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

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

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

module.labels <- readRDS(here("Data","Spleen","log.tdata.FPKM.sample.info.subset.spleen.WGCNA.module.labels.RData"))
module.eigens <- readRDS(here("Data","Spleen","log.tdata.FPKM.sample.info.subset.spleen.WGCNA.module.eigens.RData"))
modules <- readRDS(here("Data","Spleen","log.tdata.FPKM.sample.info.subset.spleen.WGCNA.module.membership.RData"))
net.deg <- readRDS(here("Data","Spleen","Chang_2DG_BL6_connectivity_spleen.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.spleen)

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

Palevioletred3 Module

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

Moccasin Module

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

Grey Module

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

Mediumpurple1 Module

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

Indianred4 Module

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

Slateblue1 Module

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

Coral2 Module

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

Blue3 Module

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

Blue2 Module

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

Black Module

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

Orangered4 Module

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

Tan4 Module

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

Orange Module

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

Thistle1 Module

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

Brown4 Module

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

Darkgreen Module

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

Darkseagreen4 Module

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

Mediumorchid Module

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

Plum Module

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

Thistle3 Module

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

Darkseagreen3 Module

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

Navajowhite Module

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

Sienna2 Module

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

Lightskyblue4 Module

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

Mediumpurple Module

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

Indianred2 Module

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

Royalblue3 Module

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

Cornflowerblue Module

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

Pink2 Module

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

Indianred1 Module

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

Lightblue2 Module

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

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")
}