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

log.tdata.FPKM.subset <- subset(log.tdata.FPKM.sample.info.subset.hypothalamus, select = -c(Time,Treatment,Tissue))
  
WGCNA.pathway <-readRDS(here("Data","Hypothalamus","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_hypothalamus_WGCNA.RData"))

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

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

MEs0 <- read.csv(here("Data","Hypothalamus","log.tdata.FPKM.sample.info.subset.hypothalamus.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$midnightblue,MEs$bisque4,MEs$navajowhite2))
colnames(Sigmodules) <- c("midnightblue","bisque4","navajowhite2")

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")
}

midnightblue

Treatment

Partial Correlation Treatment

estimate p.value statistic n gp Method
-0.8325 0.0001177 -5.417 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
-0.8746 1.987e-05 -6.505 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
-0.6437 0.009607 -3.033 16 1 pearson

NULL

Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.5537 0.03222 2.398 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
0.3068 0.266 1.162 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
0.6437 0.009607 3.033 16 1 pearson

NULL

Treat.Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.5537 0.03222 2.398 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
-0.8746 1.987e-05 -6.505 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
-0.7978 0.0003655 -4.771 16 1 pearson

NULL

bisque4

Treatment

Partial Correlation Treatment

estimate p.value statistic n gp Method
0.6619 0.007193 3.183 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
0.687 0.00466 3.409 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
0.4293 0.1103 1.714 16 1 pearson

NULL

Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
-0.3578 0.1904 -1.381 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
-0.2682 0.3338 -1.004 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
-0.4293 0.1103 -1.714 16 1 pearson

NULL

Treat.Time

Partial Correlation Treatment

estimate p.value statistic n gp Method
-0.3578 0.1904 -1.381 16 1 pearson

NULL

Partial Correlation Time

estimate p.value statistic n gp Method
0.687 0.00466 3.409 16 1 pearson

NULL

Partial Correlation Treat.Time

estimate p.value statistic n gp Method
0.6319 0.0115 2.94 16 1 pearson

NULL

Scatterplots Module membership vs. Gene significance

Time

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

lightsteelblue1

darkslateblue

lightblue4

lavenderblush2

orangered1

antiquewhite2

darkgreen

indianred4

lightpink4

mediumpurple1

midnightblue

skyblue

grey

lightslateblue

plum

blue

bisque4

brown2

darkturquoise

thistle3

lightgreen

antiquewhite4

black

salmon4

Treatment

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

lightsteelblue1

darkslateblue

lightblue4

lavenderblush2

orangered1

antiquewhite2

darkgreen

indianred4

lightpink4

mediumpurple1

midnightblue

skyblue

grey

lightslateblue

plum

blue

bisque4

brown2

darkturquoise

thistle3

lightgreen

antiquewhite4

black

salmon4

Treatment by Time

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

lightsteelblue1

darkslateblue

lightblue4

lavenderblush2

orangered1

antiquewhite2

darkgreen

indianred4

lightpink4

mediumpurple1

midnightblue

skyblue

grey

lightslateblue

plum

blue

bisque4

brown2

darkturquoise

thistle3

lightgreen

antiquewhite4

black

salmon4

Analysis performed by Ann Wells

The Carter Lab The Jackson Laboratory 2023

ann.wells@jax.org