Pathway Analysis Hippocampus
Ann Wells
April 23, 2023
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","Hippocampus","log.tdata.FPKM.sample.info.subset.hippocampus.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","Hippocampus","log.tdata.FPKM.sample.info.subset.hippocampus.WGCNA.module.membership.csv"), header=T)
Data_setup(WGCNA, file = "Annotated_genes_in_hippocampus_WGCNA_Chang_B6_96hr_4wk.RData", folder="Hippocampus")
Matched<-readRDS(here("Data","Hippocampus","Annotated_genes_in_hippocampus_WGCNA_Chang_B6_96hr_4wk.RData"))
pathways(Matched, pathwayfile = "Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_hippocampus_WGCNA.RData", genefile = "Chang_B6_96hr_4wk_gprofiler_gene_annotation_list_hippocampus_WGCNA.RData", folder = "Hippocampus")
WGCNA.pathway <-readRDS(here("Data","Hippocampus","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_hippocampus_WGCNA.RData"))antiquewhite
bisque2
blanchedalmond
blue1
blue3
brown1
brown2
chocolate
chocolate2
chocolate3
coral1
darkgoldenrod1
darkgoldenrod4
darkgrey
darkolivegreen
darkseagreen2
darkslateblue
deeppink
deepskyblue4
dodgerblue1
dodgerblue3
firebrick2
firebrick3
goldenrod4
green
green3
grey
indianred1
indianred2
indianred4
khaki3
lavenderblush1
lavenderblush2
lavenderblush3
orange3
orangered1
pink1
pink4
rosybrown3
salmon1
slateblue1
tan1
tan4
tomato
turquoise
Table of Pathways
WGCNA.pathway <- readRDS(here("Data","Hippocampus","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_hippocampus_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”
Bisque2
if(class(WGCNA.pathway[[2]]) == "numeric"){
print("No pathways significantly overrepresented")
} else {DT.table.path(WGCNA.pathway[[2]][c(11,3:6)])}Blanchedalmond
if(class(WGCNA.pathway[[3]]) == "numeric"){
print("No pathways significantly overrepresented")
} else {DT.table.path(WGCNA.pathway[[3]][c(11,3:6)])}[1] “No pathways significantly overrepresented”
Blue1
if(class(WGCNA.pathway[[4]]) == "numeric"){
print("No pathways significantly overrepresented")
} else {DT.table.path(WGCNA.pathway[[4]][c(11,3:6)])}[1] “No pathways significantly overrepresented”
Blue3
if(class(WGCNA.pathway[[5]]) == "numeric"){
print("No pathways significantly overrepresented")
} else {DT.table.path(WGCNA.pathway[[5]][c(11,3:6)])}Brown1
if(class(WGCNA.pathway[[6]]) == "numeric"){
print("No pathways significantly overrepresented")
} else {DT.table.path(WGCNA.pathway[[6]][c(11,3:6)])}Brown2
if(class(WGCNA.pathway[[7]]) == "numeric"){
print("No pathways significantly overrepresented")
} else {DT.table.path(WGCNA.pathway[[7]][c(11,3:6)])}Chocolate
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)])}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)])}Darkgoldenrod1
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”
Darkgoldenrod4
if(class(WGCNA.pathway[[13]]) == "numeric"){
print("No pathways significantly overrepresented")
} else {DT.table.path(WGCNA.pathway[[13]][c(11,3:6)])}Darkgrey
if(class(WGCNA.pathway[[14]]) == "numeric"){
print("No pathways significantly overrepresented")
} else {DT.table.path(WGCNA.pathway[[14]][c(11,3:6)])}Darkolivegreen
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”
Darkseagreen2
if(class(WGCNA.pathway[[16]]) == "numeric"){
print("No pathways significantly overrepresented")
} else {DT.table.path(WGCNA.pathway[[16]][c(11,3:6)])}Darkslateblue
if(class(WGCNA.pathway[[17]]) == "numeric"){
print("No pathways significantly overrepresented")
} else {DT.table.path(WGCNA.pathway[[17]][c(11,3:6)])}Deeppink
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”
Deepskyblue4
if(class(WGCNA.pathway[[19]]) == "numeric"){
print("No pathways significantly overrepresented")
} else {DT.table.path(WGCNA.pathway[[19]][c(11,3:6)])}Dodgerblue1
if(class(WGCNA.pathway[[20]]) == "numeric"){
print("No pathways significantly overrepresented")
} else {DT.table.path(WGCNA.pathway[[20]][c(11,3:6)])}Dodgerblue3
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”
Firebrick2
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”
Firebrick3
if(class(WGCNA.pathway[[23]]) == "numeric"){
print("No pathways significantly overrepresented")
} else {DT.table.path(WGCNA.pathway[[23]][c(11,3:6)])}[1] “No pathways significantly overrepresented”
Goldenrod4
if(class(WGCNA.pathway[[24]]) == "numeric"){
print("No pathways significantly overrepresented")
} else {DT.table.path(WGCNA.pathway[[24]][c(11,3:6)])}Green
if(class(WGCNA.pathway[[25]]) == "numeric"){
print("No pathways significantly overrepresented")
} else {DT.table.path(WGCNA.pathway[[25]][c(11,3:6)])}Green3
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”
Grey
if(class(WGCNA.pathway[[27]]) == "numeric"){
print("No pathways significantly overrepresented")
} else {DT.table.path(WGCNA.pathway[[27]][c(11,3:6)])}Indianred1
if(class(WGCNA.pathway[[28]]) == "numeric"){
print("No pathways significantly overrepresented")
} else {DT.table.path(WGCNA.pathway[[28]][c(11,3:6)])}Indianred2
if(class(WGCNA.pathway[[29]]) == "numeric"){
print("No pathways significantly overrepresented")
} else {DT.table.path(WGCNA.pathway[[29]][c(11,3:6)])}Indianred4
if(class(WGCNA.pathway[[30]]) == "numeric"){
print("No pathways significantly overrepresented")
} else {DT.table.path(WGCNA.pathway[[30]][c(11,3:6)])}Khaki3
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”
Lavenderblush1
if(class(WGCNA.pathway[[32]]) == "numeric"){
print("No pathways significantly overrepresented")
} else {DT.table.path(WGCNA.pathway[[32]][c(11,3:6)])}Lavenderblush2
if(class(WGCNA.pathway[[33]]) == "numeric"){
print("No pathways significantly overrepresented")
} else {DT.table.path(WGCNA.pathway[[33]][c(11,3:6)])}Lavenderblush3
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”
Orange3
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”
Orangered1
if(class(WGCNA.pathway[[36]]) == "numeric"){
print("No pathways significantly overrepresented")
} else {DT.table.path(WGCNA.pathway[[36]][c(11,3:6)])}[1] “No pathways significantly overrepresented”
Pink1
if(class(WGCNA.pathway[[37]]) == "numeric"){
print("No pathways significantly overrepresented")
} else {DT.table.path(WGCNA.pathway[[37]][c(11,3:6)])}Pink4
if(class(WGCNA.pathway[[38]]) == "numeric"){
print("No pathways significantly overrepresented")
} else {DT.table.path(WGCNA.pathway[[38]][c(11,3:6)])}Rosybrown3
if(class(WGCNA.pathway[[39]]) == "numeric"){
print("No pathways significantly overrepresented")
} else {DT.table.path(WGCNA.pathway[[39]][c(11,3:6)])}[1] “No pathways significantly overrepresented”
Salmon1
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”
Slateblue1
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”
Tan1
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”
Tan4
if(class(WGCNA.pathway[[43]]) == "numeric"){
print("No pathways significantly overrepresented")
} else {DT.table.path(WGCNA.pathway[[43]][c(11,3:6)])}Tomato
if(class(WGCNA.pathway[[44]]) == "numeric"){
print("No pathways significantly overrepresented")
} else {DT.table.path(WGCNA.pathway[[44]][c(11,3:6)])}[1] “No pathways significantly overrepresented”
Turquoise
if(class(WGCNA.pathway[[45]]) == "numeric"){
print("No pathways significantly overrepresented")
} else {DT.table.path(WGCNA.pathway[[45]][c(11,3:6)])}Pathway Frequency
WGCNA.pathway <- readRDS(here("Data","Hippocampus","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_hippocampus_WGCNA.RData"))
#pdf("Counts_of_each_pathway_identified_within_hippocampus.pdf")
count <- count.pathways(WGCNA.pathway)[1] 611 [1] 429
#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