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","Skeletal Muscle","log.tdata.FPKM.sample.info.subset.skeletal.muscle.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","Skeletal Muscle","log.tdata.FPKM.sample.info.subset.skeletal.muscle.WGCNA.module.membership.csv"), header=T)

Data_setup(WGCNA, file = "Annotated_genes_in_skeletal_muscle_WGCNA_Chang_B6_96hr_4wk.RData", folder="Skeletal Muscle")

Matched<-readRDS(here("Data","Skeletal Muscle","Annotated_genes_in_skeletal_muscle_WGCNA_Chang_B6_96hr_4wk.RData"))
  
pathways(Matched, pathwayfile = "Chang_B6_96hr_4wk_gprofiler_gprofiler_pathway_annotation_list_skeletal_muscle_WGCNA.RData", genefile = "Chang_B6_96hr_4wk_gprofiler_gene_annotation_list_skeletal_muscle_WGCNA.RData", folder = "Skeletal Muscle")
  
WGCNA.pathway <-readRDS(here("Data","Skeletal Muscle","Chang_B6_96hr_4wk_gprofiler_gprofiler_pathway_annotation_list_skeletal_muscle_WGCNA.RData"))

antiquewhite2

bisque4

brown2

brown4

coral

coral2

coral3

darkgrey

darkseagreen3

darkslateblue

firebrick4

floralwhite

green

grey

grey60

honeydew

honeydew1

lavenderblush2

lightcoral

lightcyan1

lightpink3

lightpink4

lightsteelblue1

magenta4

mediumpurple3

orangered1

orangered3

orangered4

palevioletred2

plum3

purple

saddlebrown

salmon2

sienna4

skyblue4

thistle1

turquoise

yellow4

Table of Pathways

WGCNA.pathway <- readRDS(here("Data","Skeletal Muscle","Chang_B6_96hr_4wk_gprofiler_gprofiler_pathway_annotation_list_skeletal_muscle_WGCNA.RData"))

Antiquewhite2

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

Bisque4

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

Brown2

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

Brown4

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

Coral

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

[1] “No pathways significantly overrepresented”

Coral2

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

Coral3

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

Darkgrey

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

Darkseagreen3

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

Darkslateblue

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

[1] “No pathways significantly overrepresented”

Firebrick4

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

Floralwhite

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

[1] “No pathways significantly overrepresented”

Green

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

Grey

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

[1] “No pathways significantly overrepresented”

Grey60

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

Honeydew

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

[1] “No pathways significantly overrepresented”

Honeydew1

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

Lavenderblush2

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”

Lightcoral

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

[1] “No pathways significantly overrepresented”

Lightcyan1

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

Lightpink3

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

[1] “No pathways significantly overrepresented”

Lightpink4

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

[1] “No pathways significantly overrepresented”

lightsteelblue1

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

Magenta4

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

Mediumpurple3

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

Orangered1

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”

Orangered3

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

[1] “No pathways significantly overrepresented”

Orangered4

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

Palevioletred2

if(class(WGCNA.pathway[[31]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[31]][c(11,3:6)])}

[1] “No pathways significantly overrepresented”

Plum3

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

Purple

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

Saddlebrown

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”

Salmon2

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

Sienna4

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

Skyblue4

if(class(WGCNA.pathway[[37]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[37]][c(11,3:6)])}

[1] “No pathways significantly overrepresented”

Thistle1

if(class(WGCNA.pathway[[38]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[38]][c(11,3:6)])}

Turquoise

if(class(WGCNA.pathway[[39]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[39]][c(11,3:6)])}

Yellow4

if(class(WGCNA.pathway[[40]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[40]][c(11,3:6)])}

[1] “No pathways significantly overrepresented”

Pathway Frequency

WGCNA.pathway <- readRDS(here("Data","Skeletal Muscle","Chang_B6_96hr_4wk_gprofiler_gprofiler_pathway_annotation_list_skeletal_muscle_WGCNA.RData"))

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

[1] 823 [1] 601

#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