Introduction and Data files

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 the overall summary expression of each module across the main effects, as well as, assess the significance of each main effect and their interaction, using ANOVA, for each module and assess potential interactions visually for each module.

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

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

tdata.FPKM.sample.info <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_phenotypes.RData"))

tdata.FPKM <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_numeric.RData"))

log.tdata.FPKM <- log(tdata.FPKM + 1)
log.tdata.FPKM <- as.data.frame(log.tdata.FPKM)

log.tdata.FPKM.sample.info <- cbind(log.tdata.FPKM, tdata.FPKM.sample.info[,27238:27240])

log.tdata.FPKM.sample.info <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(rowname != "A113") %>% column_to_rownames()

log.tdata.FPKM.subset <- log.tdata.FPKM[,colMeans(log.tdata.FPKM != 0) > 0.5] 

log.tdata.FPKM.sample.info.subset <- cbind(log.tdata.FPKM.subset,tdata.FPKM.sample.info[,27238:27240])
log.tdata.FPKM.sample.info.subset <- log.tdata.FPKM.sample.info.subset %>% rownames_to_column() %>% filter(rowname != "A113") %>% column_to_rownames()

log.tdata.FPKM.sample.info.subset.kidney <- log.tdata.FPKM.sample.info.subset %>% rownames_to_column() %>% filter(Tissue == "Kidney") %>% column_to_rownames()

log.tdata.FPKM.sample.info.subset.kidney$Treatment[log.tdata.FPKM.sample.info.subset.kidney$Treatment=="None"] <- "Control"

log.tdata.FPKM.sample.info.subset.kidney <- cbind(log.tdata.FPKM.sample.info.subset.kidney, Time.Treatment = paste(log.tdata.FPKM.sample.info.subset.kidney$Time, log.tdata.FPKM.sample.info.subset.kidney$Treatment))

module.labels <- readRDS(here("Data","Kidney","log.tdata.FPKM.sample.info.subset.kidney.WGCNA.module.labels.RData"))
module.eigens <- readRDS(here("Data","Kidney","log.tdata.FPKM.sample.info.subset.kidney.WGCNA.module.eigens.RData"))
modules <- readRDS(here("Data","Kidney","log.tdata.FPKM.sample.info.subset.kidney.WGCNA.module.membership.RData"))
net.deg <- readRDS(here("Data","Kidney","Chang_2DG_BL6_connectivity_kidney.RData"))
ensembl.location <- readRDS(here("Data","Ensembl_gene_id_and_location.RData"))

Eigengene Stratification

Eigengene were stratified by time and treatment. The heatmap is a matrix of the average eigengene value for each level of the trait.

factors <- c("Time","Treatment","Time.Treatment")
eigenmetabolite(factors,log.tdata.FPKM.sample.info.subset.kidney)

Time

Treatment

Time.Treatment

ANOVA

An ANOVA using aligned rank transformation was performed for each module. The full model is y ~ time + treatment + time:treatment.

# Three-Way ANOVA for each Eigenmetabolite
model.data = dplyr::bind_cols(module.eigens[rownames(log.tdata.FPKM.sample.info.subset.kidney),], log.tdata.FPKM.sample.info.subset.kidney[,c("Time","Treatment")])
model.data$Time <- as.factor(model.data$Time)
model.data$Treatment <- as.factor(model.data$Treatment)

final.anova <- list()
for (m in colnames(module.eigens)) {
  a <- art(data = model.data, model.data[,m] ~ Time*Treatment)
  model <- anova(a)
  adjust <- p.adjust(model$`Pr(>F)`, method = "BH")
  
  final.anova[[m]] <- cbind(model, adjust)
}

Cyan Module

DT.table(final.anova[[1]])

Blue Module

DT.table(final.anova[[2]])

Red Module

DT.table(final.anova[[3]])

Pink Module

DT.table(final.anova[[4]])

Magenta Module

DT.table(final.anova[[5]])

Darkred Module

DT.table(final.anova[[6]])

Midnightblue Module

DT.table(final.anova[[7]])

Purple Module

DT.table(final.anova[[8]])

Greenyellow Module

DT.table(final.anova[[9]])

Salmon Module

DT.table(final.anova[[10]])

Lightcyan Module

DT.table(final.anova[[11]])

Grey60 Module

DT.table(final.anova[[12]])

Lightgreen Module

DT.table(final.anova[[13]])

Lightyellow Module

DT.table(final.anova[[14]])

Darkgreen Module

DT.table(final.anova[[15]])

Darkturquoise Module

DT.table(final.anova[[16]])

Darkgrey Module

DT.table(final.anova[[17]])

Interaction plots

Interaction plots were created to identify which modules have a potential interaction between time and treatment. A potential interaction is identified when the two lines cross.

for (m in module.labels) {
  p = plot.interaction(model.data, "Time", "Treatment", resp = m)
  name <- sapply(str_split(m,"_"),"[",2)
  cat("\n###",name,"\n")
  print(p)
  cat("\n \n")
}