_gtex_gene(file_location):
    df = pd.read_table(file_location, skiprows=2, header=0, index_col=2).iloc[:, 2:]
    return df

def get_gdc_tpm_genecount(file_path, meta_file_path):
    ov_gene_meta = pd.read_table(meta_file_path, index_col=8, header=0)
    ov_gene_filenames = [ov_gene_meta.iloc[r, 12] + "/" + ov_gene_meta.iloc[r, 11] for r in range(len(ov_gene_meta))]
    ov_gene_caseid = ov_gene_meta.index
    ov_gene = pd.DataFrame()
    ov_genecount = pd.DataFrame()
    for i, f in enumerate(ov_gene_filenames):
        file = pd.read_table(f"{file_path}/{f}", skiprows=1, header=0, index_col=1).iloc[4:]
        caseid = ov_gene_caseid[i]
        print(caseid)
        tpm = file[["tpm_unstranded"]]
        tpm.columns = [caseid]
        ov_gene = pd.concat([ov_gene, tpm], axis=1)
        gc = file[["unstranded"]]
        gc.columns = [caseid]
        ov_genecount = pd.concat([ov_genecount, gc], axis=1)
    ov_gene = ov_gene.groupby(level=0).mean()
    q = ~ov_gene.columns.duplicated(keep="first")
    ov_gene = ov_gene.loc[:,q]
    ov_genecount = ov_genecount.groupby(level=0).mean()
    q = ~ov_genecount.columns.duplicated(keep="first")
    ov_genecount = ov_genecount.loc[:,q]
    return ov_gene, ov_genecount

def genecount_to_tmm_for_gsea(genecount_df): 
    import conorm
    nf  = conorm.tmm_norm_factors(genecount_df)
    gdc_gc_tmm = conorm.cpm(genecount_df, norm_factors=nf)
    gdc_gc_tmm.insert(0, "NAME", None)
    gdc_gc_tmm.reset_index()
    gdc_gc_tmm.rename(columns = {'index':'GENE'})
    return gdc_gc_tmm

def gsea(tmm_df, pheno, genesets):
    gs_res = gp.gsea(data=tmm_df, # or data='./P53_resampling_data.txt'
                     gene_sets=genesets, # or enrichr library names
                     cls=pheno, # cls=class_vector
                     # set permutation_type to phenotype if samples >=15
                     permutation_type='phenotype',
                     permutation_num=1000, # reduce number to speed up test
                     outdir=None,  # do not write output to disk
                     method='signal_to_noise',
                     threads=4, seed=0)
    return gs_res

def plotting_gsea(gsea_res, geneset_name):
    index = gsea_res.res2d[gsea_res.res2d["Term"]==geneset_name].index[0]
    terms = gsea_res.res2d.Term
    gseaplot(gsea_res.ranking, term=terms[index], **gsea_res.results[terms[index]])
    ax = heatmap(df = gsea_res.heatmat.loc[gsea_res.res2d.Lead_genes[index].split(";")], z_score=0, title=terms[index], figsize=(20,45), xticklabels=True)
    ax = dotplot(gsea_res.res2d,
         column="FDR q-val",
#              title='HALLMARK',
         x="Gene_set",
         cmap=plt.cm.viridis,
         size=3, # adjust dot size
         figsize=(4,5), cutoff=0.25, show_ring=False)
        
        
def deseq2_py2r(gc_matrix_df, pheno_df, formula, contrast):
    rbase = importr('base')
    deseq2r = importr("DESeq2")
    DESeqDataSetFromMatrix_r = robjects.globalenv.find('DESeqDataSetFromMatrix')
    mode_r = robjects.globalenv.find('mode')
    sapply = robjects.globalenv.find('sapply')
    as_integer_r = robjects.globalenv.find('as.integer')
    deseq_r = robjects.globalenv.find('DESeq')
    results_r = robjects.globalenv.find('results')
    print("Converting df")
    with localconverter(ro.default_converter + pandas2ri.converter):
        geneExpressionProfile_r = ro.conversion.py2rpy(gc_matrix_df)

    with localconverter(ro.default_converter + pandas2ri.converter):
        pheno_r = ro.conversion.py2rpy(pheno_df)

    formula_r = Formula(formula)
    dds_r = DESeqDataSetFromMatrix_r(countData = rbase.as_matrix(sapply(geneExpressionProfile_r, as_integer_r)),
                                  colData = rbase.as_matrix(pheno_r),
                                  design = formula_r)
    dds_r = deseq_r(dds_r)
    res_r = results_r(dds_r, contrast=ro.StrVector(contrast))

    return pd.DataFrame(np.array(rbase.as_matrix(res_r)), index=gc_matrix_df.index, columns=["baseMean","log2FoldChange","lfcSE","stat","pvalue", "padj"])

def grouping4(esr1_es_df, esr1_threshold, es_threshold):
    groups_label = np.full(len(esr1_es_df), "____________________")

    esr1low = esr1_es_df["ESR1"]<=esr1_threshold
    eslow = esr1_es_df["EARLY"]<=es_threshold

    esr1low_eslow_query = np.logical_and(esr1low, eslow)

    groups_label[esr1low_eslow_query] = "ESR1_low_EERES_low"
#     print("ESR1_low_EERES_low", len(groups_label[esr1low_eslow_query]))
    eshigh = esr1_es_df["EARLY"]>es_threshold
    esr1low_eshigh_query = np.logical_and(esr1low, eshigh)

    groups_label[esr1low_eshigh_query] = "ESR1_low_EERES_high"
#     print("ESR1_low_EERES_high", len(groups_label[esr1low_eshigh_query]))
    esr1high = esr1_es_df["ESR1"]>esr1_threshold
    esr1high_eslow_query = np.logical_and(esr1high, eslow)

    groups_label[esr1high_eslow_query] = "ESR1_high_EERES_low"
#     print("ESR1_high_EERES_low", len(groups_label[esr1high_eslow_query]))
    
    esr1high_eshigh_query = np.logical_and(esr1high, eshigh)
#     print(esr1high_eshigh_query.sum())
    
    groups_label[esr1high_eshigh_query] = "ESR1_high_EERES_high"
    esr1_es_df["group"] = groups_label
    
    return esr1_es_df
    
def plotting_4groups(survival_esr1_es_df, survivalType, esr1_threshold, es_threshold):

    groups_label = np.full(len(survival_esr1_es_df), "____________________")

    esr1low = survival_esr1_es_df["ESR1"]<=esr1_threshold
    eslow = survival_esr1_es_df["EARLY"]<=es_threshold

    esr1low_eslow_query = np.logical_and(esr1low, eslow)

    groups_label[esr1low_eslow_query] = "ESR1_low_EERES_low"
#     print("ESR1_low_EERES_low", len(groups_label[esr1low_eslow_query]))
    eshigh = survival_esr1_es_df["EARLY"]>es_threshold
    esr1low_eshigh_query = np.logical_and(esr1low, eshigh)

    groups_label[esr1low_eshigh_query] = "ESR1_low_EERES_high"
#     print("ESR1_low_EERES_high", len(groups_label[esr1low_eshigh_query]))
    esr1high = survival_esr1_es_df["ESR1"]>esr1_threshold
    esr1high_eslow_query = np.logical_and(esr1high, eslow)

    groups_label[esr1high_eslow_query] = "ESR1_high_EERES_low"
#     print("ESR1_high_EERES_low", len(groups_label[esr1high_eslow_query]))
    
    esr1high_eshigh_query = np.logical_and(esr1high, eshigh)
#     print(esr1high_eshigh_query.sum())
    groups_label[esr1high_eshigh_query] = "ESR1_high_EERES_high"
#     print("ESR1_high_EERES_high", len(groups_label[esr1high_eshigh_query]))
    
#     print(np.unique(groups_label, return_counts=True))
#     print("ESR1_low_EERES_low", survival_esr1_es_df[esr1low_eslow_query][f"{survivalType}_MONTHS"].median())
#     print("ESR1_low_EERES_high", survival_esr1_es_df[esr1low_eshigh_query][f"{survivalType}_MONTHS"].median())
#     print("ESR1_high_EERES_low", survival_esr1_es_df[esr1high_eslow_query][f"{survivalType}_MONTHS"].median())
#     print("ESR1_high_EERES_high", survival_esr1_es_df[esr1high_eshigh_query][f"{survivalType}_MONTHS"].median())
#     kmf = KaplanMeierFitter(label="waltons_data")
#     kmf.fit(survival_esr1_es_df[esr1low_eslow_query][f"{survivalType}_MONTHS"], survival_esr1_es_df[esr1low_eslow_query][f"{survivalType}_STATUS"])
#     print("ESR1_low_EERES_low",kmf.median_survival_time_)
#     kmf = KaplanMeierFitter(label="waltons_data")
#     kmf.fit(survival_esr1_es_df[esr1low_eshigh_query][f"{survivalType}_MONTHS"], survival_esr1_es_df[esr1low_eshigh_query][f"{survivalType}_STATUS"])
#     print("ESR1_low_EERES_high", kmf.median_survival_time_)
#     kmf = KaplanMeierFitter(label="waltons_data")
#     kmf.fit(survival_esr1_es_df[esr1high_eslow_query][f"{survivalType}_MONTHS"], survival_esr1_es_df[esr1high_eslow_query][f"{survivalType}_STATUS"])
#     print("ESR1_high_EERES_low", kmf.median_survival_time_)
#     kmf = KaplanMeierFitter(label="waltons_data")
#     kmf.fit(survival_esr1_es_df[esr1high_eshigh_query][f"{survivalType}_MONTHS"], survival_esr1_es_df[esr1high_eshigh_query][f"{survivalType}_STATUS"])
#     print("ESR1_high_EERES_high", kmf.median_survival_time_)
    result_df = pd.DataFrame(index=["ESR1_low_EERES_low", "ESR1_low_EERES_high", "ESR1_high_EERES_low", "ESR1_high_EERES_high"], columns=["ESR1_low_EERES_low", "ESR1_low_EERES_high", "ESR1_high_EERES_low", "ESR1_high_EERES_high"])
    results1 = km.fit(survival_esr1_es_df[esr1low_eshigh_query|esr1low_eslow_query][f"{survivalType}_MONTHS"], survival_esr1_es_df[esr1low_eshigh_query|esr1low_eslow_query][f"{survivalType}_STATUS"], groups_label[esr1low_eshigh_query|esr1low_eslow_query])
    print("ESR1_low_EERES_low", "vs", "ESR1_low_EERES_high", results1['logrank_P'])
    result_df.loc["ESR1_low_EERES_low", "ESR1_low_EERES_high"] = results1['logrank_P']
    results1 = km.fit(survival_esr1_es_df[esr1high_eslow_query|esr1low_eslow_query][f"{survivalType}_MONTHS"], survival_esr1_es_df[esr1high_eslow_query|esr1low_eslow_query][f"{survivalType}_STATUS"], groups_label[esr1high_eslow_query|esr1low_eslow_query])
    print("ESR1_low_EERES_low", "vs", "ESR1_high_EERES_low", results1['logrank_P'])
    result_df.loc["ESR1_low_EERES_low", "ESR1_high_EERES_low"] = results1['logrank_P']
    results3 = km.fit(survival_esr1_es_df[esr1high_eshigh_query|esr1low_eslow_query][f"{survivalType}_MONTHS"], survival_esr1_es_df[esr1high_eshigh_query|esr1low_eslow_query][f"{survivalType}_STATUS"], groups_label[esr1high_eshigh_query|esr1low_eslow_query])
    print("ESR1_low_EERES_low", "vs", "ESR1_high_EERES_high", results3['logrank_P'])
    result_df.loc["ESR1_low_EERES_low", "ESR1_high_EERES_high"] = results3['logrank_P']
    results2 = km.fit(survival_esr1_es_df[esr1low_eshigh_query|esr1high_eslow_query][f"{survivalType}_MONTHS"], survival_esr1_es_df[esr1low_eshigh_query|esr1high_eslow_query][f"{survivalType}_STATUS"], groups_label[esr1low_eshigh_query|esr1high_eslow_query])
    print("ESR1_low_EERES_high", "vs", "ESR1_high_EERES_low", results2['logrank_P'])
    result_df.loc["ESR1_low_EERES_high", "ESR1_high_EERES_low"] = results2['logrank_P']
    results2 = km.fit(survival_esr1_es_df[esr1low_eshigh_query|esr1high_eshigh_query][f"{survivalType}_MONTHS"], survival_esr1_es_df[esr1low_eshigh_query|esr1high_eshigh_query][f"{survivalType}_STATUS"], groups_label[esr1low_eshigh_query|esr1high_eshigh_query])
    print("ESR1_low_EERES_high", "vs", "ESR1_high_EERES_high", results2['logrank_P'])
    result_df.loc["ESR1_low_EERES_high", "ESR1_high_EERES_high"] = results2['logrank_P']
    results4 = km.fit(survival_esr1_es_df[esr1high_eshigh_query|esr1high_eslow_query][f"{survivalType}_MONTHS"], survival_esr1_es_df[esr1high_eshigh_query|esr1high_eslow_query][f"{survivalType}_STATUS"], groups_label[esr1high_eshigh_query|esr1high_eslow_query])
    print("ESR1_high_EERES_low", "vs", "ESR1_high_EERES_high", results4['logrank_P'])
    result_df.loc["ESR1_high_EERES_low", "ESR1_high_EERES_high"] = results4['logrank_P']
    print(result_df)
    results = km.fit(survival_esr1_es_df[f"{survivalType}_MONTHS"], survival_esr1_es_df[f"{survivalType}_STATUS"], groups_label)
    km.plot(results, full_ylim=True, y_percentage=True, fontsize=15)
    plt.show()

def plotting_2groups(survival_esr1_es_df, survivalType, esr1_threshold, es_threshold):
    results = km.fit(survival_esr1_es_df[f"{survivalType}_MONTHS"], survival_esr1_es_df[f"{survivalType}_STATUS"], (survival_esr1_es_df["EARLY"]>es_threshold).apply(lambda x: "High EERES" if x else "Low EERES"))
    km.plot(results, full_ylim=True, y_percentage=True, fontsize=15)
    plt.show()
    results = km.fit(survival_esr1_es_df[f"{survivalType}_MONTHS"], survival_esr1_es_df[f"{survivalType}_STATUS"], (survival_esr1_es_df["ESR1"]>esr1_threshold).apply(lambda x: "High ESR1" if x else "Low ESR1"))
    km.plot(results, full_ylim=True, y_percentage=True, fontsize=15)
    plt.show()
    
def plotting_scatter_esr1_es(esr1_es_df, esr1_threshold, es_threshold):    
    plt.scatter(np.log2(esr1_es_df["ESR1"] + 1), esr1_es_df["EARLY"])
    plt.xlabel("log2[ESR1+1]", weight="bold", fontsize=12, labelpad=0)
    plt.ylabel("EERES", weight="bold", fontsize=12, labelpad=-5)

    x = (np.log2(esr1_threshold+1), np.log2(esr1_threshold+1))
    y = (esr1_es_df["EARLY"].min() - 0.1, esr1_es_df["EARLY"].max() + 0.1)
    plt.plot(x, y, "--", color='orange')
    plt.plot([np.log2(esr1_es_df["ESR1"]+1).min() - 0.5, np.log2(esr1_es_df["ESR1"]+1).max() + 0.5], [es_threshold, es_threshold], "--", color='orange')
    plt.ylim([esr1_es_df["EARLY"].min() - 0.1, esr1_es_df["EARLY"].max() + 0.1])
    plt.xlim([np.log2(esr1_es_df["ESR1"]+1).min() - 0.5, np.log2(esr1_es_df["ESR1"]+1).max() + 0.5])
    sr, sp = stats.spearmanr(np.log2(esr1_es_df["ESR1"]), esr1_es_df["EARLY"])
    plt.title(f"Spearman R={sr:.3f}, p={sp:.3e}", weight="bold")
    plt.show()


def groups_clinical(gdc_clinical_df, stage_colname, age_colname, grouping):
    clinical_grouping_df = gdc_clinical_df.join(grouping, how='inner')
    clinical_grouping_df.loc[clinical_grouping_df[stage_colname]=="Stage I", stage_colname] = "T1"
    clinical_grouping_df.loc[clinical_grouping_df[stage_colname]=="Stage IB", stage_colname] = "T1"
    clinical_grouping_df.loc[clinical_grouping_df[stage_colname]=="Stage IC", stage_colname] = "T1"
    clinical_grouping_df.loc[clinical_grouping_df[stage_colname]=="Stage IA", stage_colname] = "T1"
    clinical_grouping_df.loc[clinical_grouping_df[stage_colname]=="Stage II", stage_colname] = "T2"
    clinical_grouping_df.loc[clinical_grouping_df[stage_colname]=="Stage IIA", stage_colname] = "T2"
    clinical_grouping_df.loc[clinical_grouping_df[stage_colname]=="Stage IIB", stage_colname] = "T2"
    clinical_grouping_df.loc[clinical_grouping_df[stage_colname]=="Stage III", stage_colname] = "T3"
    clinical_grouping_df.loc[clinical_grouping_df[stage_colname]=="Stage IIIA", stage_colname] = "T3"
    clinical_grouping_df.loc[clinical_grouping_df[stage_colname]=="Stage IIIB", stage_colname] = "T3"
    clinical_grouping_df.loc[clinical_grouping_df[stage_colname]=="Stage IIIC", stage_colname] = "T3"
    clinical_grouping_df.loc[clinical_grouping_df[stage_colname]=="Stage IV", stage_colname] = "T4"
    clinical_grouping_df.loc[clinical_grouping_df[stage_colname]=="Stage IVB", stage_colname] = "T4"
    clinical_grouping_df.loc[clinical_grouping_df[stage_colname]=="Stage IVD", stage_colname] = "T4"
    clinical_grouping_df.loc[clinical_grouping_df[stage_colname]=="Stage X", stage_colname] = "TX"
    clinical_stage_grouped = clinical_grouping_df.groupby(["group", stage_colname]).count().iloc[:,:1]
    clinical_stage_count = pd.DataFrame(np.zeros((4,4)), index = ["ESR1_high_EERES_high","ESR1_high_EERES_low","ESR1_low_EERES_high","ESR1_low_EERES_low"], columns = ["T1", "T2", "T3", "T4"])
    for r in clinical_stage_grouped.iterrows():
        clinical_stage_count.loc[r[0][0], r[0][1]] = r[1].values[0]
        
        
    ages_tmp = clinical_grouping_df[[age_colname]]
    ages_tmp.loc[ages_tmp[age_colname]=="[Not Available]", age_colname] = "10000"
    ages_tmp = ages_tmp.astype(int)
    clinical_grouping_df.loc[((ages_tmp[age_colname]>=21)&(ages_tmp[age_colname]<=40)), age_colname] = "21-40"
    clinical_grouping_df.loc[((ages_tmp[age_colname]>=41)&(ages_tmp[age_colname]<=60)), age_colname] = "41-60"
    clinical_grouping_df.loc[((ages_tmp[age_colname]>=61)&(ages_tmp[age_colname]<=80)), age_colname] = "61-80"
    clinical_grouping_df.loc[((ages_tmp[age_colname]>=81)&(ages_tmp[age_colname]<=100)), age_colname] = "81-100"
    clinical_age_count = pd.DataFrame(np.zeros((4,4)), index = ["ESR1_high_EERES_high","ESR1_high_EERES_low","ESR1_low_EERES_high","ESR1_low_EERES_low"], columns = ["21-40", "41-60", "61-80", "81-100"])
    clinical_age_grouped = clinical_grouping_df.groupby(["group", age_colname]).count()
    for r in clinical_age_grouped.iterrows():
        clinical_age_count.loc[r[0][0], r[0][1]] = r[1].values[0]
    return clinical_stage_count, clinical_age_count
    
def gsva_py2r(geneExpressionProfile, gene_sets):
    rbase = importr('base')
    print("Converting df")
    with localconverter(ro.default_converter + pandas2ri.converter):
        geneExpressionProfile_r = ro.conversion.py2rpy(geneExpressionProfile)
    gene_sets_r = ro.ListVector(gene_sets)
    gsvar = importr("GSVA")
    es = gsvar.gsva(rbase.as_matrix(geneExpressionProfile_r), gene_sets_r)
    es_df = pd.DataFrame(np.array(es.transpose()), index=es.colnames, columns=es.rownames)
    return es_df
    
def ERs_vs_EERES_table(ERs_vs_EERES_df):
    df = pd.DataFrame(index=["ESR1","ESR2","ESRRA","ESRRB","ESRRG","GPER1"], columns=["R","p"])
    for er in ["ESR1","ESR2","ESRRA","ESRRB","ESRRG","GPER1"]:
        s, p = stats.spearmanr(np.log2(ERs_vs_EERES_df[er]+1), ERs_vs_EERES_df["EARLY"])
        p = f"{p:.2e}"
        df.loc[er,"R"] = s
        df.loc[er,"p"] = p
    return df

Loading gene expression for GTEx (breast, ovary, uterus, cervix) and survival and gene expression for TCGA (BRCA, OV, UCEC, CESC)¶

In [3]:
pfs_path = "Data/gdc/TCGA-BRCA/clinical/cBioportal/KM_Plot__Progression_Free__Survival_(months).txt"
os_path = "Data/gdc/TCGA-BRCA/clinical/cBioportal/KM_Plot__Overall_Survival__(months).txt"
dfs_path = "Data/gdc/TCGA-BRCA/clinical/cBioportal/KM_Plot__Disease_Free__Survival_(months).txt"
dss_path = "Data/gdc/TCGA-BRCA/clinical/cBioportal/KM_Plot__Disease-specific_Survival__(months).txt"
brca_pfs, brca_os, brca_dfs, brca_dss = get_cBioportal_survival(pfs_path, os_path, dfs_path, dss_path)

pfs_path = "Data/gdc/TCGA-OV/Clinical/cBioportal/KM_Plot__Progression_Free__Survival_(months).txt"
os_path = "Data/gdc/TCGA-OV/Clinical/cBioportal/KM_Plot__Overall_Survival__(months).txt"
dfs_path = "Data/gdc/TCGA-OV/Clinical/cBioportal/KM_Plot__Disease_Free__Survival_(months).txt"
dss_path = "Data/gdc/TCGA-OV/Clinical/cBioportal/KM_Plot__Disease-specific_Survival__(months).txt"
ov_pfs, ov_os, ov_dfs, ov_dss = get_cBioportal_survival(pfs_path, os_path, dfs_path, dss_path)

pfs_path = "Data/gdc/TCGA-UCEC/Clinical/cBioportal/KM_Plot__Progression_Free__Survival_(months).txt"
os_path = "Data/gdc/TCGA-UCEC/Clinical/cBioportal/KM_Plot__Overall_Survival__(months).txt"
dfs_path = "Data/gdc/TCGA-UCEC/Clinical/cBioportal/KM_Plot__Disease_Free__Survival_(months).txt"
dss_path = "Data/gdc/TCGA-UCEC/Clinical/cBioportal/KM_Plot__Disease-specific_Survival__(months).txt"
ucec_pfs, ucec_os, ucec_dfs, ucec_dss = get_cBioportal_survival(pfs_path, os_path, dfs_path, dss_path)

pfs_path = "Data/gdc/TCGA-CESC/Clinical/cBioportal/KM_Plot__Progression_Free__Survival_(months).txt"
os_path = "Data/gdc/TCGA-CESC/Clinical/cBioportal/KM_Plot__Overall_Survival__(months).txt"
dfs_path = "Data/gdc/TCGA-CESC/Clinical/cBioportal/KM_Plot__Disease_Free__Survival_(months).txt"
dss_path = "Data/gdc/TCGA-CESC/Clinical/cBioportal/KM_Plot__Disease-specific_Survival__(months).txt"
cesc_pfs, cesc_os, cesc_dfs, cesc_dss = get_cBioportal_survival(pfs_path, os_path, dfs_path, dss_path)
In [4]:
# Load GTEx and TCGA breast, ovary, uterine, cervix gene expression

brca_gdc_tpm, brca_gdc_genecount = get_gdc_tpm_genecount("Data/gdc/TCGA-BRCA/Gene Expression/download", "Data/gdc/TCGA-BRCA/Gene Expression/File_metadata.txt")
brca_gtex_gc = load_gtex_gene("Data/GTEx/gene_reads_2017-06-05_v8_breast_mammary_tissue.gct.txt").groupby(level=0).mean()
brca_gtex_gdc_gc = brca_gtex_gc.join(brca_gdc_genecount, how='inner')
brca_gtex_gdc_pheno = np.array(["Normal" for i in range(len(brca_gtex_gc.columns))] + ["Cancer" for i in range(len(brca_gdc_genecount.columns))])
brca_gtex_gdc_pheno_df = pd.DataFrame(brca_gtex_gdc_pheno, index=brca_gtex_gdc_gc.columns, columns=["type"])
brca_gtex_gdc_tmm = genecount_to_tmm_for_gsea(brca_gtex_gdc_gc)
brca_gtex_tpm_location = "Data/GTEx/gene_tpm_2017-06-05_v8_breast_mammary_tissue.gct.txt"
brca_gtex_tpm = load_gtex_gene(brca_gtex_tpm_location)

ov_gdc_tpm, ov_gdc_genecount = get_gdc_tpm_genecount("Data/gdc/TCGA-OV/Gene Expression/download", "Data/gdc/TCGA-OV/Gene Expression/File_metadata.txt")
ov_gtex_gc = load_gtex_gene("Data/GTEx/gene_reads_2017-06-05_v8_ovary.gct.txt").groupby(level=0).mean()
ov_gtex_gdc_gc = ov_gtex_gc.join(ov_gdc_genecount, how='inner')
ov_gtex_gdc_pheno = np.array(["Normal" for i in range(len(ov_gtex_gc.columns))] + ["Cancer" for i in range(len(ov_gdc_genecount.columns))])
ov_gtex_gdc_pheno_df = pd.DataFrame(ov_gtex_gdc_pheno, index=ov_gtex_gdc_gc.columns, columns=["type"])
ov_gtex_gdc_tmm = genecount_to_tmm_for_gsea(ov_gtex_gdc_gc)
ov_gtex_tpm_location = "Data/GTEx/gene_tpm_2017-06-05_v8_ovary.gct.txt"
ov_gtex_tpm = load_gtex_gene(ov_gtex_tpm_location)

ucec_gdc_tpm, ucec_gdc_genecount = get_gdc_tpm_genecount("Data/gdc/TCGA-UCEC/Gene Expression/download", "Data/gdc/TCGA-UCEC/Gene Expression/File_metadata.txt")
ucec_gtex_gc = load_gtex_gene("Data/GTEx/gene_tpm_2017-06-05_v8_uterus.gct.txt").groupby(level=0).mean()
ucec_gtex_gdc_gc = ucec_gtex_gc.join(ucec_gdc_genecount, how='inner')
ucec_gtex_gdc_pheno = np.array(["Normal" for i in range(len(ucec_gtex_gc.columns))] + ["Cancer" for i in range(len(ucec_gdc_genecount.columns))])
ucec_gtex_gdc_pheno_df = pd.DataFrame(ucec_gtex_gdc_pheno, index=ucec_gtex_gdc_gc.columns, columns=["type"])
ucec_gtex_gdc_tmm = genecount_to_tmm_for_gsea(ucec_gtex_gdc_gc)
ucec_gtex_tpm_location = "Data/GTEx/gene_tpm_2017-06-05_v8_uterus.gct.txt"
ucec_gtex_tpm = load_gtex_gene(ucec_gtex_tpm_location)

cesc_gdc_tpm, cesc_gdc_genecount = get_gdc_tpm_genecount("Data/gdc/TCGA-CESC/Gene Expression/download", "Data/gdc/TCGA-CESC/Gene Expression/File_metadata.txt")
cesc_gtex_gc = load_gtex_gene("Data/GTEx/gene_reads_2017-06-05_v8_cervix_endocervix.gct.txt").groupby(level=0).mean()
cesc_gtex_gdc_gc = cesc_gtex_gc.join(cesc_gdc_genecount, how='inner')
cesc_gtex_gdc_pheno = np.array(["Normal" for i in range(len(cesc_gtex_gc.columns))] + ["Cancer" for i in range(len(cesc_gdc_genecount.columns))])
cesc_gtex_gdc_pheno_df = pd.DataFrame(cesc_gtex_gdc_pheno, index=cesc_gtex_gdc_gc.columns, columns=["type"])
cesc_gtex_gdc_tmm = genecount_to_tmm_for_gsea(cesc_gtex_gdc_gc)
cesc_gtex_tpm_location = "Data/GTEx/gene_tpm_2017-06-05_v8_cervix_endocervix.gct.txt"
cesc_gtex_tpm = load_gtex_gene(cesc_gtex_tpm_location)

TCGA-E2-A1L7
TCGA-E2-A1L7
TCGA-AR-A0U0
TCGA-BH-A28O
TCGA-A2-A0D4
TCGA-E9-A1R4
TCGA-AO-A1KQ
TCGA-AC-A62V
TCGA-D8-A143
TCGA-A2-A0SV
TCGA-AN-A0XW
TCGA-D8-A1XV
TCGA-A2-A4RW
TCGA-A7-A0CD
TCGA-E2-A1IG
TCGA-D8-A1XB
TCGA-C8-A134
TCGA-BH-A0BS
TCGA-AR-A2LE
TCGA-A2-A0CO
TCGA-E9-A1NA
TCGA-AN-A0AK
TCGA-E9-A1NA
TCGA-A7-A0DA
TCGA-E2-A572
TCGA-A2-A259
TCGA-BH-A28Q
TCGA-E2-A1IO
TCGA-AQ-A7U7
TCGA-AN-A0FD
TCGA-A8-A07G
TCGA-AO-A0JL
TCGA-B6-A0IM
TCGA-B6-A0IP
TCGA-GM-A2DF
TCGA-A2-A25B
TCGA-BH-A0B0
TCGA-AO-A0JD
TCGA-AN-A0FL
TCGA-E2-A14V
TCGA-AN-A0FF
TCGA-C8-A138
TCGA-E2-A14R
TCGA-AC-A2BM
TCGA-A1-A0SP
TCGA-A2-A0CQ
TCGA-A8-A08J
TCGA-BH-A6R8
TCGA-E9-A1QZ
TCGA-A8-A0AB
TCGA-BH-A0H9
TCGA-AC-A3W7
TCGA-B6-A0IE
TCGA-A8-A07I
TCGA-BH-A0BQ
TCGA-LD-A9QF
TCGA-BH-A18T
TCGA-A7-A26G
TCGA-BH-A0H7
TCGA-D8-A1XG
TCGA-BH-A0E0
TCGA-E2-A14U
TCGA-BH-A0E0
TCGA-S3-AA10
TCGA-BH-A0B7
TCGA-A8-A076
TCGA-B6-A0RN
TCGA-E9-A244
TCGA-E2-A1LK
TCGA-LL-A5YL
TCGA-A8-A06Y
TCGA-BH-A0AZ
TCGA-B6-A0X0
TCGA-EW-A1P4
TCGA-BH-A0BG
TCGA-D8-A1JD
TCGA-BH-A18K
TCGA-D8-A27F
TCGA-A8-A09I
TCGA-A2-A0ST
TCGA-BH-A1FH
TCGA-A7-A0D9
TCGA-B6-A0IN
TCGA-E2-A15K
TCGA-E2-A15K
TCGA-E2-A14Y
TCGA-LD-A7W5
TCGA-B6-A0I5
TCGA-A2-A3XV
TCGA-A2-A0SX
TCGA-EW-A1OY
TCGA-AN-A0FJ
TCGA-AR-A24Z
TCGA-D8-A1XT
TCGA-B6-A0RL
TCGA-E9-A22E
TCGA-BH-A18P
TCGA-E2-A15H
TCGA-E9-A1NG
TCGA-D8-A1JK
TCGA-AC-A7VB
TCGA-AO-A0J2
TCGA-BH-A0B8
TCGA-D8-A27N
TCGA-AC-A8OS
TCGA-BH-A0DO
TCGA-E2-A14Z
TCGA-E2-A1BC
TCGA-AR-A256
TCGA-E2-A1BC
TCGA-A7-A426
TCGA-A8-A08B
TCGA-E2-A15C
TCGA-AC-A5XS
TCGA-OL-A6VR
TCGA-BH-A18Q
TCGA-AR-A24R
TCGA-BH-A0DG
TCGA-EW-A1PG
TCGA-A8-A08H
TCGA-BH-A0DD
TCGA-E2-A1B4
TCGA-D8-A27H
TCGA-E2-A1BD
TCGA-AO-A03T
TCGA-B6-A1KN
TCGA-A7-A5ZV
TCGA-BH-A2L8
TCGA-A2-A0YH
TCGA-D8-A1JI
TCGA-AR-A1AH
TCGA-BH-A0HW
TCGA-EW-A1J3
TCGA-A7-A5ZX
TCGA-EW-A423
TCGA-A2-A25E
TCGA-E2-A153
TCGA-C8-A26Y
TCGA-OL-A66H
TCGA-AQ-A04L
TCGA-BH-A0WA
TCGA-BH-A1EX
TCGA-A8-A08C
TCGA-BH-A0C3
TCGA-BH-A0DE
TCGA-AN-A0AS
TCGA-BH-A0GY
TCGA-AR-A1AN
TCGA-BH-A18S
TCGA-AC-A23H
TCGA-AC-A23H
TCGA-OL-A5RZ
TCGA-E2-A15M
TCGA-E9-A1RC
TCGA-GM-A2DN
TCGA-B6-A0I1
TCGA-EW-A1IW
TCGA-BH-A42V
TCGA-AR-A251
TCGA-AC-A8OP
TCGA-D8-A1XU
TCGA-BH-A18L
TCGA-B6-A0IK
TCGA-BH-A18L
TCGA-BH-A0B1
TCGA-A8-A06Q
TCGA-D8-A27R
TCGA-E9-A22G
TCGA-AO-A03N
TCGA-E2-A1LE
TCGA-3C-AAAU
TCGA-E2-A1IG
TCGA-LL-A9Q3
TCGA-AR-A2LM
TCGA-BH-A0HA
TCGA-AR-A0TS
TCGA-BH-A0HA
TCGA-AR-A24T
TCGA-D8-A1JP
TCGA-A8-A06U
TCGA-PE-A5DC
TCGA-BH-A0DI
TCGA-D8-A1XL
TCGA-S3-AA11
TCGA-AO-A0JJ
TCGA-B6-A1KI
TCGA-A1-A0SB
TCGA-3C-AALJ
TCGA-A2-A0CR
TCGA-D8-A1JG
TCGA-AN-A0FZ
TCGA-BH-A0BZ
TCGA-A8-A095
TCGA-BH-A1FE
TCGA-BH-A1FE
TCGA-A8-A07F
TCGA-BH-A1EN
TCGA-A2-A0EO
TCGA-A2-A3XT
TCGA-D8-A1Y2
TCGA-BH-A1FB
TCGA-BH-A1FB
TCGA-A7-A26F
TCGA-E9-A5UP
TCGA-A2-A1G0
TCGA-BH-A0H0
TCGA-BH-A0H9
TCGA-PL-A8LV
TCGA-BH-A209
TCGA-AO-A1KS
TCGA-BH-A0C0
TCGA-A8-A0A7
TCGA-BH-A0BF
TCGA-E9-A1RI
TCGA-BH-A0DP
TCGA-AC-A62X
TCGA-A8-A083
TCGA-A8-A08L
TCGA-D8-A146
TCGA-E2-A1IJ
TCGA-A1-A0SG
TCGA-D8-A1XC
TCGA-AO-A0J5
TCGA-BH-A0B5
TCGA-BH-A0B5
TCGA-3C-AALK
TCGA-AO-A0J9
TCGA-A2-A3KD
TCGA-C8-A12Q
TCGA-A8-A0A2
TCGA-C8-A1HK
TCGA-A2-A0CV
TCGA-D8-A27I
TCGA-EW-A424
TCGA-EW-A6S9
TCGA-D8-A1X6
TCGA-BH-A1F6
TCGA-BH-A0BL
TCGA-EW-A1J5
TCGA-AN-A046
TCGA-EW-A1IX
TCGA-E9-A1R5
TCGA-E2-A1L6
TCGA-AO-A0JC
TCGA-E2-A1LB
TCGA-E9-A54Y
TCGA-GM-A3XG
TCGA-EW-A2FW
TCGA-AR-A24S
TCGA-OL-A97C
TCGA-AR-A2LL
TCGA-D8-A1XA
TCGA-E2-A1B5
TCGA-E2-A15A
TCGA-E2-A14N
TCGA-A7-A3RF
TCGA-A2-A0D2
TCGA-D8-A1JA
TCGA-E2-A10C
TCGA-A2-A0EX
TCGA-BH-A0DT
TCGA-A7-A0CE
TCGA-A7-A0CE
TCGA-A2-A4S2
TCGA-AR-A5QM
TCGA-AR-A24W
TCGA-AC-A3TM
TCGA-4H-AAAK
TCGA-A7-A0CG
TCGA-BH-A18N
TCGA-D8-A27E
TCGA-A8-A06O
TCGA-C8-A8HQ
TCGA-A7-A26J
TCGA-B6-A0RU
TCGA-BH-A18V
TCGA-BH-A18V
TCGA-HN-A2NL
TCGA-E2-A1II
TCGA-AR-A24O
TCGA-A7-A26E
TCGA-GM-A2D9
TCGA-A8-A07W
TCGA-A2-A04W
TCGA-D8-A1XY
TCGA-EW-A1P7
TCGA-A8-A093
TCGA-AC-A2FB
TCGA-C8-A137
TCGA-E9-A1N3
TCGA-BH-A0DQ
TCGA-BH-A0DQ
TCGA-AC-A3BB
TCGA-C8-A1HJ
TCGA-B6-A0IO
TCGA-C8-A1HL
TCGA-A8-A07Z
TCGA-A2-A3XU
TCGA-A2-A0T3
TCGA-AN-A0XT
TCGA-AC-A62Y
TCGA-GM-A2DB
TCGA-A8-A07S
TCGA-BH-A204
TCGA-A2-A0D1
TCGA-AN-A0AT
TCGA-B6-A0IJ
TCGA-E2-A15E
TCGA-A8-A0A4
TCGA-A8-A09T
TCGA-A2-A3XY
TCGA-BH-A18U
TCGA-A8-A06Z
TCGA-A2-A3KC
TCGA-BH-A0H7
TCGA-BH-A0B7
TCGA-AR-A1AJ
TCGA-OL-A66O
TCGA-AC-A3YI
TCGA-BH-A0BT
TCGA-BH-A0AZ
TCGA-AO-A1KT
TCGA-AR-A0TT
TCGA-BH-A1FL
TCGA-B6-A409
TCGA-E9-A248
TCGA-A2-A3Y0
TCGA-BH-A1FH
TCGA-A7-A3IZ
TCGA-LL-A6FR
TCGA-E9-A22H
TCGA-A7-A0D9
TCGA-A1-A0SI
TCGA-AN-A0XO
TCGA-BH-A0HF
TCGA-B6-A0WS
TCGA-AR-A250
TCGA-S3-A6ZG
TCGA-BH-A1EU
TCGA-GI-A2C9
TCGA-GI-A2C9
TCGA-A2-A0CZ
TCGA-E9-A243
TCGA-EW-A2FS
TCGA-A7-A0CJ
TCGA-BH-A0BJ
TCGA-E2-A1LH
TCGA-E2-A1LH
TCGA-E2-A159
TCGA-LL-A7T0
TCGA-C8-A8HR
TCGA-D8-A1JF
TCGA-C8-A1HG
TCGA-GM-A2DC
TCGA-D8-A1JS
TCGA-A7-A4SE
TCGA-E9-A226
TCGA-E2-A15G
TCGA-BH-A208
TCGA-D8-A1JN
TCGA-AO-A03V
TCGA-D8-A3Z6
TCGA-BH-A0DL
TCGA-BH-A0DL
TCGA-GM-A3NY
TCGA-B6-A402
TCGA-E2-A153
TCGA-BH-A0E9
TCGA-E2-A1IK
TCGA-A2-A0YJ
TCGA-E2-A10A
TCGA-A8-A08I
TCGA-GM-A2DM
TCGA-A1-A0SM
TCGA-A2-A0CY
TCGA-A2-A25C
TCGA-A8-A0A9
TCGA-BH-A1FM
TCGA-BH-A0C3
TCGA-C8-A1HI
TCGA-OL-A5S0
TCGA-AQ-A54N
TCGA-AO-A0JG
TCGA-AO-A12G
TCGA-A2-A0T7
TCGA-B6-A0RP
TCGA-GM-A2DH
TCGA-BH-A0B2
TCGA-D8-A1XF
TCGA-A2-A0CK
TCGA-E9-A247
TCGA-EW-A6SD
TCGA-A7-A4SB
TCGA-E9-A1R2
TCGA-AR-A24Q
TCGA-A8-A091
TCGA-OL-A6VQ
TCGA-GI-A2C8
TCGA-GI-A2C8
TCGA-E2-A108
TCGA-B6-A0X5
TCGA-BH-A1FD
TCGA-BH-A0W5
TCGA-AO-A03U
TCGA-AC-A2FG
TCGA-BH-A0BM
TCGA-BH-A0BM
TCGA-AC-A2FF
TCGA-AC-A2FF
TCGA-AR-A24M
TCGA-E9-A6HE
TCGA-BH-A0EI
TCGA-AC-A4ZE
TCGA-E9-A22A
TCGA-A8-A08R
TCGA-AR-A0TW
TCGA-A8-A082
TCGA-BH-A5J0
TCGA-E2-A1B1
TCGA-C8-A1HN
TCGA-D8-A27G
TCGA-A2-A0CL
TCGA-GM-A5PV
TCGA-D8-A1XD
TCGA-AC-A3HN
TCGA-B6-A0WX
TCGA-B6-A0RT
TCGA-EW-A1J2
TCGA-C8-A278
TCGA-BH-A1F8
TCGA-D8-A27W
TCGA-A2-A1FW
TCGA-AC-A23E
TCGA-E2-A14S
TCGA-AR-A1AS
TCGA-D8-A27K
TCGA-BH-A209
TCGA-AR-A24K
TCGA-AC-A2QH
TCGA-A8-A081
TCGA-AR-A2LJ
TCGA-W8-A86G
TCGA-A8-A09R
TCGA-A1-A0SK
TCGA-E2-A574
TCGA-C8-A131
TCGA-EW-A1P8
TCGA-B6-A0WY
TCGA-AC-A6IV
TCGA-BH-A0B4
TCGA-AC-A2FK
TCGA-A8-A08T
TCGA-AO-A12H
TCGA-GM-A2DK
TCGA-BH-A1F6
TCGA-A2-A0EY
TCGA-E9-A5UO
TCGA-AN-A0XP
TCGA-A7-A4SF
TCGA-E9-A1N6
TCGA-AO-A0J6
TCGA-A8-A09K
TCGA-B6-A0RO
TCGA-BH-A1ES
TCGA-BH-A1ES
TCGA-E2-A1LB
TCGA-A8-A07R
TCGA-A2-A04Q
TCGA-EW-A6SB
TCGA-EW-A3E8
TCGA-A2-A0EV
TCGA-Z7-A8R5
TCGA-A8-A06X
TCGA-BH-A0BV
TCGA-C8-A12X
TCGA-BH-A0DT
TCGA-E2-A15A
TCGA-A8-A08G
TCGA-AR-A0U4
TCGA-B6-A408
TCGA-E2-A1B0
TCGA-BH-A1EW
TCGA-AC-A6IX
TCGA-AC-A6IX
TCGA-B6-A0RG
TCGA-PL-A8LY
TCGA-BH-A18I
TCGA-BH-A18N
TCGA-E9-A24A
TCGA-AN-A04C
TCGA-A1-A0SO
TCGA-A7-A13G
TCGA-A7-A13G
TCGA-C8-A12K
TCGA-E9-A1N4
TCGA-E9-A1N4
TCGA-BH-A0E6
TCGA-AO-A03L
TCGA-A7-A26J
TCGA-A7-A26J
TCGA-V7-A7HQ
TCGA-GM-A3XL
TCGA-E2-A1IU
TCGA-D8-A1JE
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/tmp/ipykernel_1042853/4149498380.py:48: DeprecationWarning: the `interpolation=` argument to nanquantile was renamed to `method=`, which has additional options.
Users of the modes 'nearest', 'lower', 'higher', or 'midpoint' are encouraged to review the method they used. (Deprecated NumPy 1.22)
  nf  = conorm.tmm_norm_factors(genecount_df)

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/tmp/ipykernel_1042853/4149498380.py:48: DeprecationWarning: the `interpolation=` argument to nanquantile was renamed to `method=`, which has additional options.
Users of the modes 'nearest', 'lower', 'higher', or 'midpoint' are encouraged to review the method they used. (Deprecated NumPy 1.22)
  nf  = conorm.tmm_norm_factors(genecount_df)

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TCGA-D1-A17T
TCGA-EO-A22Y
TCGA-FI-A3PV
TCGA-AP-A0LP
TCGA-AX-A3FX
TCGA-AP-A05D
TCGA-AX-A3FW
TCGA-D1-A17R
TCGA-DI-A1BU
TCGA-EO-A2CH
TCGA-D1-A17N
TCGA-BG-A0MK
TCGA-BG-A0M2
TCGA-D1-A1NW
TCGA-AX-A3FS
TCGA-A5-AB3J
TCGA-AJ-A3OK
TCGA-EY-A1GX
TCGA-AJ-A5DW
TCGA-EY-A2OM
TCGA-SJ-A6ZJ
TCGA-AX-A2H4
TCGA-D1-A163
TCGA-EO-A1Y5
TCGA-BK-A26L
TCGA-FL-A1YF
TCGA-B5-A0K8
TCGA-A5-A2K4
TCGA-BG-A18C
TCGA-EY-A210
TCGA-PG-A914
TCGA-D1-A101
TCGA-B5-A1MX
TCGA-AJ-A2QO
TCGA-BS-A0TJ
TCGA-AX-A3G1
TCGA-AX-A2H5
TCGA-FI-A2F9
TCGA-AX-A06J
TCGA-AP-A1DQ
TCGA-AJ-A23N
TCGA-DF-A2L0
TCGA-D1-A167
TCGA-AX-A1CC
TCGA-BG-A0VW
TCGA-AX-A0IZ
TCGA-D1-A2G7
TCGA-A5-A0GD
TCGA-D1-A1NZ
TCGA-K6-A3WQ
TCGA-EO-A1Y8
TCGA-AJ-A3OJ
TCGA-AX-A3G3
TCGA-AX-A3G6
TCGA-D1-A17S
TCGA-EC-A1QX
TCGA-AP-A0LF
TCGA-AX-A3FT
TCGA-AP-A05H
TCGA-B5-A5OD
TCGA-D1-A16X
TCGA-EO-A3KU
TCGA-EY-A212
TCGA-AX-A2HA
TCGA-A5-A2K2
TCGA-AX-A2HA
TCGA-B5-A1MR
TCGA-DI-A0WH
TCGA-BG-A0VZ
TCGA-A5-A1OJ
TCGA-EY-A2ON
TCGA-BK-A0CA
TCGA-FI-A2D5
TCGA-BS-A0V4
TCGA-BS-A0V8
TCGA-BS-A0V7
TCGA-D1-A17Q
TCGA-D1-A17F
TCGA-B5-A0JR
TCGA-AX-A1CK
TCGA-A5-A0GU
TCGA-BS-A0TG
TCGA-AX-A062
TCGA-D1-A1O7
TCGA-EY-A548
TCGA-BG-A0W2
TCGA-E6-A8L9
TCGA-B5-A11X
TCGA-AJ-A3NC
TCGA-D1-A16G
TCGA-FI-A2D6
TCGA-5B-A90C
TCGA-AP-A0LG
TCGA-AX-A1CK
TCGA-AP-A1DV
TCGA-FI-A2CY
TCGA-D1-A16Y
TCGA-AJ-A3NC
TCGA-B5-A3FH
TCGA-AJ-A3EL
TCGA-D1-A168
TCGA-E6-A2P8
TCGA-AX-A0IU
TCGA-EY-A1GC
TCGA-D1-A17U
TCGA-AP-A0LM
TCGA-B5-A3S1
TCGA-B5-A1MS
TCGA-D1-A175
TCGA-DI-A1NO
TCGA-BG-A0MI
TCGA-AJ-A2QM
TCGA-D1-A16F
TCGA-A5-A2K5
TCGA-EO-A22X
TCGA-DF-A2KV
TCGA-FI-A2EX
TCGA-D1-A176
TCGA-B5-A0JZ
TCGA-D1-A16V
TCGA-B5-A5OE
TCGA-D1-A16S
TCGA-BK-A139

/tmp/ipykernel_1042853/4149498380.py:48: DeprecationWarning: the `interpolation=` argument to nanquantile was renamed to `method=`, which has additional options.
Users of the modes 'nearest', 'lower', 'higher', or 'midpoint' are encouraged to review the method they used. (Deprecated NumPy 1.22)
  nf  = conorm.tmm_norm_factors(genecount_df)

TCGA-C5-A1BK
TCGA-EA-A5ZF
TCGA-BI-A20A
TCGA-IR-A3LA
TCGA-C5-A2LV
TCGA-VS-A9V2
TCGA-C5-A1M5
TCGA-Q1-A6DT
TCGA-VS-A8EG
TCGA-JW-A5VH
TCGA-HM-A6W2
TCGA-HM-A6W2
TCGA-IR-A3LL
TCGA-C5-A1MQ
TCGA-DG-A2KK
TCGA-C5-A1ML
TCGA-C5-A1BL
TCGA-VS-A8EH
TCGA-EK-A2PK
TCGA-C5-A3HF
TCGA-EK-A2PM
TCGA-C5-A1MP
TCGA-C5-A3HL
TCGA-ZJ-AAXF
TCGA-JW-A5VG
TCGA-VS-A94Y
TCGA-UC-A7PD
TCGA-C5-A1M8
TCGA-C5-A3HD
TCGA-FU-A770
TCGA-LP-A5U3
TCGA-ZJ-AAX4
TCGA-UC-A7PF
TCGA-C5-A0TN
TCGA-EA-A5O9
TCGA-VS-AA62
TCGA-C5-A8XH
TCGA-C5-A2LS
TCGA-VS-A8QA
TCGA-JW-AAVH
TCGA-C5-A7CK
TCGA-VS-A9V3
TCGA-MU-A8JM
TCGA-EX-A8YF
TCGA-EX-A69M
TCGA-LP-A5U2
TCGA-EX-A1H6
TCGA-JX-A3Q0
TCGA-VS-A8QF
TCGA-C5-A1MJ
TCGA-VS-A94X
TCGA-C5-A1BF
TCGA-VS-A8QM
TCGA-C5-A1MN
TCGA-DS-A1OB
TCGA-C5-A1BE
TCGA-VS-A9UQ
TCGA-VS-A8EB
TCGA-EA-A556
TCGA-DG-A2KL
TCGA-VS-A8EJ
TCGA-MA-AA3Y
TCGA-VS-A94W
TCGA-C5-A1MH
TCGA-LP-A4AX
TCGA-EA-A43B
TCGA-C5-A8ZZ
TCGA-JX-A3Q8
TCGA-EA-A5ZD
TCGA-DR-A0ZL
TCGA-BI-A0VS
TCGA-BI-A0VR
TCGA-Q1-A73P
TCGA-EA-A3Y4
TCGA-EA-A3HT
TCGA-VS-A950
TCGA-JX-A3PZ
TCGA-EA-A439
TCGA-ZJ-AAXI
TCGA-EK-A2PL
TCGA-DS-A1OA
TCGA-ZX-AA5X
TCGA-VS-A9UU
TCGA-WL-A834
TCGA-VS-A9UO
TCGA-C5-A7X3
TCGA-MY-A5BF
TCGA-MY-A5BF
TCGA-C5-A1MF
TCGA-EK-A2PG
TCGA-UC-A7PG
TCGA-UC-A7PG
TCGA-4J-AA1J
TCGA-JW-A5VI
TCGA-PN-A8MA
TCGA-C5-A7X8
TCGA-VS-A8Q9
TCGA-IR-A3LC
TCGA-ZJ-AAXT
TCGA-2W-A8YY
TCGA-C5-A7UC
TCGA-Q1-A73Q
TCGA-Q1-A6DW
TCGA-EA-A5ZE
TCGA-MU-A5YI
TCGA-DS-A0VK
TCGA-ZJ-AAXN
TCGA-VS-A9UD
TCGA-EK-A2RL
TCGA-DG-A2KH
TCGA-EA-A3HR
TCGA-DS-A3LQ
TCGA-FU-A40J
TCGA-IR-A3L7
TCGA-GH-A9DA
TCGA-FU-A3HY
TCGA-VS-A9UZ
TCGA-FU-A3WB
TCGA-EK-A2H1
TCGA-VS-A9UH
TCGA-VS-A94Z
TCGA-EA-A50E
TCGA-ZJ-AAXJ
TCGA-EX-A1H5
TCGA-HM-A4S6
TCGA-EA-A3HQ
TCGA-VS-A8Q8
TCGA-VS-A9U7
TCGA-EX-A69L
TCGA-FU-A3YQ
TCGA-EA-A1QS
TCGA-FU-A3HZ
TCGA-LP-A7HU
TCGA-FU-A3TQ
TCGA-Q1-A5R3
TCGA-DR-A0ZM
TCGA-LP-A4AW
TCGA-EA-A3HS
TCGA-IR-A3LI
TCGA-EK-A2IP
TCGA-VS-A9V4
TCGA-C5-A2M2
TCGA-MU-A51Y
TCGA-IR-A3LF
TCGA-VS-A9UJ
TCGA-C5-A1BJ
TCGA-EA-A78R
TCGA-VS-A9UL
TCGA-VS-A8EI
TCGA-EA-A410
TCGA-HM-A3JK
TCGA-DG-A2KJ
TCGA-JW-A852
TCGA-C5-A1BM
TCGA-MA-AA41
TCGA-C5-A1M6
TCGA-C5-A7UH
TCGA-EA-A411
TCGA-FU-A23K
TCGA-FU-A23L
TCGA-EA-A97N
TCGA-ZJ-AAXB
TCGA-C5-A2M1
TCGA-C5-A1BN
TCGA-C5-A8XK
TCGA-C5-A1BI
TCGA-VS-A9U5
TCGA-C5-A1MI
TCGA-C5-A8XI
TCGA-FU-A57G
TCGA-EA-A1QT
TCGA-VS-A958
TCGA-VS-A9U6
TCGA-EA-A3HU
TCGA-EK-A2RE
TCGA-VS-A954
TCGA-VS-A9UI
TCGA-FU-A5XV
TCGA-EA-A3QD
TCGA-EA-A4BA
TCGA-VS-A952
TCGA-EK-A2IR
TCGA-C5-A1ME
TCGA-VS-A9UY
TCGA-EA-A6QX
TCGA-C5-A1M9
TCGA-EA-A3QE
TCGA-ZJ-AB0I
TCGA-VS-A9V0
TCGA-VS-A9V1
TCGA-VS-A9UT
TCGA-EK-A2RC
TCGA-EK-A2RK
TCGA-JX-A5QV
TCGA-EK-A2RA
TCGA-MA-AA3X
TCGA-VS-A9UR
TCGA-C5-A7X5
TCGA-ZJ-AAXD
TCGA-VS-A8EC
TCGA-Q1-A73S
TCGA-ZJ-A8QQ
TCGA-DS-A5RQ
TCGA-ZJ-AAX8
TCGA-C5-A1MK
TCGA-FU-A3TX
TCGA-IR-A3LK
TCGA-FU-A3EO
TCGA-FU-A3EO
TCGA-C5-A2LX
TCGA-DS-A1O9
TCGA-C5-A7CL
TCGA-JW-A5VK
TCGA-MA-AA3Z
TCGA-C5-A3HE
TCGA-RA-A741
TCGA-C5-A8YR
TCGA-C5-A2LZ
TCGA-EK-A2PI
TCGA-EK-A2RM
TCGA-DS-A7WI
TCGA-VS-A8QH
TCGA-UC-A7PI
TCGA-Q1-A6DV
TCGA-EK-A2H0
TCGA-C5-A907
TCGA-EK-A2RO
TCGA-C5-A905
TCGA-VS-A9UV
TCGA-MY-A913
TCGA-VS-A9UB
TCGA-VS-A957
TCGA-VS-A8QC
TCGA-EK-A3GM
TCGA-VS-A9UC
TCGA-EK-A2RJ
TCGA-C5-A7UI
TCGA-C5-A8YT
TCGA-MY-A5BD
TCGA-EA-A44S
TCGA-DS-A7WF
TCGA-EK-A3GK
TCGA-Q1-A73O
TCGA-JW-A5VL
TCGA-C5-A7CM
TCGA-VS-A9V5
TCGA-EK-A2R8
TCGA-FU-A2QG
TCGA-C5-A7CH
TCGA-EK-A2R7
TCGA-EK-A2GZ
TCGA-Q1-A73R
TCGA-VS-A8EK
TCGA-C5-A7UE
TCGA-MA-AA42
TCGA-VS-A953
TCGA-FU-A3NI
TCGA-DS-A7WH
TCGA-ZJ-A8QR
TCGA-ZJ-AAXU
TCGA-C5-A8YQ
TCGA-EK-A3GJ
TCGA-C5-A7XC
TCGA-DS-A0VL
TCGA-VS-A8EL
TCGA-LP-A4AU
TCGA-HG-A2PA
TCGA-DS-A1OC
TCGA-C5-A2LY
TCGA-C5-A1BQ
TCGA-IR-A3LB
TCGA-C5-A8XJ
TCGA-ZJ-AAXA
TCGA-C5-A1M7
TCGA-C5-A901
TCGA-EX-A449
TCGA-ZJ-A8QO
TCGA-DS-A1OD
TCGA-EA-A5FO
TCGA-DS-A0VM
TCGA-MA-AA43
TCGA-VS-A9UM
TCGA-JW-A5VJ
TCGA-VS-A9UP
TCGA-ZJ-AB0H
TCGA-HM-A3JJ
TCGA-HM-A3JJ
TCGA-LP-A4AV
TCGA-IR-A3LH
TCGA-VS-A959
TCGA-C5-A7CG
TCGA-Q1-A5R2
TCGA-EX-A3L1
TCGA-EK-A3GN
TCGA-MY-A5BE
TCGA-MA-AA3W
TCGA-C5-A7CJ
TCGA-EK-A2RB
TCGA-R2-A69V
TCGA-DG-A2KM
TCGA-EK-A2RN
TCGA-EK-A2R9
TCGA-C5-A902
TCGA-Q1-A5R1
TCGA-JW-A69B
TCGA-C5-A2LT
TCGA-DS-A0VN
TCGA-C5-A7CO
TCGA-XS-A8TJ

/tmp/ipykernel_1042853/4149498380.py:48: DeprecationWarning: the `interpolation=` argument to nanquantile was renamed to `method=`, which has additional options.
Users of the modes 'nearest', 'lower', 'higher', or 'midpoint' are encouraged to review the method they used. (Deprecated NumPy 1.22)
  nf  = conorm.tmm_norm_factors(genecount_df)
In [ ]:
# ov_gdc_tmm = genecount_to_tmm_for_gsea(ov_gdc_genecount)
# ov_gdc_tmm.iloc[:,1:].to_csv("ov_gdc_tmm.csv")
# ov_gdc_tpm.to_csv("ov_gdc_tpm.csv")
In [5]:
f = open('h.all.v2023.1.Hs.json', 'r')
h_all_json = json.loads(f.read())
h_all_json
hallmark_gs = {key: h_all_json[key]["geneSymbols"]for key in h_all_json.keys()}
f = open('c6.all.v2023.1.Hs.json', 'r')
c6_all_json = json.loads(f.read())
c6_all_json
c6_gs = {key: c6_all_json[key]["geneSymbols"]for key in c6_all_json.keys()}
f = open('c3.tft.gtrd.v2023.1.Hs.json', 'r')
c3_tft_json = json.loads(f.read())
c3_tft_json
c3_gs = {key: c3_tft_json[key]["geneSymbols"]for key in c3_tft_json.keys()}
In [6]:
len(set(hallmark_gs["HALLMARK_ESTROGEN_RESPONSE_EARLY"]).intersection(c6_gs["MEK_UP.V1_DN"]))/len(hallmark_gs["HALLMARK_ESTROGEN_RESPONSE_EARLY"])
Out[6]:
0.15
In [7]:
brca_gdc_tpm
Out[7]:
TCGA-E2-A1L7 TCGA-AR-A0U0 TCGA-BH-A28O TCGA-A2-A0D4 TCGA-E9-A1R4 TCGA-AO-A1KQ TCGA-AC-A62V TCGA-D8-A143 TCGA-A2-A0SV TCGA-AN-A0XW ... TCGA-AC-A5EH TCGA-A1-A0SD TCGA-A2-A0SU TCGA-E9-A1NI TCGA-B6-A0RQ TCGA-A2-A0CX TCGA-A2-A25F TCGA-AC-A23G TCGA-E2-A15D TCGA-OL-A5D7
gene_name
5S_rRNA 0.231289 0.204489 0.193633 0.256344 0.076333 0.134200 0.322033 0.059178 0.092778 0.140722 ... 0.0000 0.066067 0.245711 0.086689 0.0000 0.000000 0.000000 0.133544 0.0000 0.0000
5_8S_rRNA 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.0000 0.069117 0.000000 0.000000 0.0000 0.000000 0.000000 0.000000 0.0000 0.0000
7SK 0.036586 0.000000 0.037600 0.000000 0.000000 0.041571 0.000000 0.000000 0.044886 0.128714 ... 0.0000 0.000000 0.000000 0.000000 0.0000 0.065329 0.024243 0.000000 0.0000 0.0000
A1BG 0.095400 0.048800 0.357700 0.384100 0.364200 0.233600 0.107600 0.067100 0.117100 0.369300 ... 0.1941 0.346800 0.097700 0.020700 0.2668 0.113000 0.180400 0.446000 0.3221 0.0560
A1BG-AS1 2.036100 0.693800 1.926900 2.128600 2.546700 1.959600 0.546300 0.377900 1.096000 1.870200 ... 1.7947 1.849500 0.949500 1.127500 2.3798 0.803400 0.741900 3.209600 2.1898 1.0393
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
ZZEF1 22.224500 15.184600 22.424000 6.723100 21.986200 5.789800 6.021400 11.884700 7.580600 8.623300 ... 13.4076 16.404400 7.027800 11.893900 18.7700 7.368500 23.281400 16.446400 21.7841 11.1431
ZZZ3 30.547300 25.071200 27.180200 15.913500 15.230100 10.108100 6.902900 25.557900 11.546500 19.725300 ... 16.8483 29.787200 28.208200 14.266900 28.4495 11.836500 26.301300 17.070800 26.1865 7.2553
hsa-mir-1253 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.0000 0.000000 0.000000 0.000000 0.0000 0.000000 0.000000 0.000000 0.0000 0.0000
hsa-mir-423 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.0000 0.000000 0.000000 0.000000 0.0000 0.000000 0.000000 0.000000 0.0000 0.0000
snoZ196 0.000000 0.000000 1.461000 0.000000 0.000000 0.807500 0.000000 0.000000 1.577800 0.000000 ... 1.7445 0.000000 0.000000 0.000000 0.0000 0.000000 0.953500 0.000000 0.7616 0.0000

59427 rows × 1095 columns

In [8]:
brca_hallmark_es = gsva_py2r(np.log2(brca_gdc_tpm+1),hallmark_gs)
ov_hallmark_es = gsva_py2r(np.log2(ov_gdc_tpm+1),hallmark_gs)
ucec_hallmark_es = gsva_py2r(np.log2(ucec_gdc_tpm+1),hallmark_gs)
cesc_hallmark_es = gsva_py2r(np.log2(cesc_gdc_tpm+1),hallmark_gs)

Converting df
Estimating GSVA scores for 50 gene sets.
Estimating ECDFs with Gaussian kernels
  |======================================================================| 100%

Converting df
Estimating GSVA scores for 50 gene sets.
Estimating ECDFs with Gaussian kernels
  |======================================================================| 100%

Converting df
Estimating GSVA scores for 50 gene sets.
Estimating ECDFs with Gaussian kernels
  |======================================================================| 100%

Converting df
Estimating GSVA scores for 50 gene sets.
Estimating ECDFs with Gaussian kernels
  |======================================================================| 100%
In [10]:
brca_es = pd.DataFrame(brca_hallmark_es["HALLMARK_ESTROGEN_RESPONSE_EARLY"].values, index=brca_hallmark_es.index, columns=['EARLY'])
ov_es = pd.DataFrame(ov_hallmark_es["HALLMARK_ESTROGEN_RESPONSE_EARLY"].values, index=ov_hallmark_es.index, columns=['EARLY'])
ucec_es = pd.DataFrame(ucec_hallmark_es["HALLMARK_ESTROGEN_RESPONSE_EARLY"].values, index=ucec_hallmark_es.index, columns=['EARLY'])
cesc_es = pd.DataFrame(cesc_hallmark_es["HALLMARK_ESTROGEN_RESPONSE_EARLY"].values, index=cesc_hallmark_es.index, columns=['EARLY'])
In [9]:
with pd.ExcelWriter('Table S1.xlsx') as writer:
    brca_es.rename(columns={"EARLY":"EERES"}).to_excel(writer, "BRCA")
    ov_es.rename(columns={"EARLY":"EERES"}).to_excel(writer, "OV")
    ucec_es.rename(columns={"EARLY":"EERES"}).to_excel(writer, "UCEC")
    cesc_es.rename(columns={"EARLY":"EERES"}).to_excel(writer, "CESC")

Figure 1¶

In [11]:
def finding_best_es_for_survival(es_dfs_df, es_dss_df, es_df):
    bestes=None
    bestp=1
    sp = []

    for i in np.arange(es_df["EARLY"].min(), es_df["EARLY"].max(), 0.01):
        results_dfs = km.fit(es_dfs_df[f"DFS_MONTHS"], es_dfs_df[f"DFS_STATUS"], (es_dfs_df["EARLY"]>i).apply(lambda x: "High EERES" if x else "Low EERES"))
        results_dss = km.fit(es_dss_df[f"DSS_MONTHS"], es_dss_df[f"DSS_STATUS"], (es_dss_df["EARLY"]>i).apply(lambda x: "High EERES" if x else "Low EERES"))
        p = (results_dfs['logrank_P']+results_dss['logrank_P'])/2
        if p<bestp:
            bestes = i
            bestp = (results_dfs['logrank_P']+results_dss['logrank_P'])/2
    print("EERES Threshold", bestes)
    print("n", len(es_df))
    print("n >threshold", (es_df["EARLY"]>bestes).sum())
    print("n <=threshold", (es_df["EARLY"]<=bestes).sum())
    results = km.fit(es_dfs_df[f"DFS_MONTHS"], es_dfs_df[f"DFS_STATUS"], (es_dfs_df["EARLY"]>bestes).apply(lambda x: "High EERES" if x else "Low EESRS"))
    km.plot(results, full_ylim=True, y_percentage=True, fontsize=15)
    plt.show()
    results = km.fit(es_dss_df[f"DSS_MONTHS"], es_dss_df[f"DSS_STATUS"], (es_dss_df["EARLY"]>bestes).apply(lambda x: "High EERES" if x else "Low EESRS"))
    km.plot(results, full_ylim=True, y_percentage=True, fontsize=15)
    plt.show()
    return bestes
    
print("BRCA")    
brca_dfs_es = brca_dfs.join(brca_es, how='inner').dropna()
brca_dss_es = brca_dss.join(brca_es, how='inner').dropna()
brca_bestes = finding_best_es_for_survival(brca_dfs_es, brca_dss_es, brca_es)
print("OV") 
ov_dfs_es = ov_dfs.join(ov_es, how='inner').dropna()
ov_dss_es = ov_dss.join(ov_es, how='inner').dropna()
ov_bestes = finding_best_es_for_survival(ov_dfs_es, ov_dss_es, ov_es)
print("UCEC") 
ucec_dfs_es = ucec_dfs.join(ucec_es, how='inner').dropna()
ucec_dss_es = ucec_dss.join(ucec_es, how='inner').dropna()
ucec_bestes = finding_best_es_for_survival(ucec_dfs_es, ucec_dss_es, ucec_es)
print("CESC") 
cesc_dfs_es = cesc_dfs.join(cesc_es, how='inner').dropna()
cesc_dss_es = cesc_dss.join(cesc_es, how='inner').dropna()
cesc_bestes = finding_best_es_for_survival(cesc_dfs_es, cesc_dss_es, cesc_es)

BRCA
EERES Threshold -0.051194787594808
n 1095
n >threshold 610
n <=threshold 485
No description has been provided for this image
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OV
EERES Threshold -0.18398784249546785
n 378
n >threshold 263
n <=threshold 115
No description has been provided for this image
No description has been provided for this image 
UCEC
EERES Threshold 0.15745491375942877
n 557
n >threshold 147
n <=threshold 410
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No description has been provided for this image 
CESC
EERES Threshold -0.2282193398849608
n 304
n >threshold 248
n <=threshold 56
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In [11]:
tmp = brca_dfs_es[["DFS_STATUS", "DFS_MONTHS", "EARLY"]]
tmp["EARLY"] = tmp["EARLY"].apply(lambda x: 1 if x>-0.051194787594808 else 0)
tmp.to_csv("brca_dfs.csv")
tmp = brca_dss_es[["DSS_STATUS", "DSS_MONTHS", "EARLY"]]
tmp["EARLY"] = tmp["EARLY"].apply(lambda x: 1 if x>-0.051194787594808 else 0)
tmp.to_csv("brca_dss.csv")

tmp = ov_dfs_es[["DFS_STATUS", "DFS_MONTHS", "EARLY"]]
tmp["EARLY"] = tmp["EARLY"].apply(lambda x: 1 if x>-0.18398784249546785 else 0)
tmp.to_csv("ov_dfs.csv")
tmp = ov_dss_es[["DSS_STATUS", "DSS_MONTHS", "EARLY"]]
tmp["EARLY"] = tmp["EARLY"].apply(lambda x: 1 if x>-0.18398784249546785 else 0)
tmp.to_csv("ov_dss.csv")

tmp = ucec_dfs_es[["DFS_STATUS", "DFS_MONTHS", "EARLY"]]
tmp["EARLY"] = tmp["EARLY"].apply(lambda x: 1 if x>0.15745491375942877 else 0)
tmp.to_csv("ucec_dfs.csv")
tmp = ucec_dss_es[["DSS_STATUS", "DSS_MONTHS", "EARLY"]]
tmp["EARLY"] = tmp["EARLY"].apply(lambda x: 1 if x>0.15745491375942877 else 0)
tmp.to_csv("ucec_dss.csv")

tmp = cesc_dfs_es[["DFS_STATUS", "DFS_MONTHS", "EARLY"]]
tmp["EARLY"] = tmp["EARLY"].apply(lambda x: 1 if x>-0.2282193398849608 else 0)
tmp.to_csv("cesc_dfs.csv")
tmp = cesc_dss_es[["DSS_STATUS", "DSS_MONTHS", "EARLY"]]
tmp["EARLY"] = tmp["EARLY"].apply(lambda x: 1 if x>-0.2282193398849608 else 0)
tmp.to_csv("cesc_dss.csv")

/tmp/ipykernel_30800/682250153.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  tmp["EARLY"] = tmp["EARLY"].apply(lambda x: 1 if x>-0.051194787594808 else 0)
/tmp/ipykernel_30800/682250153.py:5: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  tmp["EARLY"] = tmp["EARLY"].apply(lambda x: 1 if x>-0.051194787594808 else 0)
/tmp/ipykernel_30800/682250153.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  tmp["EARLY"] = tmp["EARLY"].apply(lambda x: 1 if x>-0.18398784249546785 else 0)
/tmp/ipykernel_30800/682250153.py:12: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  tmp["EARLY"] = tmp["EARLY"].apply(lambda x: 1 if x>-0.18398784249546785 else 0)
/tmp/ipykernel_30800/682250153.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  tmp["EARLY"] = tmp["EARLY"].apply(lambda x: 1 if x>0.15745491375942877 else 0)
/tmp/ipykernel_30800/682250153.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  tmp["EARLY"] = tmp["EARLY"].apply(lambda x: 1 if x>0.15745491375942877 else 0)
/tmp/ipykernel_30800/682250153.py:23: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  tmp["EARLY"] = tmp["EARLY"].apply(lambda x: 1 if x>-0.2282193398849608 else 0)
/tmp/ipykernel_30800/682250153.py:26: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  tmp["EARLY"] = tmp["EARLY"].apply(lambda x: 1 if x>-0.2282193398849608 else 0)
In [13]:
def plotting_esr1_eeres_scatter_spearman(esr1_es_df):    
    plt.scatter(np.log2(esr1_es_df["ESR1"] + 1), esr1_es_df["EARLY"])
    plt.xlabel("log2[ESR1+1]", weight="bold", fontsize=12, labelpad=0)
    plt.ylabel("EERES", weight="bold", fontsize=12, labelpad=-5)
    sr, sp = stats.spearmanr(np.log2(esr1_es_df["ESR1"]), esr1_es_df["EARLY"])
    plt.title(f"Spearman R={sr:.3f}, p={sp:.3e}", weight="bold")
    plt.show()
    
brca_es = pd.DataFrame(brca_hallmark_es["HALLMARK_ESTROGEN_RESPONSE_EARLY"].values, index=brca_hallmark_es.index, columns=["EARLY"])
brca_esr1_es_df = brca_gdc_tpm.transpose()[["ESR1"]].join(brca_es, how='inner')
plotting_esr1_eeres_scatter_spearman(brca_esr1_es_df)

ov_es = pd.DataFrame(ov_hallmark_es["HALLMARK_ESTROGEN_RESPONSE_EARLY"].values, index=ov_hallmark_es.index, columns=["EARLY"])
ov_esr1_es_df = ov_gdc_tpm.transpose()[["ESR1"]].join(ov_es, how='inner')
plotting_esr1_eeres_scatter_spearman(ov_esr1_es_df)

ucec_es = pd.DataFrame(ucec_hallmark_es["HALLMARK_ESTROGEN_RESPONSE_EARLY"].values, index=ucec_hallmark_es.index, columns=["EARLY"])
ucec_esr1_es_df = ucec_gdc_tpm.transpose()[["ESR1"]].join(ucec_es, how='inner')
plotting_esr1_eeres_scatter_spearman(ucec_esr1_es_df)

cesc_es = pd.DataFrame(cesc_hallmark_es["HALLMARK_ESTROGEN_RESPONSE_EARLY"].values, index=cesc_hallmark_es.index, columns=["EARLY"])
cesc_esr1_es_df = cesc_gdc_tpm.transpose()[["ESR1"]].join(cesc_es, how='inner')
plotting_esr1_eeres_scatter_spearman(cesc_esr1_es_df)
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Figure 2¶

ESR1 and EERES on Survival of hormonal therapy treated patients¶

In [14]:
q_groups = {}
for q in np.arange(0.1,1,0.05):
    br_clinical_gdc = pd.read_table("Data/gdc/TCGA-BRCA/clinical/nationwidechildrens.org_clinical_patient_brca.txt", skiprows=1, header=0, index_col=1).iloc[1:]
    brca_es = pd.DataFrame(brca_hallmark_es["HALLMARK_ESTROGEN_RESPONSE_EARLY"].values, index=brca_hallmark_es.index, columns=["EARLY"])
    brca_esr1_es_df = brca_gdc_tpm.transpose()[["ESR1"]].join(brca_es, how='inner')
    brca_erpos_her2neg_index = br_clinical_gdc[((br_clinical_gdc["breast_carcinoma_estrogen_receptor_status"]=="Positive"))&(br_clinical_gdc["lab_proc_her2_neu_immunohistochemistry_receptor_status"]=="Negative")].index
    brca_erpos_her2neg_index = [i.replace('.','-') for i in brca_erpos_her2neg_index]
    brca_erpos_her2neg_index_esr1_index = set(brca_erpos_her2neg_index).intersection(brca_esr1_es_df.index)
    print("n:", len(brca_erpos_her2neg_index_esr1_index))
    brca_esr1_es_df = brca_esr1_es_df.loc[list(brca_erpos_her2neg_index_esr1_index)]
    BRCA_ESR1_Threshold = brca_esr1_es_df["ESR1"].quantile(q)
    BRCA_EERES_Threshold = brca_esr1_es_df["EARLY"].quantile(q)
    print(BRCA_ESR1_Threshold)
    print(BRCA_EERES_Threshold)
    
    plotting_scatter_esr1_es(brca_esr1_es_df, BRCA_ESR1_Threshold, BRCA_EERES_Threshold)
    
    brca_groups = grouping4(brca_esr1_es_df, BRCA_ESR1_Threshold, BRCA_EERES_Threshold)

    q_groups[q] = {'BRCA_ESR1_Threshold':BRCA_ESR1_Threshold, 'BRCA_EERES_Threshold':BRCA_EERES_Threshold}

n: 436
29.35475
-0.28884320118370976
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n: 436
43.950675000000004
-0.20438048637962367
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n: 436
58.74400000000001
-0.15194432395471977
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n: 436
68.10275
-0.08065019107565878
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n: 436
77.9179
-0.025146768801859266
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n: 436
89.568425
0.015048116919465372
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n: 436
102.37820000000004
0.05880599028967579
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n: 436
123.099225
0.1061000830013521
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n: 436
140.102
0.14691609120149926
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n: 436
153.9781250000001
0.17969457947342965
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n: 436
173.33830000000023
0.20547056079620443
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n: 436
199.29877500000003
0.21932558344087438
No description has been provided for this image 
n: 436
226.31265000000008
0.24852481578754917
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n: 436
256.38257500000014
0.2821959900735944
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n: 436
296.9775000000001
0.31572829628565613
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n: 436
338.723425
0.35571723332676947
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n: 436
381.8058500000002
0.3883947642399483
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n: 436
493.54282500000215
0.4366569137147916
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In [12]:
brca_endocrine_index = pd.read_table("Data/brca_tcga_pan_can_atlas_2018/brca_tcga_pan_can_atlas_2018_clinical_data_endocrineTreated.tsv", index_col=1).index
In [15]:
for q in q_groups:
    print(q)
    BRCA_ESR1_Threshold = q_groups[q]['BRCA_ESR1_Threshold']
    BRCA_EERES_Threshold = q_groups[q]['BRCA_EERES_Threshold']
    
    print("PFS")
    
    br_pfs_es = brca_pfs.join(brca_esr1_es_df, how='inner')
    br_pfs_es = br_pfs_es[~br_pfs_es.index.duplicated(keep="first")]
    plotting_2groups(br_pfs_es, "PFS", BRCA_ESR1_Threshold, BRCA_EERES_Threshold)
    plotting_4groups(br_pfs_es, "PFS", BRCA_ESR1_Threshold, BRCA_EERES_Threshold)
    
    print("OS")
    
    br_os_es = brca_os.join(brca_esr1_es_df, how='inner')
    br_os_es = br_os_es[~br_os_es.index.duplicated(keep="first")]
    plotting_2groups(br_os_es, "OS", BRCA_ESR1_Threshold, BRCA_EERES_Threshold)
    plotting_4groups(br_os_es, "OS", BRCA_ESR1_Threshold, BRCA_EERES_Threshold)
    
    print("DFS")
    
    br_dfs_es = brca_dfs.join(brca_esr1_es_df, how='inner')
    br_dfs_es = br_dfs_es[~br_dfs_es.index.duplicated(keep="first")]
    plotting_2groups(br_dfs_es, "DFS", BRCA_ESR1_Threshold, BRCA_EERES_Threshold)
    plotting_4groups(br_dfs_es, "DFS", BRCA_ESR1_Threshold, BRCA_EERES_Threshold)
    
    print("DSS")
    
    br_dss_es = brca_dss.join(brca_esr1_es_df, how='inner')
    br_dss_es = br_dss_es[~br_dss_es.index.duplicated(keep="first")]
    plotting_2groups(br_dss_es, "DSS", BRCA_ESR1_Threshold, BRCA_EERES_Threshold)
    plotting_4groups(br_dss_es, "DSS", BRCA_ESR1_Threshold, BRCA_EERES_Threshold)

0.1
PFS
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ESR1_low_EERES_low vs ESR1_low_EERES_high 0.8598786805963252
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.5201102619875551
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.6329711579191577
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.7482941382955608
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.737526064124406
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.8538484019660898
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.859879   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low               0.52011             0.632971  
ESR1_low_EERES_high             0.748294             0.737526  
ESR1_high_EERES_low                  NaN             0.853848  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
OS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.9555688034538364
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.6612251134791733
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.34691325795723904
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.625325358271994
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.6360814577004981
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.9875905429808715
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.955569   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.661225             0.346913  
ESR1_low_EERES_high             0.625325             0.636081  
ESR1_high_EERES_low                  NaN             0.987591  
ESR1_high_EERES_high                 NaN                  NaN
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DFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.7838594446269418
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.20438922788614522
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.9631777045647636
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.18048579714551144
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.9434151447359935
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.18551959439733268
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.783859   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.204389             0.963178  
ESR1_low_EERES_high             0.180486             0.943415  
ESR1_high_EERES_low                  NaN              0.18552  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DSS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 1.0
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.1967056024589432
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.14433386571613221
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.14103164052071204
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.21547136682388363
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.9121914800870119
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN                 1.0   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.196706             0.144334  
ESR1_low_EERES_high             0.141032             0.215471  
ESR1_high_EERES_low                  NaN             0.912191  
ESR1_high_EERES_high                 NaN                  NaN
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0.15000000000000002
PFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.9200218606235004
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.867793323292622
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.873827633162038
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.9436849407075355
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.8050812363068717
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.8329214185252707
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.920022   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.867793             0.873828  
ESR1_low_EERES_high             0.943685             0.805081  
ESR1_high_EERES_low                  NaN             0.832921  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
OS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.7124875507937802
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.8548810264109633
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.21637174668544573
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.7672728860386844
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.36007983911830976
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.6435079866012261
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.712488   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.854881             0.216372  
ESR1_low_EERES_high             0.767273              0.36008  
ESR1_high_EERES_low                  NaN             0.643508  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.8695165528479848
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.47992952660517163
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.7580653318841046
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.3573081766899927
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.7364660272412553
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.6073074165485859
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.869517   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low               0.47993             0.758065  
ESR1_low_EERES_high             0.357308             0.736466  
ESR1_high_EERES_low                  NaN             0.607307  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DSS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.3906743460235679
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.5741490727971621
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.14003613914740343
ESR1_low_EERES_high vs ESR1_high_EERES_low 1.0
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.13131342022078188
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.1990545844748189
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.390674   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.574149             0.140036  
ESR1_low_EERES_high                  1.0             0.131313  
ESR1_high_EERES_low                  NaN             0.199055  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
0.20000000000000004
PFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.6622116249286212
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.5283765688460079
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.9082863220968839
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.5435899146780092
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.4987389752556778
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.4315892982097067
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.662212   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.528377             0.908286  
ESR1_low_EERES_high              0.54359             0.498739  
ESR1_high_EERES_low                  NaN             0.431589  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
OS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.15219900983806545
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.8686215375739792
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.4436500841749935
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.2539255742278348
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.06975060098670358
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.38875089045625855
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.152199   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.868622              0.44365  
ESR1_low_EERES_high             0.253926             0.069751  
ESR1_high_EERES_low                  NaN             0.388751  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.5115840757349344
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.6360337395293292
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.845122748789334
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.8687878461045657
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.3602871089654599
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.2119404851538137
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.511584   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.636034             0.845123  
ESR1_low_EERES_high             0.868788             0.360287  
ESR1_high_EERES_low                  NaN              0.21194  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DSS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.4521656813501922
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.5474808401254836
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.09185060039891405
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.27698616050758074
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.06577751779867913
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.4410291749926585
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.452166   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.547481             0.091851  
ESR1_low_EERES_high             0.276986             0.065778  
ESR1_high_EERES_low                  NaN             0.441029  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
0.25000000000000006
PFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.6041400499518633
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.2530709963729081
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.5388154992686951
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.07473720820823107
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.9638363189382196
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.05357855418752254
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN             0.60414   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.253071             0.538815  
ESR1_low_EERES_high             0.074737             0.963836  
ESR1_high_EERES_low                  NaN             0.053579  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
OS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.8593440941343111
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.4695966951919802
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.29912542754635785
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.44615071586061594
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.43433680564822175
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.8299744001895408
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.859344   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.469597             0.299125  
ESR1_low_EERES_high             0.446151             0.434337  
ESR1_high_EERES_low                  NaN             0.829974  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.9000380010432019
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.22513362854847863
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.3591452367266007
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.10179338844810748
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.5756259566357591
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.004854088871972836
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.900038   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.225134             0.359145  
ESR1_low_EERES_high             0.101793             0.575626  
ESR1_high_EERES_low                  NaN             0.004854  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DSS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.6028765819021226
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.08151187580045727
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.07139574250467706
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.2924638597495853
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.31006434881733286
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.6543242908847227
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.602877   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.081512             0.071396  
ESR1_low_EERES_high             0.292464             0.310064  
ESR1_high_EERES_low                  NaN             0.654324  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
0.30000000000000004
PFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.5058132013021313
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.19549844746287565
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.7199192796832914
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.044598516666416135
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.8380343349471735
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.04392883302561642
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.505813   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.195498             0.719919  
ESR1_low_EERES_high             0.044599             0.838034  
ESR1_high_EERES_low                  NaN             0.043929  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
OS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.9380965829360217
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.8872985288092695
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.26030395757036157
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.9852233370671866
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.5688175897052216
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.5418884834069067
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.938097   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.887299             0.260304  
ESR1_low_EERES_high             0.985223             0.568818  
ESR1_high_EERES_low                  NaN             0.541888  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.41232725147562477
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.26721420831984144
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.41539298110155
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.042862607793559104
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.7340103569255207
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.024698975981733953
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.412327   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.267214             0.415393  
ESR1_low_EERES_high             0.042863              0.73401  
ESR1_high_EERES_low                  NaN             0.024699  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DSS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.28021312518838576
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.05668713718928911
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.05470216674870091
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.563194608222823
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.627749952586823
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.6755485120255311
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.280213   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.056687             0.054702  
ESR1_low_EERES_high             0.563195              0.62775  
ESR1_high_EERES_low                  NaN             0.675549  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
0.3500000000000001
PFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.4798360422639487
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.11370601547195149
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.6574717665362904
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.03773374576566702
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.8209793487394946
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.02496886626110304
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.479836   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.113706             0.657472  
ESR1_low_EERES_high             0.037734             0.820979  
ESR1_high_EERES_low                  NaN             0.024969  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
OS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.8171097507821032
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.4038273599144595
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.2636054567905919
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.3930261472869109
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.6468840357266319
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.8021157055063309
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN             0.81711   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.403827             0.263605  
ESR1_low_EERES_high             0.393026             0.646884  
ESR1_high_EERES_low                  NaN             0.802116  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.2773766277419356
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.139812347350742
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.3582962416541551
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.021142154737416503
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.6250399176419952
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.008748059383925337
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.277377   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.139812             0.358296  
ESR1_low_EERES_high             0.021142              0.62504  
ESR1_high_EERES_low                  NaN             0.008748  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DSS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.10663165998822288
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.010672575317710353
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.039141447538481644
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.333738636398704
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.9937658841319399
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.3762049645769673
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.106632   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.010673             0.039141  
ESR1_low_EERES_high             0.333739             0.993766  
ESR1_high_EERES_low                  NaN             0.376205  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
0.40000000000000013
PFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.9873769158980736
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.03319634964525207
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.9035441783339548
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.06041617680404591
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.7474555738875708
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.015240539757677763
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.987377   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.033196             0.903544  
ESR1_low_EERES_high             0.060416             0.747456  
ESR1_high_EERES_low                  NaN             0.015241  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
OS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.9099969188108601
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.15294386624066017
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.09255246245786314
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.165289150136204
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.21937328231803468
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.7514538813901733
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.909997   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.152944             0.092552  
ESR1_low_EERES_high             0.165289             0.219373  
ESR1_high_EERES_low                  NaN             0.751454  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.8241361876221089
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.06616032967377358
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.48136870727622494
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.06184207333993803
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.6956698098670374
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.0037427082266420475
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.824136   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low               0.06616             0.481369  
ESR1_low_EERES_high             0.061842              0.69567  
ESR1_high_EERES_low                  NaN             0.003743  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DSS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.12365433396065431
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.0032554693974250534
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.013931631344921752
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.16332985504177772
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.5320083839142234
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.5095807807399126
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.123654   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.003255             0.013932  
ESR1_low_EERES_high              0.16333             0.532008  
ESR1_high_EERES_low                  NaN             0.509581  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
0.45000000000000007
PFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.982427382552995
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.08041735054888723
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.6314572009729043
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.1429808692907533
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.4370418803994094
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.03693243268051407
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.982427   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.080417             0.631457  
ESR1_low_EERES_high             0.142981             0.437042  
ESR1_high_EERES_low                  NaN             0.036932  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
OS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.7252573668206016
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.25029395616333006
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.24509500502611406
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.21564283368854534
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.3042654447199684
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.6562459972469991
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.725257   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.250294             0.245095  
ESR1_low_EERES_high             0.215643             0.304265  
ESR1_high_EERES_low                  NaN             0.656246  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.7359247658028732
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.1909820075089022
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.16961770051186148
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.15204909586444373
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.3504359089844693
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.007947491281592795
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.735925   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.190982             0.169618  
ESR1_low_EERES_high             0.152049             0.350436  
ESR1_high_EERES_low                  NaN             0.007947  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DSS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.4875711681913767
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.09186688445153474
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.07060409169879438
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.32276949957104184
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.5328935947512027
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.8439934074982907
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.487571   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.091867             0.070604  
ESR1_low_EERES_high             0.322769             0.532894  
ESR1_high_EERES_low                  NaN             0.843993  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
0.5000000000000001
PFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.45839751253583605
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.07411214962475982
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.3769704516985296
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.28265464628928877
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.075463204142372
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.021088955803457276
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.458398   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.074112              0.37697  
ESR1_low_EERES_high             0.282655             0.075463  
ESR1_high_EERES_low                  NaN             0.021089  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
OS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.9997852566302716
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.10789518563010361
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.3419563718060396
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.10099202822608018
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.5365758040099686
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.3277452671141016
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.999785   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.107895             0.341956  
ESR1_low_EERES_high             0.100992             0.536576  
ESR1_high_EERES_low                  NaN             0.327745  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.7001545046038424
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.2830286719697306
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.08114127326813025
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.4461225701313132
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.04539577130181184
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.012183988636368303
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.700155   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.283029             0.081141  
ESR1_low_EERES_high             0.446123             0.045396  
ESR1_high_EERES_low                  NaN             0.012184  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DSS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.0860082565346043
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.016525849681837193
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.18246972445820414
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.35030658649065693
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.4992959569561384
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.299840571766389
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.086008   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.016526              0.18247  
ESR1_low_EERES_high             0.350307             0.499296  
ESR1_high_EERES_low                  NaN             0.299841  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
0.5500000000000002
PFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.7623079871639259
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.17961486648753947
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.09648824996481367
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.327478355152512
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.06645029844699525
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.0074432420661959915
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.762308   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.179615             0.096488  
ESR1_low_EERES_high             0.327478              0.06645  
ESR1_high_EERES_low                  NaN             0.007443  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
OS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.41749331647720145
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.004678567907211327
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.8182014017155887
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.0773601511861495
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.7330358437291215
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.027006980818053923
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.417493   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.004679             0.818201  
ESR1_low_EERES_high              0.07736             0.733036  
ESR1_high_EERES_low                  NaN             0.027007  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.5325781490043979
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.7816417522941512
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.08118422997510498
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.332373800806317
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.216776957541912
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.06629657055857258
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.532578   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.781642             0.081184  
ESR1_low_EERES_high             0.332374             0.216777  
ESR1_high_EERES_low                  NaN             0.066297  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DSS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.009328380815078537
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.0020579715864256455
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.652255085238989
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.6254816559143137
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.12233392646195887
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.03592742946626071
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.009328   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.002058             0.652255  
ESR1_low_EERES_high             0.625482             0.122334  
ESR1_high_EERES_low                  NaN             0.035927  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
0.6000000000000002
PFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.6959349378096316
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.09057494926841844
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.03493102340453924
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.28761874215146355
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.01324351804114535
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.0012720396101921284
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.695935   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.090575             0.034931  
ESR1_low_EERES_high             0.287619             0.013244  
ESR1_high_EERES_low                  NaN             0.001272  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
OS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.24466975894055207
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.0053376595793309285
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.7733551847895382
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.17294460978943757
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.2839526376566176
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.013476510585546482
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN             0.24467   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.005338             0.773355  
ESR1_low_EERES_high             0.172945             0.283953  
ESR1_high_EERES_low                  NaN             0.013477  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.8839831745605427
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.42715336510697444
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.02324005266469036
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.33407798319418397
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.024540917833679836
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.006855157567469663
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.883983   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.427153              0.02324  
ESR1_low_EERES_high             0.334078             0.024541  
ESR1_high_EERES_low                  NaN             0.006855  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DSS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.013792847978511769
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.004801316368027246
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.7934501656042929
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.7029476150899023
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.06009259772091245
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.02168994650010052
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.013793   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.004801              0.79345  
ESR1_low_EERES_high             0.702948             0.060093  
ESR1_high_EERES_low                  NaN              0.02169  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
0.6500000000000001
PFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.5368876878465606
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.057146857297087114
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.0315722762261594
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.23158262422018566
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.006002694865759897
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.0010180778656796406
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.536888   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.057147             0.031572  
ESR1_low_EERES_high             0.231583             0.006003  
ESR1_high_EERES_low                  NaN             0.001018  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
OS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.2010628123066087
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.015692692519150134
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.7097412919206219
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.28172096354689413
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.26900789034517814
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.05672685138527434
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.201063   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.015693             0.709741  
ESR1_low_EERES_high             0.281721             0.269008  
ESR1_high_EERES_low                  NaN             0.056727  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.9807412957743201
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.2956203112513096
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.05389266596807388
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.2952124313167216
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.04010865202237087
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.011689564885294188
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.980741   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low               0.29562             0.053893  
ESR1_low_EERES_high             0.295212             0.040109  
ESR1_high_EERES_low                  NaN              0.01169  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DSS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.02009822953406045
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.010451626179313573
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.28143026870034427
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.6570884614872321
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.024712990043976382
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.019547263881759785
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.020098   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.010452              0.28143  
ESR1_low_EERES_high             0.657088             0.024713  
ESR1_high_EERES_low                  NaN             0.019547  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
0.7000000000000002
PFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.8206095737512977
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.27028718352601794
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.05933685185992164
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.2853714451597228
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.022887114195411208
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.012195557349301998
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN             0.82061   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.270287             0.059337  
ESR1_low_EERES_high             0.285371             0.022887  
ESR1_high_EERES_low                  NaN             0.012196  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
OS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.784656423950342
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.09098154206462047
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.4462480354597165
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.22224223243066665
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.3602731732558776
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.16506710117708986
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.784656   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.090982             0.446248  
ESR1_low_EERES_high             0.222242             0.360273  
ESR1_high_EERES_low                  NaN             0.165067  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.6109911461829366
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.8483443890001743
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.07522696921037074
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.5571704577315346
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.1164362863976808
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.056490151824570616
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.610991   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.848344             0.075227  
ESR1_low_EERES_high              0.55717             0.116436  
ESR1_high_EERES_low                  NaN              0.05649  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DSS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.13057845648158886
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.22079567199324665
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.27414226609845604
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.8854423680963981
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.04691004523208199
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.13057892246564853
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.130578   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.220796             0.274142  
ESR1_low_EERES_high             0.885442              0.04691  
ESR1_high_EERES_low                  NaN             0.130579  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
0.7500000000000002
PFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.6672591904151143
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.39354255348368594
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.1025233225555485
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.181541901888896
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.09828210228873673
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.04395971391328831
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.667259   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.393543             0.102523  
ESR1_low_EERES_high             0.181542             0.098282  
ESR1_high_EERES_low                  NaN              0.04396  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
OS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.31898091112087645
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.13079069572894378
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.10607532962813669
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.024923148237325887
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.18349227114101313
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.03624526973838203
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.318981   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.130791             0.106075  
ESR1_low_EERES_high             0.024923             0.183492  
ESR1_high_EERES_low                  NaN             0.036245  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.40046118771396466
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.9022696275831421
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.14855442161136204
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.576062582762795
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.23945506571028233
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.16791619933495094
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.400461   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low               0.90227             0.148554  
ESR1_low_EERES_high             0.576063             0.239455  
ESR1_high_EERES_low                  NaN             0.167916  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DSS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.7676973776686257
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.6525881119047849
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.2873091612136399
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.6895233288312068
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.18451778024473142
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.23209292479855362
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.767697   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.652588             0.287309  
ESR1_low_EERES_high             0.689523             0.184518  
ESR1_high_EERES_low                  NaN             0.232093  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
0.8000000000000002
PFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.8187971900684419
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.8199606200817952
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.12941587324193007
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.44456380783828064
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.114305437635495
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.09767001442122476
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.818797   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.819961             0.129416  
ESR1_low_EERES_high             0.444564             0.114305  
ESR1_high_EERES_low                  NaN              0.09767  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
OS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.42177466137839437
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.13657526015914012
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.13548597416435734
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.03912416313617889
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.18302732675681152
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.040512113875311534
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.421775   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.136575             0.135486  
ESR1_low_EERES_high             0.039124             0.183027  
ESR1_high_EERES_low                  NaN             0.040512  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.5020004837486047
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.2996334313582004
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.17502287486628693
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.7544470094259244
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.2647392078480632
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.39802471950693796
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN               0.502   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.299633             0.175023  
ESR1_low_EERES_high             0.754447             0.264739  
ESR1_high_EERES_low                  NaN             0.398025  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DSS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.5882976889485216
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.46888334939557363
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.3266830382118065
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.6648905724759219
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.18613733602385588
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.22525290636064965
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.588298   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.468883             0.326683  
ESR1_low_EERES_high             0.664891             0.186137  
ESR1_high_EERES_low                  NaN             0.225253  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
0.8500000000000002
PFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.11294029603681321
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.9506643695471886
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.17041162828681325
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.09780675079570984
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.5727023597783104
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.21861507806463215
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN             0.11294   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.950664             0.170412  
ESR1_low_EERES_high             0.097807             0.572702  
ESR1_high_EERES_low                  NaN             0.218615  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
OS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.05144461909813354
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.4596358987245618
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.1711472454608188
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.04517232602038412
ESR1_low_EERES_high vs ESR1_high_EERES_high 1.0
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.1424959833799518
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.051445   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.459636             0.171147  
ESR1_low_EERES_high             0.045172                  1.0  
ESR1_high_EERES_low                  NaN             0.142496  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.1921521612791453
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.4339500891265684
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.24616030463684727
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.5793521176061935
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.5727023597783104
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.4385780260809997
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.192152   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low               0.43395              0.24616  
ESR1_low_EERES_high             0.579352             0.572702  
ESR1_high_EERES_low                  NaN             0.438578  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DSS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.18961989284919784
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.8939689813389983
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.3288175012242954
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.2206713619198432
ESR1_low_EERES_high vs ESR1_high_EERES_high 1.0
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.5351434523977505
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN             0.18962   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.893969             0.328818  
ESR1_low_EERES_high             0.220671                  1.0  
ESR1_high_EERES_low                  NaN             0.535143  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
0.9000000000000002
PFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.26280234640943245
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.6605960880971886
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.3680748365470784
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.08234868877744322
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.5981614526835279
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.2652894025067584
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.262802   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.660596             0.368075  
ESR1_low_EERES_high             0.082349             0.598161  
ESR1_high_EERES_low                  NaN             0.265289  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
OS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.09933504094415792
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.7970987239881288
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.36722400890552753
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.0732404625356491
ESR1_low_EERES_high vs ESR1_high_EERES_high 1.0
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.36069833626522896
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.099335   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.797099             0.367224  
ESR1_low_EERES_high              0.07324                  1.0  
ESR1_high_EERES_low                  NaN             0.360698  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.380392159731479
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.5453895316303584
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.42437026665250965
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.6322502335634566
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.5981614526835279
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.5049850750938457
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.380392   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low               0.54539              0.42437  
ESR1_low_EERES_high              0.63225             0.598161  
ESR1_high_EERES_low                  NaN             0.504985  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DSS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.27534309137800883
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.9647318460017937
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.5414692209150938
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.20891238174069476
ESR1_low_EERES_high vs ESR1_high_EERES_high 1.0
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.5741490727971621
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.275343   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.964732             0.541469  
ESR1_low_EERES_high             0.208912                  1.0  
ESR1_high_EERES_low                  NaN             0.574149  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
0.9500000000000003
PFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.7792047079102058
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.35092780192694895
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.6722737984886651
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.27087438417344717
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.5929800980174267
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.4860994597361773
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.779205   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.350928             0.672274  
ESR1_low_EERES_high             0.270874              0.59298  
ESR1_high_EERES_low                  NaN             0.486099  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
OS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.27958553844043826
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.4896403924994549
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.7023602504442037
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.07356126950907078
ESR1_low_EERES_high vs ESR1_high_EERES_high 1.0
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.5254280008505965
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.279586   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low               0.48964              0.70236  
ESR1_low_EERES_high             0.073561                  1.0  
ESR1_high_EERES_low                  NaN             0.525428  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.9490160473143544
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.3359266542698106
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.70882928304802
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.39802471950693796
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.5929800980174267
ESR1_high_EERES_low vs ESR1_high_EERES_high 1.0
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.949016   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.335927             0.708829  
ESR1_low_EERES_high             0.398025              0.59298  
ESR1_high_EERES_low                  NaN                  1.0  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DSS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.49243459049686833
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.4520306548830516
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.7927353036331211
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.20590321073206466
ESR1_low_EERES_high vs ESR1_high_EERES_high 1.0
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.6547208460185769
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.492435   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.452031             0.792735  
ESR1_low_EERES_high             0.205903                  1.0  
ESR1_high_EERES_low                  NaN             0.654721  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image
In [16]:
q=0.6500000000000001
BRCA_ESR1_Threshold = q_groups[q]['BRCA_ESR1_Threshold']
BRCA_EERES_Threshold = q_groups[q]['BRCA_EERES_Threshold']

print("PFS")

br_pfs_es = brca_pfs.join(brca_esr1_es_df, how='inner')
br_pfs_es = br_pfs_es[~br_pfs_es.index.duplicated(keep="first")]
plotting_2groups(br_pfs_es, "PFS", BRCA_ESR1_Threshold, BRCA_EERES_Threshold)
plotting_4groups(br_pfs_es, "PFS", BRCA_ESR1_Threshold, BRCA_EERES_Threshold)

print("OS")

br_os_es = brca_os.join(brca_esr1_es_df, how='inner')
br_os_es = br_os_es[~br_os_es.index.duplicated(keep="first")]
plotting_2groups(br_os_es, "OS", BRCA_ESR1_Threshold, BRCA_EERES_Threshold)
plotting_4groups(br_os_es, "OS", BRCA_ESR1_Threshold, BRCA_EERES_Threshold)

print("DFS")

br_dfs_es = brca_dfs.join(brca_esr1_es_df, how='inner')
br_dfs_es = br_dfs_es[~br_dfs_es.index.duplicated(keep="first")]
plotting_2groups(br_dfs_es, "DFS", BRCA_ESR1_Threshold, BRCA_EERES_Threshold)
plotting_4groups(br_dfs_es, "DFS", BRCA_ESR1_Threshold, BRCA_EERES_Threshold)

print("DSS")

br_dss_es = brca_dss.join(brca_esr1_es_df, how='inner')
br_dss_es = br_dss_es[~br_dss_es.index.duplicated(keep="first")]
plotting_2groups(br_dss_es, "DSS", BRCA_ESR1_Threshold, BRCA_EERES_Threshold)
plotting_4groups(br_dss_es, "DSS", BRCA_ESR1_Threshold, BRCA_EERES_Threshold)

PFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.5368876878465606
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.057146857297087114
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.0315722762261594
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.23158262422018566
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.006002694865759897
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.0010180778656796406
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.536888   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.057147             0.031572  
ESR1_low_EERES_high             0.231583             0.006003  
ESR1_high_EERES_low                  NaN             0.001018  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
OS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.2010628123066087
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.015692692519150134
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.7097412919206219
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.28172096354689413
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.26900789034517814
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.05672685138527434
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.201063   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.015693             0.709741  
ESR1_low_EERES_high             0.281721             0.269008  
ESR1_high_EERES_low                  NaN             0.056727  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DFS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.9807412957743201
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.2956203112513096
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.05389266596807388
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.2952124313167216
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.04010865202237087
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.011689564885294188
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.980741   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low               0.29562             0.053893  
ESR1_low_EERES_high             0.295212             0.040109  
ESR1_high_EERES_low                  NaN              0.01169  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image 
DSS
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.02009822953406045
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.010451626179313573
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.28143026870034427
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.6570884614872321
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.024712990043976382
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.019547263881759785
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN            0.020098   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.010452              0.28143  
ESR1_low_EERES_high             0.657088             0.024713  
ESR1_high_EERES_low                  NaN             0.019547  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image
In [17]:
br_clinical_gdc = pd.read_table("Data/gdc/TCGA-BRCA/clinical/nationwidechildrens.org_clinical_patient_brca.txt", skiprows=1, header=0, index_col=1).iloc[1:]

brca_stage_group, brca_age_group = groups_clinical(br_clinical_gdc, "pathologic_stage", "age_at_initial_pathologic_diagnosis", brca_groups)
print(chi2_contingency(brca_stage_group.iloc[:,:4]))
print(chi2_contingency(brca_age_group))

Chi2ContingencyResult(statistic=13.824113634665348, pvalue=0.1287208429801775, dof=9, expected_freq=array([[3.73831776e-01, 1.12616822e+00, 4.81308411e-01, 1.86915888e-02],
       [3.73831776e+00, 1.12616822e+01, 4.81308411e+00, 1.86915888e-01],
       [3.55140187e+00, 1.06985981e+01, 4.57242991e+00, 1.77570093e-01],
       [7.23364486e+01, 2.17913551e+02, 9.31331776e+01, 3.61682243e+00]]))
Chi2ContingencyResult(statistic=13.883684624276736, pvalue=0.1265233654569464, dof=9, expected_freq=array([[1.28440367e-01, 9.77064220e-01, 7.52293578e-01, 1.42201835e-01],
       [1.28440367e+00, 9.77064220e+00, 7.52293578e+00, 1.42201835e+00],
       [1.28440367e+00, 9.77064220e+00, 7.52293578e+00, 1.42201835e+00],
       [2.53027523e+01, 1.92481651e+02, 1.48201835e+02, 2.80137615e+01]]))

/tmp/ipykernel_1042853/4149498380.py:251: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  ages_tmp.loc[ages_tmp[age_colname]=="[Not Available]", age_colname] = "10000"
In [18]:
br_gdc_histo_group = br_clinical_gdc[['histological_type']].join(brca_groups,how='inner').groupby(['group','histological_type'])[['ESR1']].count()

br_gdc_histo_group_1 = br_gdc_histo_group.loc['ESR1_low_EERES_low'].transpose()
br_gdc_histo_group_1.index = br_gdc_histo_group_1.index.rename('group')
br_gdc_histo_group_1.columns = br_gdc_histo_group_1.columns.rename('')
br_gdc_histo_group_1.index = ['ESR1_low_EERES_low']

br_gdc_histo_group_2 = br_gdc_histo_group.loc['ESR1_low_EERES_high'].transpose()
br_gdc_histo_group_2.index = br_gdc_histo_group_2.index.rename('group')
br_gdc_histo_group_2.columns = br_gdc_histo_group_2.columns.rename('')
br_gdc_histo_group_2.index = ['ESR1_low_EERES_high']

br_gdc_histo_group_3 = br_gdc_histo_group.loc['ESR1_high_EERES_low'].transpose()
br_gdc_histo_group_3.index = br_gdc_histo_group_3.index.rename('group')
br_gdc_histo_group_3.columns = br_gdc_histo_group_3.columns.rename('')
br_gdc_histo_group_3.index = ['ESR1_high_EERES_low']

br_gdc_histo_group_4 = br_gdc_histo_group.loc['ESR1_high_EERES_high'].transpose()
br_gdc_histo_group_4.index = br_gdc_histo_group_4.index.rename('group')
br_gdc_histo_group_4.columns = br_gdc_histo_group_4.columns.rename('')
br_gdc_histo_group_4.index = ['ESR1_high_EERES_high']

br_gdc_histo_group = pd.concat([br_gdc_histo_group_1, br_gdc_histo_group_2, br_gdc_histo_group_3, br_gdc_histo_group_4]).map(lambda x: 0 if np.isnan(x) else x)
print(chi2_contingency(br_gdc_histo_group))
br_gdc_histo_group*100/br_gdc_histo_group.sum(axis=1).values.reshape((-1,1))

Chi2ContingencyResult(statistic=11.38733063684378, pvalue=0.724685166632109, dof=15, expected_freq=array([[2.61160550e+02, 9.12706422e+01, 1.80733945e+00, 1.26513761e+01,
        1.17477064e+01, 1.53623853e+01],
       [1.32568807e+01, 4.63302752e+00, 9.17431193e-02, 6.42201835e-01,
        5.96330275e-01, 7.79816514e-01],
       [1.32568807e+01, 4.63302752e+00, 9.17431193e-02, 6.42201835e-01,
        5.96330275e-01, 7.79816514e-01],
       [1.32568807e+00, 4.63302752e-01, 9.17431193e-03, 6.42201835e-02,
        5.96330275e-02, 7.79816514e-02]]))
Out[18]:
Infiltrating Ductal Carcinoma Infiltrating Lobular Carcinoma Metaplastic Carcinoma Mixed Histology (please specify) Mucinous Carcinoma Other, specify
ESR1_low_EERES_low 65.228426 24.619289 0.507614 3.299492 2.791878 3.553299
ESR1_low_EERES_high 70.000000 15.000000 0.000000 5.000000 5.000000 5.000000
ESR1_high_EERES_low 85.000000 0.000000 0.000000 0.000000 5.000000 10.000000
ESR1_high_EERES_high 50.000000 50.000000 0.000000 0.000000 0.000000 0.000000
In [19]:
brca_stage_group*100/brca_stage_group.sum(axis=1).values.reshape((-1,1))
Out[19]:
T1 T2 T3 T4 [Discrepancy] TX [Not Available]
ESR1_high_EERES_high 50.000000 0.000000 50.000000 0.000000 NaN NaN NaN
ESR1_high_EERES_low 10.000000 50.000000 35.000000 5.000000 NaN NaN NaN
ESR1_low_EERES_high 25.000000 45.000000 20.000000 5.000000 5.000000 NaN NaN
ESR1_low_EERES_low 18.274112 56.345178 23.096447 0.507614 0.507614 1.015228 0.253807
In [21]:
brca_age_group*100/brca_age_group.sum(axis=1).values.reshape((-1,1))
Out[21]:
21-40 41-60 61-80 81-100
ESR1_high_EERES_high 0.000000 0.000000 100.000000 0.000000
ESR1_high_EERES_low 0.000000 25.000000 60.000000 15.000000
ESR1_low_EERES_high 0.000000 50.000000 45.000000 5.000000
ESR1_low_EERES_low 7.106599 50.253807 35.786802 6.852792
In [22]:
# The Spearman of ER-related genes vs EERES
brca_esr1_es_df = brca_gdc_tpm.transpose()[["ESR1","ESR2","ESRRA","ESRRB","ESRRG","GPER1"]].groupby(level=0).mean().join(brca_es, how='inner')

ERs_vs_EERES_table(brca_esr1_es_df)
Out[22]:
R p
ESR1 0.641278 7.04e-128
ESR2 -0.218046 2.99e-13
ESRRA -0.041781 1.67e-01
ESRRB 0.112625 1.88e-04
ESRRG 0.162339 6.60e-08
GPER1 0.273689 2.89e-20

METABRIC¶

Figure 2f-i¶

In [23]:
br_metabric_mrna = pd.read_table("Data/brca_metabric/data_mrna_agilent_microarray.txt", index_col=0).iloc[:,1:]
br_metabric_mrna.index.name = "gene_name"
br_metabric_mrna = br_metabric_mrna.groupby(level=0).mean()
print("n:",len(br_metabric_mrna.columns))

n: 1904
In [24]:
br_metabric_hallmark_es = gsva_py2r(np.log2(br_metabric_mrna+1),hallmark_gs)

Converting df
Estimating GSVA scores for 50 gene sets.
Estimating ECDFs with Gaussian kernels
  |======================================================================| 100%
In [28]:
br_metabric_clinical_survival = pd.read_table("Data/brca_metabric/data_clinical_patient.txt", skiprows=4, header=0, index_col=0)
br_metabric_clinical = pd.read_table("Data/brca_metabric/brca_metabric_clinical_data.tsv", index_col=1)[["ER Status", "HER2 Status", "Hormone Therapy"]]
br_metabric_hormonal_er_index = set(br_metabric_clinical[(br_metabric_clinical["Hormone Therapy"]=="YES")].index).intersection(br_metabric_mrna.columns)
br_metabric_es = pd.DataFrame(br_metabric_hallmark_es["HALLMARK_ESTROGEN_RESPONSE_EARLY"].values, index=br_metabric_hallmark_es.index, columns=["EARLY"])
br_metabric_es.rename(columns={'EARLY':"EERES"}).to_excel("Table S2.xlsx", sheet_name="METABRIC")
br_metabric_esr1_es_df = br_metabric_mrna.transpose().loc[list(br_metabric_hormonal_er_index),["ESR1"]].join(br_metabric_es, how='inner')
print("n:",len(br_metabric_hormonal_er_index))

br_metabric_EERES_Threshold = br_metabric_esr1_es_df["EARLY"].quantile(0.65)
br_metabric_ESR1_Threshold = br_metabric_esr1_es_df["ESR1"].quantile(0.65)
print(br_metabric_ESR1_Threshold)
print(br_metabric_EERES_Threshold)
plotting_scatter_esr1_es(br_metabric_esr1_es_df, br_metabric_ESR1_Threshold, br_metabric_EERES_Threshold)

print(stats.spearmanr(np.log2(br_metabric_esr1_es_df["ESR1"]+1), br_metabric_esr1_es_df["EARLY"]))
print(stats.pearsonr(np.log2(br_metabric_esr1_es_df["ESR1"]+1), br_metabric_esr1_es_df["EARLY"]))

br_metabric_groups = grouping4(br_metabric_esr1_es_df, br_metabric_ESR1_Threshold, br_metabric_EERES_Threshold)

n: 1174
11.132513727
0.15338301706278723
No description has been provided for this image 
SignificanceResult(statistic=0.4274871958216788, pvalue=2.378334466592854e-53)
PearsonRResult(statistic=0.5753535687958771, pvalue=1.9694227883792115e-104)
In [29]:
br_metabric_rfs = br_metabric_clinical_survival[["RFS_STATUS", "RFS_MONTHS"]]
br_metabric_rfs["RFS_STATUS"] = [str(s)[0] if str(s)[0] != "n" else np.nan for s in br_metabric_rfs["RFS_STATUS"]]
br_metabric_rfs = br_metabric_rfs.dropna()
br_metabric_os = br_metabric_clinical_survival[["OS_STATUS", "OS_MONTHS"]]
br_metabric_os["OS_STATUS"] = [str(s)[0] for s in br_metabric_os["OS_STATUS"]]
br_metabric_os = br_metabric_os.dropna()

# br_metabric_esr1_es_os = br_metabric_os.join(br_metabric_esr1_es_df, how='inner')
# plotting_2groups(br_metabric_esr1_es_os, "OS", br_metabric_ESR1_Threshold, br_metabric_EERES_Threshold)
# plotting_4groups(br_metabric_esr1_es_os, "OS", br_metabric_ESR1_Threshold, br_metabric_EERES_Threshold)

br_metabric_esr1_es_rfs = br_metabric_rfs.join(br_metabric_esr1_es_df, how='inner')
plotting_2groups(br_metabric_esr1_es_rfs, "RFS", br_metabric_ESR1_Threshold, br_metabric_EERES_Threshold)
plotting_4groups(br_metabric_esr1_es_rfs, "RFS", br_metabric_ESR1_Threshold, br_metabric_EERES_Threshold)

/tmp/ipykernel_1042853/4203505307.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  br_metabric_rfs["RFS_STATUS"] = [str(s)[0] if str(s)[0] != "n" else np.nan for s in br_metabric_rfs["RFS_STATUS"]]
/tmp/ipykernel_1042853/4203505307.py:5: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  br_metabric_os["OS_STATUS"] = [str(s)[0] for s in br_metabric_os["OS_STATUS"]]
No description has been provided for this image
No description has been provided for this image 
ESR1_low_EERES_low vs ESR1_low_EERES_high 0.036359895073894845
ESR1_low_EERES_low vs ESR1_high_EERES_low 0.1965837436290443
ESR1_low_EERES_low vs ESR1_high_EERES_high 0.0007778738276053565
ESR1_low_EERES_high vs ESR1_high_EERES_low 0.4487430284158661
ESR1_low_EERES_high vs ESR1_high_EERES_high 0.15632356713275672
ESR1_high_EERES_low vs ESR1_high_EERES_high 0.0418002535004614
                     ESR1_low_EERES_low ESR1_low_EERES_high  \
ESR1_low_EERES_low                  NaN             0.03636   
ESR1_low_EERES_high                 NaN                 NaN   
ESR1_high_EERES_low                 NaN                 NaN   
ESR1_high_EERES_high                NaN                 NaN   

                     ESR1_high_EERES_low ESR1_high_EERES_high  
ESR1_low_EERES_low              0.196584             0.000778  
ESR1_low_EERES_high             0.448743             0.156324  
ESR1_high_EERES_low                  NaN               0.0418  
ESR1_high_EERES_high                 NaN                  NaN
No description has been provided for this image

Figure 4¶

In [30]:
brca_deseq2_res = deseq2_py2r(brca_gtex_gdc_gc, brca_gtex_gdc_pheno_df, "~type", ["type","Cancer","Normal"])
ov_deseq2_res = deseq2_py2r(ov_gtex_gdc_gc, ov_gtex_gdc_pheno_df, "~type", ["type","Cancer","Normal"])
ucec_deseq2_res = deseq2_py2r(ucec_gtex_gdc_gc, ucec_gtex_gdc_pheno_df, "~type", ["type","Cancer","Normal"])
cesc_deseq2_res = deseq2_py2r(cesc_gtex_gdc_gc, cesc_gtex_gdc_pheno_df, "~type", ["type","Cancer","Normal"])

Converting df

R[write to console]: estimating size factors

R[write to console]: estimating dispersions

R[write to console]: gene-wise dispersion estimates

R[write to console]: mean-dispersion relationship

R[write to console]: final dispersion estimates

R[write to console]: fitting model and testing

R[write to console]: -- replacing outliers and refitting for 5674 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

R[write to console]: estimating dispersions

R[write to console]: fitting model and testing


Converting df

R[write to console]: estimating size factors

R[write to console]: estimating dispersions

R[write to console]: gene-wise dispersion estimates

R[write to console]: mean-dispersion relationship

R[write to console]: final dispersion estimates

R[write to console]: fitting model and testing

R[write to console]: -- replacing outliers and refitting for 2579 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

R[write to console]: estimating dispersions

R[write to console]: fitting model and testing


Converting df

R[write to console]: estimating size factors

R[write to console]: estimating dispersions

R[write to console]: gene-wise dispersion estimates

R[write to console]: mean-dispersion relationship

R[write to console]: final dispersion estimates

R[write to console]: fitting model and testing

R[write to console]: -- replacing outliers and refitting for 4019 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

R[write to console]: estimating dispersions

R[write to console]: fitting model and testing


Converting df

R[write to console]: estimating size factors

R[write to console]: estimating dispersions

R[write to console]: gene-wise dispersion estimates

R[write to console]: mean-dispersion relationship

R[write to console]: final dispersion estimates

R[write to console]: fitting model and testing

R[write to console]: -- replacing outliers and refitting for 2706 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

R[write to console]: estimating dispersions

R[write to console]: fitting model and testing
In [31]:
def ploting_log2fc_deseq2(dds_res, genes, title):
    tmp = dds_res.loc[genes,["log2FoldChange", "lfcSE", "padj"]].reset_index()
    tmp = tmp.rename(columns={'index':'ERs'})
    fig, ax = plt.subplots(figsize=(6, 4))
    sns.barplot(data=tmp, x='ERs', y='log2FoldChange', ax=ax)
    x_coords = [p.get_x() + 0.5 * p.get_width() for p in ax.patches]
    y_coords = [p.get_height() for p in ax.patches]
    ax.set_ylim((-5, 6.5))
    ax.set_title(title, fontdict={'fontweight' : 'bold'})
    ax.bar_label(ax.containers[-1], labels=[f"padj:{p:.2e}" for p in tmp['padj']], label_type='edge', fontsize=7, padding=16)
    ax.errorbar(x=x_coords, y=y_coords, yerr=tmp["lfcSE"], fmt="none", c="k")

ERs = ["ESR1", "ESR2", "ESRRA", "ESRRB", "ESRRG", "GPER1"]
ploting_log2fc_deseq2(brca_deseq2_res, ERs, "BRCA")
ploting_log2fc_deseq2(ov_deseq2_res, ERs, "OV")
ploting_log2fc_deseq2(ucec_deseq2_res, ERs, "UCEC")
ploting_log2fc_deseq2(cesc_deseq2_res, ERs, "CESC")

/home/oscar/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if pd.api.types.is_categorical_dtype(vector):
/home/oscar/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if pd.api.types.is_categorical_dtype(vector):
/home/oscar/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if pd.api.types.is_categorical_dtype(vector):
/home/oscar/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if pd.api.types.is_categorical_dtype(vector):
/home/oscar/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if pd.api.types.is_categorical_dtype(vector):
/home/oscar/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if pd.api.types.is_categorical_dtype(vector):
/home/oscar/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if pd.api.types.is_categorical_dtype(vector):
/home/oscar/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if pd.api.types.is_categorical_dtype(vector):
/home/oscar/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if pd.api.types.is_categorical_dtype(vector):
/home/oscar/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if pd.api.types.is_categorical_dtype(vector):
/home/oscar/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if pd.api.types.is_categorical_dtype(vector):
/home/oscar/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if pd.api.types.is_categorical_dtype(vector):
No description has been provided for this image
No description has been provided for this image
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In [114]:
brca_gtex_gdc_tmm
Out[114]:
NAME GTEX-1117F-2826-SM-5GZXL GTEX-111YS-1926-SM-5GICC GTEX-1122O-1226-SM-5H113 GTEX-117XS-1926-SM-5GICO GTEX-117YX-1426-SM-5H12H GTEX-1192X-2326-SM-5987X GTEX-11DXW-0626-SM-5N9ER GTEX-11DXY-2326-SM-5GICW GTEX-11DXZ-1926-SM-5GZZL ... TCGA-AC-A5EH TCGA-A1-A0SD TCGA-A2-A0SU TCGA-E9-A1NI TCGA-B6-A0RQ TCGA-A2-A0CX TCGA-A2-A25F TCGA-AC-A23G TCGA-E2-A15D TCGA-OL-A5D7
5S_rRNA None 0.003827 0.001981 0.002036 0.003792 0.000000 0.002901 0.003889 0.001373 0.005507 ... 0.000000 0.001755 0.007500 0.002686 0.000000 0.000000 0.000000 0.003857 0.000000 0.000000
5_8S_rRNA None 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.002633 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
7SK None 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.002593 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.005977 0.001900 0.000000 0.000000 0.000000
A1BG None 6.001498 5.181340 5.602321 3.640725 1.674599 7.363725 7.218174 3.646930 2.713682 ... 0.211380 0.347529 0.112498 0.024170 0.274054 0.167352 0.226107 0.485959 0.284629 0.071593
A1BG-AS1 None 1.775954 2.963030 1.661153 0.788824 0.699833 2.521612 2.209010 1.010595 0.881066 ... 1.648760 1.563882 0.922483 1.111843 2.062616 1.004115 0.784724 2.950462 1.632871 1.121616
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
ZYX None 435.169854 202.309954 412.422032 286.858795 265.011602 276.031384 292.382703 309.329985 186.556815 ... 513.736837 154.366164 155.652193 101.830294 257.120246 198.605558 225.973774 351.139743 194.821014 203.418199
ZYXP1 None 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
ZZEF1 None 103.903488 140.688446 131.214828 158.963158 99.351232 101.514204 92.389512 99.214077 124.001162 ... 58.002546 65.319698 32.151920 55.229580 76.605275 43.365212 115.952876 71.192923 76.490280 56.629679
ZZZ3 None 48.828515 60.591578 44.443995 42.839198 27.243483 55.104175 44.180201 46.948734 37.039995 ... 41.324700 67.246906 73.168680 37.560949 65.830630 39.495186 74.269426 41.896567 52.132036 20.905014
snoZ196 None 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.042276 0.000000 0.000000 0.000000 0.000000 0.000000 0.026601 0.000000 0.014980 0.000000

36004 rows × 1555 columns

In [32]:
brca_gtex_gdc_pheno
Out[32]:
array(['Normal', 'Normal', 'Normal', ..., 'Cancer', 'Cancer', 'Cancer'],
      dtype='<U6')
In [33]:
brca_gsea_hallmark = gsea(brca_gtex_gdc_tmm, brca_gtex_gdc_pheno, hallmark_gs)
ov_gsea_hallmark = gsea(ov_gtex_gdc_tmm, ov_gtex_gdc_pheno, hallmark_gs)
ucec_gsea_hallmark = gsea(ucec_gtex_gdc_tmm, ucec_gtex_gdc_pheno, hallmark_gs)
cesc_gsea_hallmark = gsea(cesc_gtex_gdc_tmm, cesc_gtex_gdc_pheno, hallmark_gs)

2023-12-18 11:47:22,713 [WARNING] Input data contains NA, filled NA with 0
/home/oscar/.local/lib/python3.10/site-packages/gseapy/gsea.py:116: FutureWarning: DataFrame.groupby with axis=1 is deprecated. Do `frame.T.groupby(...)` without axis instead.
  df_std = df.groupby(by=cls_dict, axis=1).std(numeric_only=True)
2023-12-18 11:48:00,435 [WARNING] Input data contains NA, filled NA with 0
/home/oscar/.local/lib/python3.10/site-packages/gseapy/gsea.py:116: FutureWarning: DataFrame.groupby with axis=1 is deprecated. Do `frame.T.groupby(...)` without axis instead.
  df_std = df.groupby(by=cls_dict, axis=1).std(numeric_only=True)
2023-12-18 11:48:18,561 [WARNING] Input data contains NA, filled NA with 0
/home/oscar/.local/lib/python3.10/site-packages/gseapy/gsea.py:116: FutureWarning: DataFrame.groupby with axis=1 is deprecated. Do `frame.T.groupby(...)` without axis instead.
  df_std = df.groupby(by=cls_dict, axis=1).std(numeric_only=True)
2023-12-18 11:48:40,330 [WARNING] Input data contains NA, filled NA with 0
/home/oscar/.local/lib/python3.10/site-packages/gseapy/gsea.py:116: FutureWarning: DataFrame.groupby with axis=1 is deprecated. Do `frame.T.groupby(...)` without axis instead.
  df_std = df.groupby(by=cls_dict, axis=1).std(numeric_only=True)
In [34]:
brca_gsea_c6 = gsea(brca_gtex_gdc_tmm, brca_gtex_gdc_pheno, c6_gs)
ov_gsea_c6 = gsea(ov_gtex_gdc_tmm, ov_gtex_gdc_pheno, c6_gs)
ucec_gsea_c6 = gsea(ucec_gtex_gdc_tmm, ucec_gtex_gdc_pheno, c6_gs)
cesc_gsea_c6 = gsea(cesc_gtex_gdc_tmm, cesc_gtex_gdc_pheno, c6_gs)

2023-12-18 11:48:53,858 [WARNING] Input data contains NA, filled NA with 0
/home/oscar/.local/lib/python3.10/site-packages/gseapy/gsea.py:116: FutureWarning: DataFrame.groupby with axis=1 is deprecated. Do `frame.T.groupby(...)` without axis instead.
  df_std = df.groupby(by=cls_dict, axis=1).std(numeric_only=True)
2023-12-18 11:49:44,843 [WARNING] Input data contains NA, filled NA with 0
/home/oscar/.local/lib/python3.10/site-packages/gseapy/gsea.py:116: FutureWarning: DataFrame.groupby with axis=1 is deprecated. Do `frame.T.groupby(...)` without axis instead.
  df_std = df.groupby(by=cls_dict, axis=1).std(numeric_only=True)
2023-12-18 11:50:15,921 [WARNING] Input data contains NA, filled NA with 0
/home/oscar/.local/lib/python3.10/site-packages/gseapy/gsea.py:116: FutureWarning: DataFrame.groupby with axis=1 is deprecated. Do `frame.T.groupby(...)` without axis instead.
  df_std = df.groupby(by=cls_dict, axis=1).std(numeric_only=True)
2023-12-18 11:50:50,355 [WARNING] Input data contains NA, filled NA with 0
/home/oscar/.local/lib/python3.10/site-packages/gseapy/gsea.py:116: FutureWarning: DataFrame.groupby with axis=1 is deprecated. Do `frame.T.groupby(...)` without axis instead.
  df_std = df.groupby(by=cls_dict, axis=1).std(numeric_only=True)
In [30]:
brca_c6_es = gsva_py2r(np.log2(brca_gdc_tpm+1),c6_gs)
ov_c6_es = gsva_py2r(np.log2(ov_gdc_tpm+1),c6_gs)
ucec_c6_es = gsva_py2r(np.log2(ucec_gdc_tpm+1),c6_gs)
cesc_c6_es = gsva_py2r(np.log2(cesc_gdc_tpm+1),c6_gs)

Converting df
Estimating GSVA scores for 189 gene sets.
Estimating ECDFs with Gaussian kernels
  |======================================================================| 100%

Converting df
Estimating GSVA scores for 189 gene sets.
Estimating ECDFs with Gaussian kernels
  |======================================================================| 100%

Converting df
Estimating GSVA scores for 189 gene sets.
Estimating ECDFs with Gaussian kernels
  |======================================================================| 100%

Converting df
Estimating GSVA scores for 189 gene sets.
Estimating ECDFs with Gaussian kernels
  |======================================================================| 100%
In [35]:
plotting_gsea(brca_gsea_hallmark, "HALLMARK_ESTROGEN_RESPONSE_EARLY")
plotting_gsea(ov_gsea_hallmark, "HALLMARK_ESTROGEN_RESPONSE_EARLY")
plotting_gsea(ucec_gsea_hallmark, "HALLMARK_ESTROGEN_RESPONSE_EARLY")
plotting_gsea(cesc_gsea_hallmark, "HALLMARK_ESTROGEN_RESPONSE_EARLY")

/home/oscar/.local/lib/python3.10/site-packages/gseapy/plot.py:602: FutureWarning: The 'method' keyword in Series.replace is deprecated and will be removed in a future version.
  df[self.colname].replace(
/home/oscar/.local/lib/python3.10/site-packages/gseapy/plot.py:602: FutureWarning: The 'method' keyword in Series.replace is deprecated and will be removed in a future version.
  df[self.colname].replace(
/home/oscar/.local/lib/python3.10/site-packages/gseapy/plot.py:602: FutureWarning: The 'method' keyword in Series.replace is deprecated and will be removed in a future version.
  df[self.colname].replace(
/home/oscar/.local/lib/python3.10/site-packages/gseapy/plot.py:602: FutureWarning: The 'method' keyword in Series.replace is deprecated and will be removed in a future version.
  df[self.colname].replace(
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In [36]:
plotting_gsea(brca_gsea_hallmark, "HALLMARK_ESTROGEN_RESPONSE_LATE")
plotting_gsea(ov_gsea_hallmark, "HALLMARK_ESTROGEN_RESPONSE_LATE")
plotting_gsea(ucec_gsea_hallmark, "HALLMARK_ESTROGEN_RESPONSE_LATE")
plotting_gsea(cesc_gsea_hallmark, "HALLMARK_ESTROGEN_RESPONSE_LATE")

/home/oscar/.local/lib/python3.10/site-packages/gseapy/plot.py:602: FutureWarning: The 'method' keyword in Series.replace is deprecated and will be removed in a future version.
  df[self.colname].replace(
/home/oscar/.local/lib/python3.10/site-packages/gseapy/plot.py:602: FutureWarning: The 'method' keyword in Series.replace is deprecated and will be removed in a future version.
  df[self.colname].replace(
/home/oscar/.local/lib/python3.10/site-packages/gseapy/plot.py:602: FutureWarning: The 'method' keyword in Series.replace is deprecated and will be removed in a future version.
  df[self.colname].replace(
/home/oscar/.local/lib/python3.10/site-packages/gseapy/plot.py:602: FutureWarning: The 'method' keyword in Series.replace is deprecated and will be removed in a future version.
  df[self.colname].replace(
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Figure 5¶

In [33]:
brca_pheno_bestes = np.full(len(brca_es), "High EERES")
brca_pheno_bestes[brca_es["EARLY"]<=brca_bestes] = "Low EERES"
brca_sortq = np.argsort(brca_pheno_bestes)[::-1]
brca_pheno_bestes = brca_pheno_bestes[brca_sortq]
brca_gdc_tmm = genecount_to_tmm_for_gsea(brca_gdc_genecount.iloc[:,brca_sortq])
brca_gsea_bestes_c6 = gsea(brca_gdc_tmm, brca_pheno_bestes, c6_gs)
brca_gsea_bestes_c3 = gsea(brca_gdc_tmm, brca_pheno_bestes, c3_gs)
brca_gsea_bestes_hm = gsea(brca_gdc_tmm, brca_pheno_bestes, hallmark_gs)

2023-05-30 21:44:49,557 [WARNING] Input data contains NA, filled NA with 0
2023-05-30 21:45:54,428 [WARNING] Input data contains NA, filled NA with 0
2023-05-30 21:47:09,522 [WARNING] Input data contains NA, filled NA with 0
In [34]:
brca_pheno_bestes_df = pd.DataFrame(brca_pheno_bestes, index=brca_es.iloc[brca_sortq].index, columns=["EERES"])
brca_eeres_deseq2_res = deseq2_py2r(brca_gdc_genecount.iloc[:,brca_sortq], brca_pheno_bestes_df, "~EERES", ["EERES", "High EERES", "Low EERES"])

Converting df

R[write to console]:   Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]

R[write to console]: estimating size factors

R[write to console]:   Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]

R[write to console]: estimating dispersions

R[write to console]: gene-wise dispersion estimates

R[write to console]: mean-dispersion relationship

R[write to console]:   Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]

R[write to console]: final dispersion estimates

R[write to console]:   Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]

R[write to console]: fitting model and testing

R[write to console]:   Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]

R[write to console]: -- replacing outliers and refitting for 11378 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

R[write to console]: estimating dispersions

R[write to console]:   Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]

R[write to console]: fitting model and testing

R[write to console]:   Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]
In [35]:
ov_pheno_bestes = np.full(len(ov_es), "High EERES")
ov_pheno_bestes[ov_es["EARLY"]<=ov_bestes] = "Low EERES"
ov_sortq = np.argsort(ov_pheno_bestes)[::-1]
ov_pheno_bestes = ov_pheno_bestes[ov_sortq]
ov_gdc_tmm = genecount_to_tmm_for_gsea(ov_gdc_genecount.iloc[:,ov_sortq])
ov_gsea_bestes_c6 = gsea(ov_gdc_tmm, ov_pheno_bestes, c6_gs)
ov_gsea_bestes_c3 = gsea(ov_gdc_tmm, ov_pheno_bestes, c3_gs)
ov_gsea_bestes_hm = gsea(ov_gdc_tmm, ov_pheno_bestes, hallmark_gs)

2023-05-30 21:54:16,545 [WARNING] Input data contains NA, filled NA with 0
2023-05-30 21:54:59,956 [WARNING] Input data contains NA, filled NA with 0
2023-05-30 21:55:53,786 [WARNING] Input data contains NA, filled NA with 0
In [36]:
ov_pheno_bestes_df = pd.DataFrame(ov_pheno_bestes, index=ov_es.iloc[ov_sortq].index, columns=["EERES"])
ov_pheno_bestes_df
ov_eeres_deseq2_res = deseq2_py2r(ov_gdc_genecount.iloc[:,ov_sortq], ov_pheno_bestes_df, "~EERES", ["EERES", "High EERES", "Low EERES"])

Converting df

R[write to console]:   Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]

R[write to console]: estimating size factors

R[write to console]:   Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]

R[write to console]: estimating dispersions

R[write to console]: gene-wise dispersion estimates

R[write to console]: mean-dispersion relationship

R[write to console]:   Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]

R[write to console]: final dispersion estimates

R[write to console]:   Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]

R[write to console]: fitting model and testing

R[write to console]:   Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]

R[write to console]: -- replacing outliers and refitting for 5278 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

R[write to console]: estimating dispersions

R[write to console]:   Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]

R[write to console]: fitting model and testing

R[write to console]:   Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]
In [37]:
ucec_pheno_bestes = np.full(len(ucec_es), "High EERES")
ucec_pheno_bestes[ucec_es["EARLY"]<=ucec_bestes] = "Low EERES"
ucec_sortq = np.argsort(ucec_pheno_bestes)[::-1]
ucec_pheno_bestes = ucec_pheno_bestes[ucec_sortq]
ucec_gdc_tmm = genecount_to_tmm_for_gsea(ucec_gdc_genecount.iloc[:,ucec_sortq])
ucec_gsea_bestes_c6 = gsea(ucec_gdc_tmm, ucec_pheno_bestes, c6_gs)
ucec_gsea_bestes_c3 = gsea(ucec_gdc_tmm, ucec_pheno_bestes, c3_gs)
ucec_gsea_bestes_hm = gsea(ucec_gdc_tmm, ucec_pheno_bestes, hallmark_gs)

2023-05-30 21:58:12,638 [WARNING] Input data contains NA, filled NA with 0
2023-05-30 21:59:02,953 [WARNING] Input data contains NA, filled NA with 0
2023-05-30 22:00:01,337 [WARNING] Input data contains NA, filled NA with 0
In [38]:
ucec_pheno_bestes_df = pd.DataFrame(ucec_pheno_bestes, index=ucec_es.iloc[ucec_sortq].index, columns=["EERES"])
ucec_pheno_bestes_df
ucec_eeres_deseq2_res = deseq2_py2r(ucec_gdc_genecount.iloc[:,ucec_sortq], ucec_pheno_bestes_df, "~EERES", ["EERES", "High EERES", "Low EERES"])

Converting df

R[write to console]:   Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]

R[write to console]: estimating size factors

R[write to console]:   Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]

R[write to console]: estimating dispersions

R[write to console]: gene-wise dispersion estimates

R[write to console]: mean-dispersion relationship

R[write to console]:   Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]

R[write to console]: final dispersion estimates

R[write to console]:   Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]

R[write to console]: fitting model and testing

R[write to console]:   Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]

R[write to console]: -- replacing outliers and refitting for 8291 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

R[write to console]: estimating dispersions

R[write to console]:   Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]

R[write to console]: fitting model and testing

R[write to console]:   Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]
In [39]:
cesc_pheno_bestes = np.full(len(cesc_es), "High EERES")
cesc_pheno_bestes[cesc_es["EARLY"]<=cesc_bestes] = "Low EERES"
cesc_sortq = np.argsort(cesc_pheno_bestes)[::-1]
cesc_pheno_bestes = cesc_pheno_bestes[cesc_sortq]
cesc_gdc_tmm = genecount_to_tmm_for_gsea(cesc_gdc_genecount.iloc[:,cesc_sortq])
cesc_gsea_bestes_c6 = gsea(cesc_gdc_tmm, cesc_pheno_bestes, c6_gs)
cesc_gsea_bestes_c3 = gsea(cesc_gdc_tmm, cesc_pheno_bestes, c3_gs)
cesc_gsea_bestes_hm = gsea(cesc_gdc_tmm, cesc_pheno_bestes, hallmark_gs)

2023-05-30 22:03:33,299 [WARNING] Input data contains NA, filled NA with 0
2023-05-30 22:04:13,490 [WARNING] Input data contains NA, filled NA with 0
2023-05-30 22:05:03,477 [WARNING] Input data contains NA, filled NA with 0
In [40]:
cesc_pheno_bestes_df = pd.DataFrame(cesc_pheno_bestes, index=cesc_es.iloc[cesc_sortq].index, columns=["EERES"])
cesc_pheno_bestes_df
cesc_eeres_deseq2_res = deseq2_py2r(cesc_gdc_genecount.iloc[:,cesc_sortq], cesc_pheno_bestes_df, "~EERES", ["EERES", "High EERES", "Low EERES"])

Converting df

R[write to console]:   Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]

R[write to console]: estimating size factors

R[write to console]:   Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]

R[write to console]: estimating dispersions

R[write to console]: gene-wise dispersion estimates

R[write to console]: mean-dispersion relationship

R[write to console]:   Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]

R[write to console]: final dispersion estimates

R[write to console]:   Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]

R[write to console]: fitting model and testing

R[write to console]:   Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]

R[write to console]: -- replacing outliers and refitting for 6626 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

R[write to console]: estimating dispersions

R[write to console]:   Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]

R[write to console]: fitting model and testing

R[write to console]:   Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]
In [41]:
plotting_gsea(brca_gsea_bestes_c6, "MEK_UP.V1_DN")
plotting_gsea(ov_gsea_bestes_c6, "MEK_UP.V1_UP")
plotting_gsea(ucec_gsea_bestes_c6, "MEK_UP.V1_UP")
plotting_gsea(cesc_gsea_bestes_c6, "MEK_UP.V1_UP")
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In [42]:
plotting_gsea(brca_gsea_bestes_c6, "MEK_UP.V1_UP")
plotting_gsea(ov_gsea_bestes_c6, "MEK_UP.V1_DN")
plotting_gsea(ucec_gsea_bestes_c6, "MEK_UP.V1_DN")
plotting_gsea(cesc_gsea_bestes_c6, "MEK_UP.V1_DN")
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In [43]:
# CCLE

ccle_log2tpm = pd.read_csv("Data/CCLE/OmicsExpressionProteinCodingGenesTPMLogp1.csv", index_col=0)
ccle_log2tpm.columns = [c.split("(")[0][:-1] for c in ccle_log2tpm.columns]
In [44]:
ccle_model = pd.read_csv("Data/CCLE/Model.csv", index_col=0)[["COSMICID"]]
ccle_model = ccle_model.reset_index().set_index("COSMICID")
In [90]:
ccle_selected_genes = ccle_log2tpm[["MALL", "NT5C2", "CDC42BPB", "MYO5A", "TNFRSF21", "PRKCH", "PCDH1", "SCAMP4", "MPZL2", "TTC9", "INAVA", "EPHA2", "EGFR", "MAPK1", "RAF1", "MAP2K1"]]
In [46]:
ccle_ic50 = pd.read_excel("Data/CCLE/GDSC2_fitted_dose_response_24Jul22.xlsx", index_col=3)[["DRUG_NAME", "DRUG_ID", "LN_IC50", "AUC"]]
In [47]:
def plotting_scatter_spearman(x, y, xlabel, ylabel):  
    from scipy.stats import spearmanr
    plt.scatter(x, y)
    plt.xlabel(xlabel, weight="bold", fontsize=12, labelpad=4)
    plt.ylabel(ylabel, weight="bold", fontsize=12, labelpad=4)
    sr, sp = spearmanr(x, y)
    plt.title(f"Spearman R={sr:.3f}, p={sp:.3e}", weight="bold")
    plt.show()
    
ccle_model_ic50 = ccle_model.join(ccle_ic50, how='inner').reset_index().set_index("ModelID")
ccle_model_ic50_esr1_eeres = ccle_model_ic50.join(ccle_selected_genes, how="inner")
for did in [1199,1200,1816,1372]:
    ccle_model_ic50_esr1_eeres_tamoxifen = ccle_model_ic50_esr1_eeres[ccle_model_ic50_esr1_eeres["DRUG_ID"]==did]
    for g in ["MALL", "NT5C2", "CDC42BPB", "MYO5A", "TNFRSF21", "PRKCH", "PCDH1", "SCAMP4", "MPZL2", "TTC9", "INAVA", "EPHA2"]:
        print(g)
        print(did, ccle_model_ic50_esr1_eeres_tamoxifen["DRUG_NAME"][0])
        dn = ccle_model_ic50_esr1_eeres_tamoxifen["DRUG_NAME"][0]
        s, p = spearmanr(ccle_model_ic50_esr1_eeres_tamoxifen[g], ccle_model_ic50_esr1_eeres_tamoxifen["LN_IC50"])
        plotting_scatter_spearman(ccle_model_ic50_esr1_eeres_tamoxifen[g].values, ccle_model_ic50_esr1_eeres_tamoxifen["LN_IC50"].values, f"log2[{g}+1]", f"{dn} LN_IC50")
        plt.show()

MALL
1199 Tamoxifen
No description has been provided for this image 
NT5C2
1199 Tamoxifen
No description has been provided for this image 
CDC42BPB
1199 Tamoxifen
No description has been provided for this image 
MYO5A
1199 Tamoxifen
No description has been provided for this image 
TNFRSF21
1199 Tamoxifen
No description has been provided for this image 
PRKCH
1199 Tamoxifen
No description has been provided for this image 
PCDH1
1199 Tamoxifen
No description has been provided for this image 
SCAMP4
1199 Tamoxifen
No description has been provided for this image 
MPZL2
1199 Tamoxifen
No description has been provided for this image 
TTC9
1199 Tamoxifen
No description has been provided for this image 
INAVA
1199 Tamoxifen
No description has been provided for this image 
EPHA2
1199 Tamoxifen
No description has been provided for this image 
MALL
1200 Fulvestrant
No description has been provided for this image 
NT5C2
1200 Fulvestrant
No description has been provided for this image 
CDC42BPB
1200 Fulvestrant
No description has been provided for this image 
MYO5A
1200 Fulvestrant
No description has been provided for this image 
TNFRSF21
1200 Fulvestrant
No description has been provided for this image 
PRKCH
1200 Fulvestrant
No description has been provided for this image 
PCDH1
1200 Fulvestrant
No description has been provided for this image 
SCAMP4
1200 Fulvestrant
No description has been provided for this image 
MPZL2
1200 Fulvestrant
No description has been provided for this image 
TTC9
1200 Fulvestrant
No description has been provided for this image 
INAVA
1200 Fulvestrant
No description has been provided for this image 
EPHA2
1200 Fulvestrant
No description has been provided for this image 
MALL
1816 Fulvestrant
No description has been provided for this image 
NT5C2
1816 Fulvestrant
No description has been provided for this image 
CDC42BPB
1816 Fulvestrant
No description has been provided for this image 
MYO5A
1816 Fulvestrant
No description has been provided for this image 
TNFRSF21
1816 Fulvestrant
No description has been provided for this image 
PRKCH
1816 Fulvestrant
No description has been provided for this image 
PCDH1
1816 Fulvestrant
No description has been provided for this image 
SCAMP4
1816 Fulvestrant
No description has been provided for this image 
MPZL2
1816 Fulvestrant
No description has been provided for this image 
TTC9
1816 Fulvestrant
No description has been provided for this image 
INAVA
1816 Fulvestrant
No description has been provided for this image 
EPHA2
1816 Fulvestrant
No description has been provided for this image 
MALL
1372 Trametinib
No description has been provided for this image 
NT5C2
1372 Trametinib
No description has been provided for this image 
CDC42BPB
1372 Trametinib
No description has been provided for this image 
MYO5A
1372 Trametinib
No description has been provided for this image 
TNFRSF21
1372 Trametinib
No description has been provided for this image 
PRKCH
1372 Trametinib
No description has been provided for this image 
PCDH1
1372 Trametinib
No description has been provided for this image 
SCAMP4
1372 Trametinib
No description has been provided for this image 
MPZL2
1372 Trametinib
No description has been provided for this image 
TTC9
1372 Trametinib
No description has been provided for this image 
INAVA
1372 Trametinib
No description has been provided for this image 
EPHA2
1372 Trametinib
No description has been provided for this image

Figure 6¶

In [83]:
def ploting_log2fc_deseq2_mek(dds_res, genes, title, xlabel):
    tmp2 = dds_res.loc[genes,["log2FoldChange", "lfcSE", "padj"]].reset_index()
    tmp2 = tmp2.rename(columns={'gene_name':'MEK1/2'})
    fig, ax = plt.subplots(figsize=(6, 4))
    sns.barplot(data=tmp2, x='MEK1/2', y='log2FoldChange', ax=ax)
    ax.set_title(title)
    ax.set_xlabel(xlabel)
#     ax.set_ylim((-2,3))
    ax.bar_label(ax.containers[-1], labels=[f"padj:{p:.2e}" for p in tmp2['padj']], label_type='center', fontsize=6, padding=6)
    x_coords = [p.get_x() + 0.5 * p.get_width() for p in ax.patches]
    y_coords = [p.get_height() for p in ax.patches]
    ax.errorbar(x=x_coords, y=y_coords, yerr=tmp2["lfcSE"], fmt="none", c="k")

brca_mek_genes = ["MAP2K1", "MAP2K2", "GREB1", "TTC39A", "ANXA9", "MYB", "PGR"]
ov_mek_genes = ["MAP2K1", "MAP2K2", "NT5C2", "CDC42BPB", "MYO5A", "PRKCH", "PCDH1"]
ucec_mek_genes = ["MAP2K1", "MAP2K2", "TNFRSF21", "SCAMP4", "NT5C2", "MPZL2", "TTC9"]
cesc_mek_genes = ["MAP2K1", "MAP2K2", "MALL", "NT5C2", "INAVA", "CDC42BPB", "EPHA2"]
all_mek_genes = ["MAP2K1", "MAP2K2", "GREB1", "TTC39A", "ANXA9", "MYB", "PGR", "NT5C2", "CDC42BPB", "MYO5A", "PRKCH", "PCDH1", "TNFRSF21", "SCAMP4", "MPZL2", "TTC9", "MALL", "INAVA", "EPHA2"]
selected_mek_genes = ['MALL', 'TNFRSF21', 'EPHA2', 'PCDH1', 'MPZL2', 'INAVA']    
ploting_log2fc_deseq2_mek(brca_eeres_deseq2_res, selected_mek_genes, "BRCA", "")
ploting_log2fc_deseq2_mek(ov_eeres_deseq2_res, selected_mek_genes, "OV", "")
ploting_log2fc_deseq2_mek(ucec_eeres_deseq2_res, selected_mek_genes, "UCEC", "")
ploting_log2fc_deseq2_mek(cesc_eeres_deseq2_res, selected_mek_genes, "CESC", "")
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In [97]:
def ploting_log2fc_deseq2_mek(dds_res, genes, title, xlabel):
    tmp2 = dds_res.loc[genes,["log2FoldChange", "lfcSE", "padj"]].reset_index()
    tmp2 = tmp2.rename(columns={'gene_name':'MEK1/2'})
    fig, ax = plt.subplots(figsize=(6, 4))
    sns.barplot(data=tmp2, x='MEK1/2', y='log2FoldChange', ax=ax)
    ax.set_title(title)
    ax.set_xlabel(xlabel)
#     ax.set_ylim((-2,3))
    ax.bar_label(ax.containers[-1], labels=[f"padj:{p:.2e}" for p in tmp2['padj']], label_type='center', fontsize=6, padding=20)
    x_coords = [p.get_x() + 0.5 * p.get_width() for p in ax.patches]
    y_coords = [p.get_height() for p in ax.patches]
    ax.errorbar(x=x_coords, y=y_coords, yerr=tmp2["lfcSE"], fmt="none", c="k")
    
gene_related_to_er_and_mek = ["EGFR", "RAF1", "MAPK1", "MAP2K1"]
ploting_log2fc_deseq2_mek(brca_eeres_deseq2_res, gene_related_to_er_and_mek, "", "")
ploting_log2fc_deseq2_mek(ov_eeres_deseq2_res, gene_related_to_er_and_mek, "", "")
ploting_log2fc_deseq2_mek(ucec_eeres_deseq2_res, gene_related_to_er_and_mek, "", "")
ploting_log2fc_deseq2_mek(cesc_eeres_deseq2_res, gene_related_to_er_and_mek, "", "")
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In [52]:
def finding_best_score_for_survival(es_dfs_df, es_dss_df, es_df):
    bestes=None
    bestp=1
    sp = []

    for i in np.arange(es_df["EARLY"].quantile(0.1), es_df["EARLY"].quantile(0.9), 0.01):
        results_dfs = km.fit(es_dfs_df[f"DFS_MONTHS"], es_dfs_df[f"DFS_STATUS"], (es_dfs_df["EARLY"]>i).apply(lambda x: "High EERES" if x else "Low EERES"))
        results_dss = km.fit(es_dss_df[f"DSS_MONTHS"], es_dss_df[f"DSS_STATUS"], (es_dss_df["EARLY"]>i).apply(lambda x: "High EERES" if x else "Low EERES"))
        p = (results_dfs['logrank_P']+results_dss['logrank_P'])/2
        if p<bestp:
            bestes = i
            bestp = (results_dfs['logrank_P']+results_dss['logrank_P'])/2
    print("EERES Threshold", bestes)
    print("n", len(es_df))
    print("n >threshold", (es_df["EARLY"]>bestes).sum())
    print("n <=threshold", (es_df["EARLY"]<=bestes).sum())
    results = km.fit(es_dfs_df[f"DFS_MONTHS"], es_dfs_df[f"DFS_STATUS"], (es_dfs_df["EARLY"]>bestes).apply(lambda x: "High EERES" if x else "Low EESRS"))
    km.plot(results, full_ylim=True, y_percentage=True, fontsize=15)
    plt.show()
    results = km.fit(es_dss_df[f"DSS_MONTHS"], es_dss_df[f"DSS_STATUS"], (es_dss_df["EARLY"]>bestes).apply(lambda x: "High EERES" if x else "Low EESRS"))
    km.plot(results, full_ylim=True, y_percentage=True, fontsize=15)
    plt.show()
    return bestes

def roc_curve_testing(X, y, title, genes):
    from sklearn.preprocessing import StandardScaler
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
    print(len(y_test))
    clf = LogisticRegression(random_state=0).fit(X_train, y_train)
    print(pd.DataFrame({"Gene":selected_mek_genes, "Coef":clf.coef_[0]}))
    test_score = clf.predict_proba(X_test)
    test_acc = clf.score(X_test, y_test)
    import matplotlib.pyplot as plt
    from sklearn.metrics import RocCurveDisplay
    

    RocCurveDisplay.from_predictions(
        y_test,
        test_score[:,1],
        name="Low EERES vs High EERES",
        color="darkorange",
        pos_label=1
    )
    plt.plot([0, 1], [0, 1], "k--", label="chance level (AUC = 0.5)")
    plt.axis("square")
    plt.xlabel("False Positive Rate")
    plt.ylabel("True Positive Rate")
    plt.title(title)
    plt.legend()
    plt.show()
    
selected_mek_genes = ['MALL', 'TNFRSF21', 'EPHA2', 'PCDH1', 'MPZL2', 'INAVA']
all_mek_genes = set(brca_mek_genes+ov_mek_genes+ucec_mek_genes+cesc_mek_genes)

brca_mek_y = pd.DataFrame(brca_pheno_bestes, index=brca_es.iloc[brca_sortq].index, columns=["EERES"])
brca_mek_X = brca_gdc_tpm.iloc[:,brca_sortq].loc[selected_mek_genes].transpose()
roc_curve_testing(brca_mek_X, brca_mek_y.applymap(lambda x: 0 if x=="Low EERES" else 1), "BRCA", selected_mek_genes)

ov_mek_y = pd.DataFrame(ov_pheno_bestes, index=ov_es.iloc[ov_sortq].index, columns=["EERES"])
ov_mek_X = ov_gdc_tpm.iloc[:,ov_sortq].loc[selected_mek_genes].transpose()
roc_curve_testing(ov_mek_X, ov_mek_y.applymap(lambda x: 0 if x=="Low EERES" else 1), "OV", selected_mek_genes)

ucec_mek_y = pd.DataFrame(ucec_pheno_bestes, index=ucec_es.iloc[ucec_sortq].index, columns=["EERES"])
ucec_mek_X = ucec_gdc_tpm.iloc[:,ucec_sortq].loc[selected_mek_genes].transpose()
roc_curve_testing(ucec_mek_X, ucec_mek_y.applymap(lambda x: 0 if x=="Low EERES" else 1), "UCEC", selected_mek_genes)

cesc_mek_y = pd.DataFrame(cesc_pheno_bestes, index=cesc_es.iloc[cesc_sortq].index, columns=["EERES"])
cesc_mek_X = cesc_gdc_tpm.iloc[:,cesc_sortq].loc[selected_mek_genes].transpose()
roc_curve_testing(cesc_mek_X, cesc_mek_y.applymap(lambda x: 0 if x=="Low EERES" else 1), "CESC", selected_mek_genes)

219
       Gene      Coef
0      MALL -0.092818
1  TNFRSF21 -0.008771
2     EPHA2 -0.006848
3     PCDH1  0.029096
4     MPZL2 -0.003317
5     INAVA -0.036396

/home/oscar/.local/lib/python3.10/site-packages/sklearn/utils/validation.py:1141: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
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76
       Gene      Coef
0      MALL  0.075162
1  TNFRSF21  0.009379
2     EPHA2 -0.000068
3     PCDH1  0.019487
4     MPZL2  0.027253
5     INAVA  0.010130

/home/oscar/.local/lib/python3.10/site-packages/sklearn/utils/validation.py:1141: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
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112
       Gene      Coef
0      MALL  0.068889
1  TNFRSF21  0.011314
2     EPHA2  0.000472
3     PCDH1  0.017722
4     MPZL2  0.014948
5     INAVA -0.011358

/home/oscar/.local/lib/python3.10/site-packages/sklearn/utils/validation.py:1141: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
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61
       Gene      Coef
0      MALL  0.151171
1  TNFRSF21  0.009883
2     EPHA2  0.003833
3     PCDH1  0.002883
4     MPZL2 -0.000882
5     INAVA  0.020528

/home/oscar/.local/lib/python3.10/site-packages/sklearn/utils/validation.py:1141: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
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