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 contribution of each main effect or their combination to each module identified, as well as the relationship of each gene within the module and its significance.

needed.packages <- c("tidyverse", "here", "functional", "gplots", "dplyr", "GeneOverlap", "R.utils", "reshape2","magrittr","data.table", "RColorBrewer","preprocessCore", "ARTool","emmeans", "phia", "gProfileR", "WGCNA","plotly", "pheatmap","ppcor", "pander","downloadthis")
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","WGCNA_contribution_source.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.subset <- log.tdata.FPKM.subset %>% rownames_to_column() %>% filter(rowname != "A113") %>% column_to_rownames()

log.tdata.FPKM.sample.info.subset.small.intestine <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue == "Small Intestine") %>% column_to_rownames()

log.tdata.FPKM.subset <- subset(log.tdata.FPKM.sample.info.subset.small.intestine, select = -c(Time,Treatment,Tissue))
  
WGCNA.pathway <-readRDS(here("Data","Small Intestine","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_small_intestine_WGCNA.RData"))

Matched<-readRDS(here("Data","Small Intestine","Annotated_genes_in_small_intestine_WGCNA_Chang_B6_96hr_4wk.RData"))


module.names <- Matched$X..Module.
name <- str_split(module.names,"_")
samples <-c()
for(i in 1:length(name)){
samples[[i]] <- name[[i]][2]
}  
name <- str_split(samples,"\"")
name <- unlist(name)

Treatment <- unclass(as.factor(log.tdata.FPKM.sample.info.subset.small.intestine[,27238]))
Time <- unclass(as.factor(log.tdata.FPKM.sample.info.subset.small.intestine[,27237]))
Treat.Time <- paste0(Treatment, Time)
phenotype <- data.frame(cbind(Treatment, Time, Treat.Time))

nSamples <- nrow(log.tdata.FPKM.sample.info.subset.small.intestine)

MEs0 <- read.csv(here("Data","Small Intestine","log.tdata.FPKM.sample.info.subset.small.intestine.WGCNA.module.eigens.csv"),header = T, row.names = 1)
name <- str_split(names(MEs0),"_")
samples <-c()
for(i in 1:length(name)){
samples[[i]] <- name[[i]][2]
}  
name <- str_split(samples,"\"")
name <- unlist(name)
colnames(MEs0) <-name
MEs <- orderMEs(MEs0)
moduleTraitCor <- cor(MEs, phenotype, use = "p");
moduleTraitPvalue <- corPvalueStudent(moduleTraitCor, nSamples)

Relationship between Modules and Traits

#sizeGrWindow(10,6)
# Will display correlations and their p-values
textMatrix = paste(signif(moduleTraitCor, 2), "\n(",
                        signif(moduleTraitPvalue, 1), ")", sep = "");
dim(textMatrix) = dim(moduleTraitCor)

# Display the correlation values within a heatmap plot
heat <- pheatmap(moduleTraitCor, main = paste("Module-trait relationships"), color=colorRampPalette(brewer.pal(n = 12, name = "Paired"))(10), cluster_rows = F, cluster_cols = F, fontsize_number = 4, angle_col = 45, number_color = "black", border_color = "white")
heat

DT::datatable(moduleTraitPvalue, extensions = 'Buttons',
                  rownames = TRUE, 
                  filter="top",
                  options = list(dom = 'Blfrtip',
                                 buttons = c('copy', 'csv', 'excel'),
                                 lengthMenu = list(c(10,25,50,-1),
                                                   c(10,25,50,"All")), 
                                 scrollX= TRUE), class = "display")

Partial Correlation

phenotype$Treatment <- as.numeric(phenotype$Treatment)
phenotype$Time <- as.numeric(phenotype$Time)
phenotype$Treat.Time <- as.numeric(phenotype$Treat.Time)

Sigmodules <- as.data.frame(cbind(MEs$green4,MEs$blue2,MEs$cornsilk,MEs$magenta2,MEs$darkgoldenrod4))
colnames(Sigmodules) <- c("green4","blue2","cornsilk","magenta2","darkgoldenrod4")

for(i in 1:length(Sigmodules)){
   cat("\n##",colnames(Sigmodules[i]),"{.tabset .tabset-fade .tabset-pills}","\n")
   for(j in 1:length(phenotype)){
      cat("\n###", colnames(phenotype[j]),"{.tabset .tabset-fade .tabset-pills}","\n")
      for(k in 1:length(phenotype)){
         cat("\n####", "Partial Correlation", colnames(phenotype[k]),"\n")
         partial <- pcor.test(Sigmodules[,i], phenotype[,j],phenotype[,k])
         panderOptions('knitr.auto.asis', FALSE)
         print(pander(partial))
         cat("\n \n")
      }
      cat("\n \n")
   }
   cat("\n \n")
}

green4

Treatment

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.701 0.0036 3.544 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
0.7018 0.003545 3.552 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
0.03103 0.9126 0.1119 16 1 pearson

NULL

Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.06716 0.812 0.2427 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
0.0479 0.8654 0.1729 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
-0.03103 0.9126 -0.1119 16 1 pearson

NULL

Treat.Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.06716 0.812 0.2427 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
0.7018 0.003545 3.552 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
0.7022 0.003513 3.556 16 1 pearson

NULL

blue2

Treatment

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.2651 0.3396 0.9914 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
0.3494 0.2018 1.345 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
0.6882 0.004562 3.42 16 1 pearson

NULL

Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
-0.6755 0.005718 -3.303 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
-0.6513 0.008535 -3.095 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
-0.6882 0.004562 -3.42 16 1 pearson

NULL

Treat.Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
-0.6755 0.005718 -3.303 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
0.3494 0.2018 1.345 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
0.199 0.477 0.7322 16 1 pearson

NULL

cornsilk

Treatment

Partial Correlation Treatment

estimate p.value statistic n gp Method
-0.3696 0.1751 -1.434 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
-0.4499 0.09245 -1.816 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
-0.6356 0.01088 -2.968 16 1 pearson

NULL

Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.6136 0.01499 2.802 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
0.5701 0.02649 2.502 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
0.6356 0.01088 2.968 16 1 pearson

NULL

Treat.Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.6136 0.01499 2.802 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
-0.4499 0.09245 -1.816 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
-0.3111 0.2591 -1.18 16 1 pearson

NULL

magenta2

Treatment

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.04077 0.8853 0.1471 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
0.05394 0.8486 0.1948 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
-0.6511 0.008561 -3.093 16 1 pearson

NULL

Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.6553 0.008005 3.128 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
0.6548 0.008076 3.123 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
0.6511 0.008561 3.093 16 1 pearson

NULL

Treat.Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.6553 0.008005 3.128 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
0.05394 0.8486 0.1948 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
0.1057 0.7077 0.3833 16 1 pearson

NULL

darkgoldenrod4

Treatment

Partial Correlation Treatment

estimate p.value statistic n gp Method
-0.3646 0.1815 -1.412 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
-0.446 0.09562 -1.797 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
0.5917 0.02016 2.646 16 1 pearson

NULL

Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
-0.6186 0.01395 -2.839 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
-0.5761 0.02461 -2.541 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
-0.5917 0.02016 -2.646 16 1 pearson

NULL

Treat.Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
-0.6186 0.01395 -2.839 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
-0.446 0.09562 -1.797 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
-0.4201 0.119 -1.669 16 1 pearson

NULL

Scatterplots Module membership vs. Gene significance

Time

time.contribution(phenotype, log.tdata.FPKM.subset, MEs)

sienna1

mediumpurple3

antiquewhite1

antiquewhite4

royalblue

lightskyblue2

white

antiquewhite2

blue3

burlywood

blue2

blue1

sienna3

thistle2

lightgreen

mistyrose

lightblue2

oldlace

red4

cornsilk

lightblue

firebrick

indianred

magenta2

yellow4

rosybrown3

green4

aliceblue

magenta3

hotpink4

deepskyblue

orange

grey

mediumpurple1

darkgoldenrod4

antiquewhite

firebrick3

linen

rosybrown4

slateblue2

pink2

coral1

coral4

lightblue4

moccasin

chocolate3

Treatment

treat.contribution(phenotype, log.tdata.FPKM.subset, MEs)

sienna1

mediumpurple3

antiquewhite1

antiquewhite4

royalblue

lightskyblue2

white

antiquewhite2

blue3

burlywood

blue2

blue1

sienna3

thistle2

lightgreen

mistyrose

lightblue2

oldlace

red4

cornsilk

lightblue

firebrick

indianred

magenta2

yellow4

rosybrown3

green4

aliceblue

magenta3

hotpink4

deepskyblue

orange

grey

mediumpurple1

darkgoldenrod4

antiquewhite

firebrick3

linen

rosybrown4

slateblue2

pink2

coral1

coral4

lightblue4

moccasin

chocolate3

Treatment by Time

treat.time.contribution(phenotype, log.tdata.FPKM.subset, MEs)

sienna1

mediumpurple3

antiquewhite1

antiquewhite4

royalblue

lightskyblue2

white

antiquewhite2

blue3

burlywood

blue2

blue1

sienna3

thistle2

lightgreen

mistyrose

lightblue2

oldlace

red4

cornsilk

lightblue

firebrick

indianred

magenta2

yellow4

rosybrown3

green4

aliceblue

magenta3

hotpink4

deepskyblue

orange

grey

mediumpurple1

darkgoldenrod4

antiquewhite

firebrick3

linen

rosybrown4

slateblue2

pink2

coral1

coral4

lightblue4

moccasin

chocolate3

Analysis performed by Ann Wells

The Carter Lab The Jackson Laboratory 2023

ann.wells@jax.org