Introduction and Data files

This dataset contains nine tissues (heart, hippocampus, hypothalamus, kidney, liver, prefrontal cortex, skeletal muscle, small intestine, and spleen) from C57BL/6J mice that were fed 2-deoxyglucose (6g/L) through their drinking water for 96hrs or 4wks. 96hr mice were given their 2DG treatment 2 weeks after the other cohort started the 4 week treatment. The organs from the mice were harvested and processed for metabolomics and transcriptomics. The data in this document pertains to the transcriptomics data only. The counts that were used were FPKM normalized before being log transformed. It was determined that sample A113 had low RNAseq quality and through further analyses with PCA, MA plots, and clustering was an outlier and will be removed for the rest of the analyses performed. This document will determine the contribution of each main effect or their combination to each module identified, as well as the relationship of each gene within the module and its significance.

needed.packages <- c("tidyverse", "here", "functional", "gplots", "dplyr", "GeneOverlap", "R.utils", "reshape2","magrittr","data.table", "RColorBrewer","preprocessCore", "ARTool","emmeans", "phia", "gProfileR", "WGCNA","plotly", "pheatmap","ppcor", "pander","downloadthis")
for(i in 1:length(needed.packages)){library(needed.packages[i], character.only = TRUE)}

source(here("source_files","WGCNA_source.R"))
source(here("source_files","WGCNA_contribution_source.R"))
tdata.FPKM.sample.info <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_phenotypes.RData"))

tdata.FPKM <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_numeric.RData"))

log.tdata.FPKM <- log(tdata.FPKM + 1)
log.tdata.FPKM <- as.data.frame(log.tdata.FPKM)

log.tdata.FPKM.sample.info <- cbind(log.tdata.FPKM, tdata.FPKM.sample.info[,27238:27240])

log.tdata.FPKM.sample.info <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(rowname != "A113") %>% column_to_rownames()

log.tdata.FPKM.subset <- log.tdata.FPKM[,colMeans(log.tdata.FPKM != 0) > 0.5] 

log.tdata.FPKM.subset <- log.tdata.FPKM.subset %>% rownames_to_column() %>% filter(rowname != "A113") %>% column_to_rownames()

log.tdata.FPKM.sample.info.subset.kidney <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue == "Kidney") %>% column_to_rownames()

log.tdata.FPKM.subset <- subset(log.tdata.FPKM.sample.info.subset.kidney, select = -c(Time,Treatment,Tissue))
  
WGCNA.pathway <-readRDS(here("Data","Kidney","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_kidney_WGCNA.RData"))

Matched<-readRDS(here("Data","Kidney","Annotated_genes_in_kidney_WGCNA_Chang_B6_96hr_4wk.RData"))


module.names <- Matched$X..Module.
name <- str_split(module.names,"_")
samples <-c()
for(i in 1:length(name)){
samples[[i]] <- name[[i]][2]
}  
name <- str_split(samples,"\"")
name <- unlist(name)

Treatment <- unclass(as.factor(log.tdata.FPKM.sample.info.subset.kidney[,27238]))
Time <- unclass(as.factor(log.tdata.FPKM.sample.info.subset.kidney[,27237]))
Treat.Time <- paste0(Treatment, Time)
phenotype <- data.frame(cbind(Treatment, Time, Treat.Time))

nSamples <- nrow(log.tdata.FPKM.sample.info.subset.kidney)

MEs0 <- read.csv(here("Data","Kidney","log.tdata.FPKM.sample.info.subset.kidney.WGCNA.module.eigens.csv"),header = T, row.names = 1)
name <- str_split(names(MEs0),"_")
samples <-c()
for(i in 1:length(name)){
samples[[i]] <- name[[i]][2]
}  
name <- str_split(samples,"\"")
name <- unlist(name)
colnames(MEs0) <-name
MEs <- orderMEs(MEs0)
moduleTraitCor <- cor(MEs, phenotype, use = "p");
moduleTraitPvalue <- corPvalueStudent(moduleTraitCor, nSamples)

Relationship between Modules and Traits

#sizeGrWindow(10,6)
# Will display correlations and their p-values
textMatrix = paste(signif(moduleTraitCor, 2), "\n(",
                        signif(moduleTraitPvalue, 1), ")", sep = "");
dim(textMatrix) = dim(moduleTraitCor)

# Display the correlation values within a heatmap plot
heat <- pheatmap(moduleTraitCor, main = paste("Module-trait relationships"), display_numbers = textMatrix,color=colorRampPalette(brewer.pal(n = 12, name = "Paired"))(10), cluster_rows = F, cluster_cols = F, fontsize_number = 4, angle_col = 45, number_color = "black", border_color = "white")
heat

DT::datatable(moduleTraitPvalue, extensions = 'Buttons',
                  rownames = TRUE, 
                  filter="top",
                  options = list(dom = 'Blfrtip',
                                 buttons = c('copy', 'csv', 'excel'),
                                 lengthMenu = list(c(10,25,50,-1),
                                                   c(10,25,50,"All")), 
                                 scrollX= TRUE), class = "display")

Partial Correlation

phenotype$Treatment <- as.numeric(phenotype$Treatment)
phenotype$Time <- as.numeric(phenotype$Time)
phenotype$Treat.Time <- as.numeric(phenotype$Treat.Time)

Sigmodules <- as.data.frame(cbind(MEs$purple,MEs$lightcyan,MEs$blue))
colnames(Sigmodules) <- c("purple","lightcyan","blue")

for(i in 1:length(Sigmodules)){
   cat("\n##",colnames(Sigmodules[i]),"{.tabset .tabset-fade .tabset-pills}","\n")
   for(j in 1:length(phenotype)){
      cat("\n###", colnames(phenotype[j]),"{.tabset .tabset-fade .tabset-pills}","\n")
      for(k in 1:length(phenotype)){
         cat("\n####", "Partial Correlation", colnames(phenotype[k]),"\n")
         partial <- pcor.test(Sigmodules[,i], phenotype[,j],phenotype[,k])
         panderOptions('knitr.auto.asis', FALSE)
         print(pander(partial))
         cat("\n \n")
      }
      cat("\n \n")
   }
   cat("\n \n")
}

purple

Treatment

Partial Correlation Treatment

estimate p.value statistic n gp Method
-0.5274 0.04334 -2.238 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
-0.5343 0.04018 -2.279 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
-0.2464 0.3759 -0.9169 16 1 pearson

NULL

Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.1889 0.5002 0.6935 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
0.1605 0.5678 0.5862 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
0.2464 0.3759 0.9169 16 1 pearson

NULL

Treat.Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.1889 0.5002 0.6935 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
-0.5343 0.04018 -2.279 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
-0.5088 0.05274 -2.131 16 1 pearson

NULL

lightcyan

Treatment

Partial Correlation Treatment

estimate p.value statistic n gp Method
-0.5205 0.0467 -2.198 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
-0.5499 0.0337 -2.374 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
-0.4266 0.1128 -1.701 16 1 pearson

NULL

Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.3779 0.1649 1.472 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
0.3227 0.2407 1.229 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
0.4266 0.1128 1.701 16 1 pearson

NULL

Treat.Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.3779 0.1649 1.472 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
-0.5499 0.0337 -2.374 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
-0.4858 0.06639 -2.004 16 1 pearson

NULL

blue

Treatment

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.2687 0.3328 1.006 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
0.3009 0.2758 1.138 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
0.4866 0.06584 2.008 16 1 pearson

NULL

Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
-0.4671 0.07918 -1.905 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
-0.4499 0.09243 -1.816 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
-0.4866 0.06584 -2.008 16 1 pearson

NULL

Treat.Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
-0.4671 0.07918 -1.905 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
0.3009 0.2758 1.138 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
0.2226 0.4251 0.8234 16 1 pearson

NULL

Scatterplots Module membership vs. Gene significance

Time

time.contribution(phenotype, log.tdata.FPKM.subset, MEs)

lightyellow

magenta

darkred

darkturquoise

cyan

blue

darkgreen

darkgrey

salmon

pink

purple

red

lightcyan

grey60

lightgreen

midnightblue

greenyellow

Treatment

treat.contribution(phenotype, log.tdata.FPKM.subset, MEs)

lightyellow

magenta

darkred

darkturquoise

cyan

blue

darkgreen

darkgrey

salmon

pink

purple

red

lightcyan

grey60

lightgreen

midnightblue

greenyellow

Treatment by Time

treat.time.contribution(phenotype, log.tdata.FPKM.subset, MEs)

lightyellow

magenta

darkred

darkturquoise

cyan

blue

darkgreen

darkgrey

salmon

pink

purple

red

lightcyan

grey60

lightgreen

midnightblue

greenyellow

Analysis performed by Ann Wells

The Carter Lab The Jackson Laboratory 2023

ann.wells@jax.org