Introduction

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 which pathways are significantly altered by each module.

needed.packages <- c("tidyverse", "here", "functional", "gplots", "dplyr", "GeneOverlap", "R.utils", "reshape2","magrittr","data.table", "RColorBrewer","preprocessCore", "ARTool","emmeans", "phia", "gprofiler2","rlist", "plotly","downloadthis")
for(i in 1:length(needed.packages)){library(needed.packages[i], character.only = TRUE)}

source(here("source_files","WGCNA_source.R"))

Module Bar plot

This bar plot shows the number of genes in each module.

modules<-read.csv(here("Data","log.tdata.FPKM.sample.info.subset.WGCNA.module.membership.csv"), header=T)

module_barplot(modules)

Pathway Analysis

Pathway analysis was performed using the gprofiler package. Genes associated with each module were compared against KEGG and REACTOME databases. Modules that did not contain any significant pathways are blank.

Pathway plots

WGCNA<-read.table(here("Data","log.tdata.FPKM.sample.info.subset.WGCNA.module.membership.csv"), header=T)

Data_setup(WGCNA, file = "Annotated_genes_in_WGCNA_Chang_B6_96hr_4wk.RData", folder="")

Matched<-readRDS(here("Data","Annotated_genes_in_WGCNA_Chang_B6_96hr_4wk.RData"))
  
pathways(Matched, pathwayfile = "Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_WGCNA.RData", genefile = "Chang_B6_96hr_4wk_gprofiler_gene_annotation_list_WGCNA.RData", folder = "")
  
WGCNA.pathway <-readRDS(here("Data","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_WGCNA.RData"))

black

darkgreen

darkolivegreen

darkorange2

floralwhite

grey

ivory

lightcyan

lightcyan1

lightsteelblue1

lightyellow

mediumpurple3

midnightblue

orangered4

pink

royalblue

skyblue

skyblue3

turquoise

violet

Table of Pathways

WGCNA.pathway <- readRDS(here("Data","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_WGCNA.RData"))

Black

if(class(WGCNA.pathway[[1]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[1]][c(11,3:6)])}

Darkgreen

if(class(WGCNA.pathway[[2]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[2]][c(11,3:6)])}

Darkolivegreen

if(class(WGCNA.pathway[[3]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[3]][c(11,3:6)])}

Darkorange2

if(class(WGCNA.pathway[[4]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[4]][c(11,3:6)])}

Floralwhite

if(class(WGCNA.pathway[[5]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[5]][c(11,3:6)])}

Grey

if(class(WGCNA.pathway[[6]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[6]][c(11,3:6)])}

Ivory

if(class(WGCNA.pathway[[7]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[7]][c(11,3:6)])}

[1] “No pathways significantly overrepresented”

Lightcyan

if(class(WGCNA.pathway[[8]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[8]][c(11,3:6)])}

Lightcyan1

if(class(WGCNA.pathway[[9]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[9]][c(11,3:6)])}

Lightsteelblue1

if(class(WGCNA.pathway[[10]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[10]][c(11,3:6)])}

lightyellow

if(class(WGCNA.pathway[[11]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[11]][c(11,3:6)])}

Mediumpurple3

if(class(WGCNA.pathway[[12]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[12]][c(11,3:6)])}

Midnightblue

if(class(WGCNA.pathway[[13]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[13]][c(11,3:6)])}

Orangered4

if(class(WGCNA.pathway[[14]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[14]][c(11,3:6)])}

Pink

if(class(WGCNA.pathway[[15]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[15]][c(11,3:6)])}

Royalblue

if(class(WGCNA.pathway[[16]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[16]][c(11,3:6)])}

Skyblue

if(class(WGCNA.pathway[[17]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[17]][c(11,3:6)])}

Skyblue3

if(class(WGCNA.pathway[[18]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[18]][c(11,3:6)])}

Turquoise

if(class(WGCNA.pathway[[19]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[19]][c(11,3:6)])}

Violet

if(class(WGCNA.pathway[[20]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[20]][c(11,3:6)])}

Pathway Frequency

WGCNA.pathway <- readRDS(here("Data","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_WGCNA.RData"))

#pdf("Counts_of_each_pathway_identified_within_all_data.pdf")
count <- count.pathways(WGCNA.pathway)

[1] 1256 [1] 984

#dev.off()

Frequency Table

DT.table.freq(count)

Frequency Plot

  #pdf("Counts_of_each_pathway_identified_within_jaccard_index.pdf")
  p <- ggplot(data=count,aes(x=list.pathways,y=Freq))
  p <- p + geom_bar(color="black", fill=colorRampPalette(brewer.pal(n = 12, name = "Paired"))(length(count[,1])), stat="identity",position="identity") + theme_classic()
  p <- p + theme(axis.text.x = element_text(angle = 90, hjust =1, size = 3)) + scale_x_discrete(labels=count$list.pathways) + xlab("Modules")
  print(p)

  #dev.off()

Analysis performed by Ann Wells

The Carter Lab The Jackson Laboratory 2023

ann.wells@jax.org