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","Small Intestine","log.tdata.FPKM.sample.info.subset.small.intestine.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","Small Intestine","log.tdata.FPKM.sample.info.subset.small.intestine.WGCNA.module.membership.csv"), header=T)

Data_setup(WGCNA, file = "Annotated_genes_in_small_intestine_WGCNA_Chang_B6_96hr_4wk.RData", folder="Small Intestine")

Matched<-readRDS(here("Data","Small Intestine","Annotated_genes_in_small_intestine_WGCNA_Chang_B6_96hr_4wk.RData"))
  
pathways(Matched, pathwayfile = "Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_small_intestine_WGCNA.RData", genefile = "Chang_B6_96hr_4wk_gprofiler_gene_annotation_list_small_intestine_WGCNA.RData", folder = "Small Intestine")
  
WGCNA.pathway <-readRDS(here("Data","Small Intestine","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_small_intestine_WGCNA.RData"))

aliceblue

antiquewhite

antiquewhite1

antiquewhite2

antiquewhite4

blue1

blue2

blue3

burlywood

chocolate3

coral1

coral4

cornsilk

darkgoldenrod4

deepskyblue

firebrick

firebrick3

green4

grey

hotpink4

indianred

lightblue

lightblue2

lightblue4

lightgreen

lightskyblue2

linen

magenta2

magenta3

mediumpurple1

mediumpurple3

mistyrose

moccasin

oldlace

orange

pink2

red4

rosybrown3

rosybrown4

royalblue

sienna1

sienna3

slateblue2

thistle2

white

yellow4

Table of Pathways

WGCNA.pathway <- readRDS(here("Data","Small Intestine","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_small_intestine_WGCNA.RData"))

Aliceblue

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

Antiquewhite

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

Antiquewhite1

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

Antiquewhite2

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

Antiquewhite4

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

Blue1

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

Blue2

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

Blue3

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

Burlywood

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

Chocolate3

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

Coral1

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

Coral4

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

Cornsilk

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

Darkgoldenrod4

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”

Deepsklyblue

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

[1] “No pathways significantly overrepresented”

Firebrick

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

Firebrick3

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

[1] “No pathways significantly overrepresented”

Green4

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

Grey

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”

Hotpink4

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

Indianred

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

Lightblue

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

Lightblue2

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

Lightblue4

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

[1] “No pathways significantly overrepresented”

Lightgreen

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

Lightskyblue2

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

[1] “No pathways significantly overrepresented”

Linen

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”

Magenta2

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”

Magenta3

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

Mediumpurple1

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”

Mediumpurple3

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”

Mistyrose

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

Moccasin

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

Oldlace

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

Orange

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

[1] “No pathways significantly overrepresented”

Pink2

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

Red4

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

Rosybrown3

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

Rosybrown4

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

Royalblue

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

Sienna1

if(class(WGCNA.pathway[[41]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[41]][c(11,3:6)])}

[1] “No pathways significantly overrepresented”

Sienna3

if(class(WGCNA.pathway[[42]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[42]][c(11,3:6)])}

[1] “No pathways significantly overrepresented”

Slateblue2

if(class(WGCNA.pathway[[43]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[43]][c(11,3:6)])}

[1] “No pathways significantly overrepresented”

Thistle2

if(class(WGCNA.pathway[[44]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[44]][c(11,3:6)])}

White

if(class(WGCNA.pathway[[45]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[45]][c(11,3:6)])}

Yellow4

if(class(WGCNA.pathway[[46]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[46]][c(11,3:6)])}

Pathway Frequency

WGCNA.pathway <- readRDS(here("Data","Small Intestine","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_small_intestine_WGCNA.RData"))

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

[1] 1068 [1] 640

#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