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 plot correlations of GSVA eigengenes for each pathway across tissues of interest.

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

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

heart <- readRDS(here("Data","Heart","log.tdata.FPKM.sample.info.subset.heart.WGCNA.module.eigens.RData"))

hypothalamus <- readRDS(here("Data","Hypothalamus","log.tdata.FPKM.sample.info.subset.hypothalamus.WGCNA.module.eigens.RData"))

liver <- readRDS(here("Data","Liver","log.tdata.FPKM.sample.info.subset.liver.WGCNA.module.eigens.RData"))

prefrontal.cortex <- readRDS(here("Data","Prefrontal Cortex","log.tdata.FPKM.sample.info.subset.prefrontal.cortex.WGCNA.module.eigens.RData"))

skeletal.muscle <- readRDS(here("Data","Skeletal Muscle","log.tdata.FPKM.sample.info.subset.skeletal.muscle.WGCNA.module.eigens.RData"))

small.intestine <- readRDS(here("Data","Small Intestine", "log.tdata.FPKM.sample.info.subset.small.intestine.WGCNA.module.eigens.RData"))

## heart
modulenames <- c()
  for(i in 1:length(heart)){
    name <- strsplit(colnames(heart),"_")
    modulenames[i] <- name[[i]][2]
  }

colnames(heart) <- modulenames

## hypothalamus
modulenames <- c()
  for(i in 1:length(hypothalamus)){
    name <- strsplit(colnames(hypothalamus),"_")
    modulenames[i] <- name[[i]][2]
  }

colnames(hypothalamus) <- modulenames

## liver
modulenames <- c()
  for(i in 1:length(liver)){
    name <- strsplit(colnames(liver),"_")
    modulenames[i] <- name[[i]][2]
  }

colnames(liver) <- modulenames

## muscle
modulenames <- c()
  for(i in 1:length(skeletal.muscle)){
    name <- strsplit(colnames(skeletal.muscle),"_")
    modulenames[i] <- name[[i]][2]
  }

colnames(skeletal.muscle) <- modulenames

## prefrontal cortex
modulenames <- c()
  for(i in 1:length(prefrontal.cortex)){
    name <- strsplit(colnames(prefrontal.cortex),"_")
    modulenames[i] <- name[[i]][2]
  }

colnames(prefrontal.cortex) <- modulenames

## small intestine
modulenames <- c()
  for(i in 1:length(small.intestine)){
    name <- strsplit(colnames(small.intestine),"_")
    modulenames[i] <- name[[i]][2]
  }

names(small.intestine) <- modulenames

modules.sig <- cbind(heart$darkgreen, hypothalamus$bisque4, prefrontal.cortex$bisque2, small.intestine$green4, skeletal.muscle$salmon2, liver$darkolivegreen)
colnames(modules.sig) <- c("Heart","Hypothalamus", "Prefrontal Cortex","Small Intestine","Skeletal Muscle","Liver")
rownames(modules.sig) <- rownames(prefrontal.cortex)

heart.gsva <- readRDS(here("Data","Heart","GSVA.modules.heart.RData"))
heart.pathways <- readRDS(here("Data","Heart","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_heart_WGCNA.RData"))
term.names <- heart.pathways[[4]]$term_name
heart.darkgreen <- heart.gsva$log.tdata.FPKM.sample.info.subset.heart.WGCNA_darkgreen[,match(term.names,colnames(heart.gsva$log.tdata.FPKM.sample.info.subset.heart.WGCNA_darkgreen))]
heart.darkgreen <- heart.darkgreen[, colSums(is.na(heart.darkgreen)) != nrow(heart.darkgreen)]

hypothalamus.gsva <- readRDS(here("Data","Hypothalamus","GSVA.modules.hypothalamus.RData"))
hypothalamus.pathways <- readRDS(here("Data","Hypothalamus","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_hypothalamus_WGCNA.RData"))
term.names <- hypothalamus.pathways[[3]]$term_name
hypothalamus.bisque4 <- hypothalamus.gsva$log.tdata.FPKM.sample.info.subset.hypothalamus.WGCNA_bisque4[,match(term.names,colnames(hypothalamus.gsva$log.tdata.FPKM.sample.info.subset.hypothalamus.WGCNA_bisque4))]
hypothalamus.bisque4 <- hypothalamus.bisque4[, colSums(is.na(hypothalamus.bisque4)) != nrow(hypothalamus.bisque4)]
  

liver.gsva <- readRDS(here("Data","Liver","GSVA.modules.liver.RData"))
liver.pathways <- readRDS(here("Data","Liver","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_liver_WGCNA.RData"))
term.names <- liver.pathways[[8]]$term_name
liver.darkolivegreen <- liver.gsva$log.tdata.FPKM.sample.info.subset.liver.WGCNA_darkolivegreen[,match(term.names,colnames(liver.gsva$log.tdata.FPKM.sample.info.subset.liver.WGCNA_darkolivegreen))]

prefrontal.cortex.gsva <- readRDS(here("Data","Prefrontal Cortex","GSVA.modules.prefrontal.cortex.RData"))
prefrontal.cortex.pathways <- readRDS(here("Data","Prefrontal Cortex","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_prefrontal_cortex_WGCNA.RData"))
term.names <- prefrontal.cortex.pathways[[3]]$term_name
prefrontal.cortex.bisque2 <- prefrontal.cortex.gsva$log.tdata.FPKM.sample.info.subset.prefrontal.cortex.WGCNA_bisque2[,match(term.names,colnames(prefrontal.cortex.gsva$log.tdata.FPKM.sample.info.subset.prefrontal.cortex.WGCNA_bisque2))]
prefrontal.cortex.bisque2 <- prefrontal.cortex.bisque2[, colSums(is.na(prefrontal.cortex.bisque2)) != nrow(prefrontal.cortex.bisque2)]

skeletal.muscle.gsva <- readRDS(here("Data","Skeletal Muscle","GSVA.modules.skeletal.muscle.RData"))
skeletal.muscle.pathways <- readRDS(here("Data","Skeletal Muscle","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_skeletal_muscle_WGCNA.RData"))
term.names <- skeletal.muscle.pathways[[35]]$term_name
skeletal.muscle.salmon2 <- skeletal.muscle.gsva$log.tdata.FPKM.sample.info.subset.skeletal.muscle.WGCNA_salmon2[,match(term.names,colnames(skeletal.muscle.gsva$log.tdata.FPKM.sample.info.subset.skeletal.muscle.WGCNA_salmon2))]
skeletal.muscle.salmon2 <- skeletal.muscle.salmon2[, colSums(is.na(skeletal.muscle.salmon2)) != nrow(skeletal.muscle.salmon2)]

small.intestine.gsva <- readRDS(here("Data","Small Intestine","GSVA.modules.small.intestine.RData"))
small.intestine.pathways <- readRDS(here("Data","Small Intestine","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_small_intestine_WGCNA.RData"))
term.names <- small.intestine.pathways[[18]]$term_name
small.intestine.green4 <- small.intestine.gsva$log.tdata.FPKM.sample.info.subset.small.intestine.WGCNA_green4[,match(term.names, colnames(small.intestine.gsva$log.tdata.FPKM.sample.info.subset.small.intestine.WGCNA_green4))]
small.intestine.green4 <- small.intestine.green4[, colSums(is.na(small.intestine.green4)) != nrow(small.intestine.green4)]

modules.tissue <- list(heart.darkgreen, hypothalamus.bisque4, prefrontal.cortex.bisque2, small.intestine.green4)

names(modules.tissue) <- c("heart.darkgreen", "hypothalamus.bisque4", "prefrontal.cortex.bisque2", "small.intestine.green4")

Correlations

for(i in 1:length(modules.tissue)){
  for(j in 1:length(modules.tissue)){
    cat("\n###",paste(names(modules.tissue[i]), "vs", names(modules.tissue[j])),"{.tabset .tabset-fade .tabset-pills}","\n")
    correlations <- cor(modules.tissue[[i]], modules.tissue[[j]])
    pheatmap(correlations, show_rownames = T,show_colnames = T, fontsize_col = 5, fontsize_row = 7, color = colorRampPalette(rev(brewer.pal(n = 9, name =
  "BuPu")))(100))
    cat("\n \n")
  }
}

heart.darkgreen vs heart.darkgreen

heart.darkgreen vs hypothalamus.bisque4

heart.darkgreen vs prefrontal.cortex.bisque2

heart.darkgreen vs small.intestine.green4

hypothalamus.bisque4 vs heart.darkgreen

hypothalamus.bisque4 vs hypothalamus.bisque4

hypothalamus.bisque4 vs prefrontal.cortex.bisque2

hypothalamus.bisque4 vs small.intestine.green4

prefrontal.cortex.bisque2 vs heart.darkgreen

prefrontal.cortex.bisque2 vs hypothalamus.bisque4

prefrontal.cortex.bisque2 vs prefrontal.cortex.bisque2

prefrontal.cortex.bisque2 vs small.intestine.green4

small.intestine.green4 vs heart.darkgreen

small.intestine.green4 vs hypothalamus.bisque4

small.intestine.green4 vs prefrontal.cortex.bisque2

small.intestine.green4 vs small.intestine.green4

  for(j in 1:length(modules.tissue)){
    cat("\n###",paste("liver.darkolivegreen", "vs", names(modules.tissue[j])),"{.tabset .tabset-fade .tabset-pills}","\n")
    correlations <- cor(liver.darkolivegreen, modules.tissue[[j]])
    pheatmap(correlations, show_rownames = T,show_colnames = T, fontsize_col = 4, fontsize_row = 4, color = colorRampPalette(rev(brewer.pal(n = 9, name =
  "BuPu")))(100), cluster_rows = F)
    cat("\n \n")
  }

liver.darkolivegreen vs heart.darkgreen

liver.darkolivegreen vs hypothalamus.bisque4

liver.darkolivegreen vs prefrontal.cortex.bisque2

liver.darkolivegreen vs small.intestine.green4

# correlations <- cor(heart.darkgreen,small.intestine.green4)
# breaks <- seq(-.65,.61,length.out = 100)
# pdf("Heatmap of small intestine green4 and heart darkgreen correlations GSVA pathways.pdf")
# pheatmap(correlations, show_rownames = T,show_colnames = T, fontsize_col = 8, fontsize_row = 8, color = colorRampPalette(rev(brewer.pal(n = 11, name =
#   "Paired")))(100), breaks = breaks)
# dev.off()

Analysis performed by Ann Wells

The Carter Lab The Jackson Laboratory 2023

ann.wells@jax.org

Scatterplots Across Tissue Modules for Chosen Modules

Ann Wells

April 23, 2023