Progress report 1

Preamble

• Load Metadata
  • Inspect the metadata column names.
  • Remove number sign at the name of column one.
  • Note that Var1 starts at index 7.
  • Select important variables for downstream analysis.
  • Define each categoric variable as factor.

# Load samplemetadata
samplemetadata <- readr::read_table2(file="~/OneDrive/CaseStudy/iMAPpipeline_rev2/iMAPdemo/iMAP/resources/metadata/samplemetadata.tsv", col_names = T)
colnames(samplemetadata)[1] <- "SampleID"

# View column names
names(samplemetadata)

 [1] "SampleID"              "BarcodeSequence"      
 [3] "ForwardPrimerSequence" "ReversePrimerSequence"
 [5] "ForwardRead"           "ReverseRead"          
 [7] "Var1"                  "Var2"                 
 [9] "Var3"                  "Var4"                 
[11] "Description"          

# Define categoric variables like
samplemetadata$SampleID <- as.factor(samplemetadata$SampleID)
samplemetadata$Var1 <- as.factor(samplemetadata$Var1)
samplemetadata$Var2 <- as.factor(samplemetadata$Var2)
samplemetadata$Var3 <- as.factor(samplemetadata$Var3)
samplemetadata$Var4 <- as.factor(samplemetadata$Var4)

# Add more variables as needed

library(dplyr)
library(knitr)

# show data
metadatastatus %>% knitr::kable() 

library(dplyr)
library(knitr)

# show variable status
varstatus %>% knitr::kable() 

View variable levels (first 5 rows)

variables %>% head(5)  %>% knitr::kable() 

Frequency of experimental variables

library(ggplot2)

# Var1
var1histplot <- ggpubr::gghistogram(variables, x = "Var1", stat="count", fill = "#ff0000") + 
  labs(x = "Variable 1", y = "Sample count") 
  
# Var2
var2histplot <- ggpubr::gghistogram(variables, x = "Var2", stat="count", fill = "#00ff00") + 
  labs(x = "Variable 2", y = "Sample count") 

# Var3
var3histplot <- ggpubr::gghistogram(variables, x = "Var3", stat="count", fill = "#0000ff") + 
  theme(axis.text.x=element_text(angle = 0, hjust = 0))+ scale_y_continuous(labels=scaleFUN) +
  labs(x = "Variable 3", y = "Sample count") 

ggpubr::ggarrange(var1histplot,var2histplot, var3histplot, ncol = 1, nrow = 3, align = "hv", labels = c("AUTO"))

library("dplyr")
library("knitr")

# Load samplemetadata

samplemetadata2 <- readr::read_table2(file="~/OneDrive/CaseStudy/iMAPpipeline_rev2/iMAPdemo/iMAP/resources/metadata/samplemetadata_casestudy.tsv", col_names = T)
samplemetadata2 <- samplemetadata2[, -7]
colnames(samplemetadata2)[1] <- "SampleID"

# View column names
names(samplemetadata2)

 [1] "SampleID"              "BarcodeSequence"      
 [3] "ForwardPrimerSequence" "ReversePrimerSequence"
 [5] "ForwardRead"           "ReverseRead"          
 [7] "Sex"                   "Time"                 
 [9] "DayID"                 "DPW"                  
[11] "Description"          

# Define categoric variables like
samplemetadata2$SampleID <- as.factor(samplemetadata2$SampleID)
samplemetadata2$Sex <- as.factor(samplemetadata2$Sex)
samplemetadata2$Time <- as.factor(samplemetadata2$Time)
samplemetadata2$DayID <- as.factor(samplemetadata2$DayID)
samplemetadata2$DPW <- as.factor(samplemetadata2$DPW)

# Add more variables as needed

variables2 <- samplemetadata2  %>% dplyr::select("SampleID", "Sex", "Time", "DayID", "DPW" )

Status of custom metadata

library(dplyr)
library(knitr)

# show data
varstatus2 %>% knitr::kable() 

Key: q_zeros: quantity of missing data; p_zeros: percentage of missing data, q_na: quantity of NA; p_na: percentage of NA, q_inf: quantity of infinite values; p_inf: percentage of infinity values, type: factor, character, integer or numeric; unique: levels of the variable.

When reviewing metadata status you may notice that numeric variables are confused with numeric data. For example, variable DPW (days post weaning) on Table x shows 12 missing values (3.33%) which is incorrect. As investigators we know that day 1 was coded zero (0) (12 zeros for 12 mouse) which in descriptive statistics is interpreted as missing data. To correct this we re-coded the samples with unique identifies shown as DayID to distinguish them from integers. However, depending of experiment this kind of variables need to be converted to character during analysis.

Frequency of experimental variables in the Demo dataset

library(ggplot2)
#sort by dpw (descending)

sexhistplot <- ggpubr::gghistogram(variables2, x = "Sex", stat="count", fill = "#ff0000") + 
  labs(x = "Sex of animal", title = "Sex variable")

timehistplot <- ggpubr::gghistogram(variables2, x = "Time", stat="count", fill = "#00ff00") + 
  labs(x = "Time of Weaning", title = "Time variable")

dpwhistplot <- ggpubr::gghistogram(variables2, x = "DayID", stat="count", fill = "#0000ff") +
  theme(axis.text.x=element_text(angle = 90, hjust = 0))+ scale_y_continuous(labels=scaleFUN) + 
  labs(x = "Day Code", title = "Sex variable")

ggpubr::ggarrange(sexhistplot,timehistplot, dpwhistplot, ncol = 1, nrow = 3,  common.legend = TRUE, legend = "top", align = "hv", labels = c(""))

save(samplemetadata, samplemetadata2, variables, variables2, file = "~/OneDrive/CaseStudy/iMAPpipeline_rev2/iMAPdemo/iMAP/code/Rmd/metadatavars.RData")
save(samplemetadata, samplemetadata2, variables, variables2, file = "~/GitHub/MAY2019/iMAP/code/Rmd/metadatavars.RData")

Posible questions

Summary of packages used in the analysis

sessionInfo()

R version 3.5.2 (2018-12-20)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.4

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] ggplot2_3.1.0 knitr_1.22    dplyr_0.8.0.1

loaded via a namespace (and not attached):
  [1] nlme_3.1-137        bitops_1.0-6        phyloseq_1.26.1    
  [4] RColorBrewer_1.1-2  tools_3.5.2         backports_1.1.3    
  [7] R6_2.4.0            vegan_2.5-4         KernSmooth_2.23-15 
 [10] rpart_4.1-13        Hmisc_4.2-0         lazyeval_0.2.1     
 [13] BiocGenerics_0.28.0 mgcv_1.8-27         colorspace_1.4-1   
 [16] permute_0.9-4       ade4_1.7-13         nnet_7.3-12        
 [19] withr_2.1.2         tidyselect_0.2.5    gridExtra_2.3      
 [22] moments_0.14        compiler_3.5.2      microbiome_1.4.2   
 [25] Biobase_2.42.0      htmlTable_1.13.1    iNEXT_2.0.19       
 [28] xml2_1.2.0          NLP_0.2-0           labeling_0.3       
 [31] entropy_1.2.1       slam_0.1-45         caTools_1.17.1.1   
 [34] scales_1.0.0        checkmate_1.9.1     tm_0.7-6           
 [37] readr_1.3.1         stringr_1.4.0       digest_0.6.18      
 [40] foreign_0.8-71      rmarkdown_1.12      XVector_0.22.0     
 [43] base64enc_0.1-3     pkgconfig_2.0.2     htmltools_0.3.6    
 [46] highr_0.8           htmlwidgets_1.3     rlang_0.3.4        
 [49] rstudioapi_0.10     jsonlite_1.6        gtools_3.8.1       
 [52] acepack_1.4.1       magrittr_1.5        Formula_1.2-3      
 [55] biomformat_1.10.1   Matrix_1.2-15       Rcpp_1.0.1         
 [58] munsell_0.5.0       S4Vectors_0.20.1    Rhdf5lib_1.4.3     
 [61] ape_5.2             stringi_1.4.3       yaml_2.2.0         
 [64] MASS_7.3-51.1       zlibbioc_1.28.0     rhdf5_2.26.2       
 [67] gplots_3.0.1.1      plyr_1.8.4          grid_3.5.2         
 [70] parallel_3.5.2      gdata_2.18.0        crayon_1.3.4       
 [73] lattice_0.20-38     cowplot_0.9.4       Biostrings_2.50.2  
 [76] splines_3.5.2       pander_0.6.3        multtest_2.38.0    
 [79] hms_0.4.2           funModeling_1.7     pillar_1.3.1       
 [82] igraph_1.2.4        ggpubr_0.2          reshape2_1.4.3     
 [85] codetools_0.2-16    stats4_3.5.2        glue_1.3.1         
 [88] evaluate_0.13       latticeExtra_0.6-28 data.table_1.12.0  
 [91] foreach_1.4.4       gtable_0.2.0        purrr_0.3.2        
 [94] tidyr_0.8.3         assertthat_0.2.1    xfun_0.6           
 [97] survival_2.43-3     tibble_2.1.1        iterators_1.0.10   
[100] IRanges_2.16.0      cluster_2.0.7-1     ROCR_1.0-7