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","Prefrontal Cortex","log.tdata.FPKM.sample.info.subset.prefrontal.cortex.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","Prefrontal Cortex","log.tdata.FPKM.sample.info.subset.prefrontal.cortex.WGCNA.module.membership.csv"), header=T)

Data_setup(WGCNA, file = "Annotated_genes_in_prefrontal_cortex_WGCNA_Chang_B6_96hr_4wk.RData", folder="Prefrontal Cortex")

Matched<-readRDS(here("Data","Prefrontal Cortex","Annotated_genes_in_prefrontal_cortex_WGCNA_Chang_B6_96hr_4wk.RData"))
  
pathways(Matched, pathwayfile = "Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_prefrontal_cortex_WGCNA.RData", genefile = "Chang_B6_96hr_4wk_gprofiler_gene_annotation_list_prefrontal_cortex_WGCNA.RData", folder = "Prefrontal Cortex")
  
WGCNA.pathway <-readRDS(here("Data","Prefrontal Cortex","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_prefrontal_cortex_WGCNA.RData"))

antiquewhite

aquamarine

bisque2

black

blanchedalmond

blue4

blueviolet

burlywood

chocolate2

chocolate4

cornsilk

cornsilk2

darkolivegreen2

darkolivegreen4

darkseagreen4

darkturquoise

dodgerblue4

goldenrod3

grey

hotpink3

khaki3

lavenderblush2

lightblue

lightgoldenrod3

lightpink1

lightskyblue

lightskyblue2

limegreen

mediumorchid1

mistyrose

oldlace

pink

plum

rosybrown2

salmon

Table of Pathways

WGCNA.pathway <- readRDS(here("Data","Prefrontal Cortex","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_prefrontal_cortex_WGCNA.RData"))

Antiquewhite

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

[1] “No pathways significantly overrepresented”

Aquamarine

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

Bisque2

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

Black

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

Blanchedalmond

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

Blue4

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

[1] “No pathways significantly overrepresented”

Blueviolet

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

Burlywood

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

[1] “No pathways significantly overrepresented”

Chocolate2

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

[1] “No pathways significantly overrepresented”

Chocolate4

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

Cornsilk

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

Cornsilk2

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

Darkolivegreen2

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

Darkolivegreen4

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

Darkseagreen4

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

Darkturquoise

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

Dodgerblue4

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

Goldenrod3

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

[1] “No pathways significantly overrepresented”

Grey

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

Hotpink3

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

Khaki3

if(class(WGCNA.pathway[[21]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[21]][c(11,3:6)])}

Lavenderblush2

if(class(WGCNA.pathway[[22]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[22]][c(11,3:6)])}

Lightblue

if(class(WGCNA.pathway[[23]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[23]][c(11,3:6)])}

lightgoldenrod3

if(class(WGCNA.pathway[[24]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[24]][c(11,3:6)])}

Lightpink1

if(class(WGCNA.pathway[[25]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[25]][c(11,3:6)])}

Lightskyblue

if(class(WGCNA.pathway[[26]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[26]][c(11,3:6)])}

Lightskyblue2

if(class(WGCNA.pathway[[27]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[27]][c(11,3:6)])}

[1] “No pathways significantly overrepresented”

Limegreen

if(class(WGCNA.pathway[[28]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[28]][c(11,3:6)])}

[1] “No pathways significantly overrepresented”

Mediumorchid1

if(class(WGCNA.pathway[[29]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[29]][c(11,3:6)])}

Mistyrose

if(class(WGCNA.pathway[[30]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[30]][c(11,3:6)])}

[1] “No pathways significantly overrepresented”

Oldlace

if(class(WGCNA.pathway[[32]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[32]][c(11,3:6)])}

[1] “No pathways significantly overrepresented”

Pink

if(class(WGCNA.pathway[[33]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[33]][c(11,3:6)])}

Plum

if(class(WGCNA.pathway[[34]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[34]][c(11,3:6)])}

[1] “No pathways significantly overrepresented”

Rosybrown2

if(class(WGCNA.pathway[[35]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[35]][c(11,3:6)])}

Salmon

if(class(WGCNA.pathway[[36]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[36]][c(11,3:6)])}

Pathway Frequency

WGCNA.pathway <- readRDS(here("Data","Prefrontal Cortex","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_prefrontal_cortex_WGCNA.RData"))

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

[1] 778 [1] 566

#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