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.prefrontal.cortex <- log.tdata.FPKM.sample.info.subset %>% rownames_to_column() %>% filter(Tissue == "Pre-frontal Cortex") %>% column_to_rownames()

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

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

module.labels <- readRDS(here("Data","Prefrontal Cortex","log.tdata.FPKM.sample.info.subset.prefrontal.cortex.WGCNA.module.labels.RData"))
module.eigens <- readRDS(here("Data","Prefrontal Cortex","log.tdata.FPKM.sample.info.subset.prefrontal.cortex.WGCNA.module.eigens.RData"))
modules <- readRDS(here("Data","Prefrontal Cortex","log.tdata.FPKM.sample.info.subset.prefrontal.cortex.WGCNA.module.membership.RData"))
net.deg <- readRDS(here("Data","Prefrontal Cortex","Chang_2DG_BL6_connectivity_prefrontal_cortex.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.prefrontal.cortex)

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.prefrontal.cortex),], log.tdata.FPKM.sample.info.subset.prefrontal.cortex[,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)
}

Blanchedalmond Module

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

Chocolate4 Module

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

Black Module

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

lightgoldenrod3 Module

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

Darkolivegreen2 Module

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

Aquamarine Module

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

Bisque2 Module

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

Darkolivegreen4 Module

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

Grey Module

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

Blueviolet Module

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

Darkturquoise Module

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

Khaki3 Module

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

Dodgerblue4 Module

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

Cornsilk2 Module

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

Darkseagreen4 Module

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

Lightpink1 Module

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

Navajowhite3 Module

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

Antiquewhite Module

DT.table(final.anova[[18]])

Pink Module

DT.table(final.anova[[19]])

Salmon Module

DT.table(final.anova[[20]])

Lavenderblush2 Module

DT.table(final.anova[[21]])

Plum Module

DT.table(final.anova[[22]])

Goldenrod3 Module

DT.table(final.anova[[23]])

Chocolate2 Module

DT.table(final.anova[[24]])

Blue4 Module

DT.table(final.anova[[25]])

Mistyrose Module

DT.table(final.anova[[26]])

Cornsilk Module

DT.table(final.anova[[27]])

Lightskyblue2 Module

DT.table(final.anova[[28]])

Burlywood Module

DT.table(final.anova[[29]])

Oldlace Module

DT.table(final.anova[[30]])

Lightblue Module

DT.table(final.anova[[31]])

Limegreen Module

DT.table(final.anova[[32]])

Hotpink3 Module

DT.table(final.anova[[33]])

Lightskyblue Module

DT.table(final.anova[[34]])

Mediumorchid1 Module

DT.table(final.anova[[35]])

Rosybrown2 Module

DT.table(final.anova[[36]])

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")
}