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.hippocampus <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue == "Hippocampus") %>% column_to_rownames()

log.tdata.FPKM.subset <- subset(log.tdata.FPKM.sample.info.subset.hippocampus, select = -c(Time,Treatment,Tissue))
  
WGCNA.pathway <-readRDS(here("Data","Hippocampus","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_hippocampus_WGCNA.RData"))

Matched<-readRDS(here("Data","Hippocampus","Annotated_genes_in_hippocampus_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.hippocampus[,27238]))
Time <- unclass(as.factor(log.tdata.FPKM.sample.info.subset.hippocampus[,27237]))
Treat.Time <- paste0(Treatment, Time)
phenotype <- data.frame(cbind(Treatment, Time, Treat.Time))

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

MEs0 <- read.csv(here("Data","Hippocampus","log.tdata.FPKM.sample.info.subset.hippocampus.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"), display_numbers = textMatrix,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$coral1,MEs$darkslateblue,MEs$green, MEs$darkolivegreen,MEs$blue1,MEs$khaki3))
colnames(Sigmodules) <- c("coral1","darkslateblue","green","darkolivegreen","blue1","khaki3")

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")
}

coral1

Treatment

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.5322 0.04112 2.267 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
0.5605 0.02974 2.44 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
0.4212 0.1179 1.675 16 1 pearson

NULL

Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
-0.3706 0.1738 -1.439 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
-0.3138 0.2547 -1.192 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
-0.4212 0.1179 -1.675 16 1 pearson

NULL

Treat.Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
-0.3706 0.1738 -1.439 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
0.5605 0.02974 2.44 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
0.4984 0.05866 2.073 16 1 pearson

NULL

darkslateblue

Treatment

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.6254 0.01265 2.89 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
0.6331 0.01129 2.949 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
0.273 0.3249 1.023 16 1 pearson

NULL

Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
-0.1993 0.4764 -0.7334 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
-0.1555 0.5799 -0.5677 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
-0.273 0.3249 -1.023 16 1 pearson

NULL

Treat.Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
-0.1993 0.4764 -0.7334 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
0.6331 0.01129 2.949 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
0.6068 0.01645 2.753 16 1 pearson

NULL

green

Treatment

Partial Correlation Treatment

estimate p.value statistic n gp Method
-0.901 4.586e-06 -7.487 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
-0.9253 7.799e-07 -8.796 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
-0.6505 0.008646 -3.088 16 1 pearson

NULL

Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.5249 0.04452 2.224 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
0.2278 0.4143 0.8434 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
0.6505 0.008646 3.088 16 1 pearson

NULL

Treat.Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.5249 0.04452 2.224 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
-0.9253 7.799e-07 -8.796 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
-0.8738 2.068e-05 -6.48 16 1 pearson

NULL

darkolivegreen

Treatment

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.517 0.04846 2.177 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
0.5174 0.04822 2.18 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
0.01044 0.9705 0.03766 16 1 pearson

NULL

Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.04991 0.8598 0.1802 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
0.04272 0.8798 0.1542 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
-0.01044 0.9705 -0.03766 16 1 pearson

NULL

Treat.Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.04991 0.8598 0.1802 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
0.5174 0.04822 2.18 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
0.5186 0.04761 2.187 16 1 pearson

NULL

blue1

Treatment

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.5835 0.02241 2.59 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
0.5844 0.02214 2.597 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
0.001643 0.9954 0.005924 16 1 pearson

NULL

Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.0702 0.8037 0.2537 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
0.05701 0.8401 0.2059 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
-0.001643 0.9954 -0.005924 16 1 pearson

NULL

Treat.Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.0702 0.8037 0.2537 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
0.5844 0.02214 2.597 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
0.5863 0.02162 2.609 16 1 pearson

NULL

khaki3

Treatment

Partial Correlation Treatment

estimate p.value statistic n gp Method
-0.2452 0.3785 -0.9118 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
-0.2819 0.3086 -1.06 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
-0.5259 0.04407 -2.229 16 1 pearson

NULL

Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.5094 0.05243 2.134 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
0.4939 0.06134 2.048 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
0.5259 0.04407 2.229 16 1 pearson

NULL

Treat.Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.5094 0.05243 2.134 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
-0.2819 0.3086 -1.06 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
-0.1948 0.4866 -0.7161 16 1 pearson

NULL

Scatterplots Module membership vs. Gene significance

Time

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

orange3

tomato

antiquewhite

tan4

blanchedalmond

chocolate2

chocolate

darkseagreen2

goldenrod4

deeppink

bisque2

deepskyblue4

brown2

coral1

darkslateblue

blue3

dodgerblue1

turquoise

chocolate3

pink4

lavenderblush2

green3

darkgoldenrod1

green

firebrick3

orangered1

tan1

indianred2

salmon1

darkgoldenrod4

lavenderblush1

khaki3

indianred1

slateblue1

pink1

darkolivegreen

dodgerblue3

rosybrown3

grey

blue1

brown1

lavenderblush3

firebrick2

darkgrey

indianred4

Treatment

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

orange3

tomato

antiquewhite

tan4

blanchedalmond

chocolate2

chocolate

darkseagreen2

goldenrod4

deeppink

bisque2

deepskyblue4

brown2

coral1

darkslateblue

blue3

dodgerblue1

turquoise

chocolate3

pink4

lavenderblush2

green3

darkgoldenrod1

green

firebrick3

orangered1

tan1

indianred2

salmon1

darkgoldenrod4

lavenderblush1

khaki3

indianred1

slateblue1

pink1

darkolivegreen

dodgerblue3

rosybrown3

grey

blue1

brown1

lavenderblush3

firebrick2

darkgrey

indianred4

Treatment by Time

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

orange3

tomato

antiquewhite

tan4

blanchedalmond

chocolate2

chocolate

darkseagreen2

goldenrod4

deeppink

bisque2

deepskyblue4

brown2

coral1

darkslateblue

blue3

dodgerblue1

turquoise

chocolate3

pink4

lavenderblush2

green3

darkgoldenrod1

green

firebrick3

orangered1

tan1

indianred2

salmon1

darkgoldenrod4

lavenderblush1

khaki3

indianred1

slateblue1

pink1

darkolivegreen

dodgerblue3

rosybrown3

grey

blue1

brown1

lavenderblush3

firebrick2

darkgrey

indianred4

Analysis performed by Ann Wells

The Carter Lab The Jackson Laboratory 2023

ann.wells@jax.org