WGCNA on CTL04.

1. Environment Set Up

1.1 Values of RMarkdown parameters

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, collapse = TRUE)
for (i in 1:length(params))
  print(paste('Parameter:', names(params)[i], ' - Value:', params[[i]], '- Class:', class(params[[i]])))
## [1] "Parameter: Dataset  - Value: CTL04 - Class: character"
## [1] "Parameter: OutputFolder  - Value: ~/DataDir/bulkRNASeq/8.WGCNA/CTL04/2_ModuleCharacterization/ - Class: character"
library(dplyr)
## 
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## The following objects are masked from 'package:stats':
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library(ggplot2)
library(DT)

library(viridis)
## Loading required package: viridisLite
library(sechm)
library(WGCNA)
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library(SummarizedExperiment)
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Dataset <- params$Dataset
OutputFolder <- params$OutputFolder

if (dir.exists(OutputFolder) == FALSE) {
 dir.create(OutputFolder, recursive=TRUE)
}

1.2 Helper functions

source('WGCNAHelper.R')

2. Data Upload

2.1 Objects from first step

load('~/DataDir/bulkRNASeq/8.WGCNA/CTL04/1_NetworkGeneration/NetworkGeneration.RData')
DummyTotal <- DummyTraits

Objects generated from first step:

  • SE_Bio: starting SE
  • adjacency: adjacency matrix
  • dissTOM: TOM distance matrix
  • DummyTraits: phenotypic traits
  • ModuleAssignment: assignment for each gene to a module
  • FinalMEs: module eigengenes
  • VstSel: selected, transformed, transposed expression matrix

2.2 Settings

metadata(SE_WGCNA)$anno_colors <- list(Condition = c('DMSO' = 'grey30', 'CTL' = 'azure3', 
                 'AhHyd_Ag'='#F8766D', 'AhHyd_Inh'='#F8766D50',
                 'Andr_Ag'='#fccb17', 'Andr_Inh'='#C49A0050',  
                 "Estr_Ag"= '#53B400', "Estr_Inh"= '#53B40050', 
                 'GC_Ag' = '#00C094', 'GC_Inh' = '#00C09450',
                 'LivX_Ag' = '#00B6EB', 'LivX_Inh' = '#00B6EB50', 
                 'Ret_Ag' = '#A58AFF', 'Ret_Inh' = '#A58AFF50', 
                 'Thyr_Ag' = '#FB61D7', 'Thyr_Inh' = '#FB61D750'
                 ))
ScaledCols <- c('darkblue', "purple","white","lightgoldenrod1", 'goldenrod1')
CorrCols <- c('darkblue', "blue","white","forestgreen", 'green')

3. Module correlation with phenotypic traits

3.1 Correlation between module eigengenes and phenotypic traits

DummyTraits <- DummyTotal[,-c(1:4, 21)]

nGenes <- ncol(VstSel) 
nSamples <- nrow(VstSel)

# spearman correlation
SpearmanCor <- WGCNA::cor(FinalMEs, DummyTraits, use = 'p', method='spearman')
SpearmanPvalue <- WGCNA::corPvalueStudent(SpearmanCor, nSamples)

# pearson correlation
PearsonCor <- WGCNA::cor(FinalMEs, DummyTraits, use = 'p', method='pearson')
PearsonPvalue <- WGCNA::corPvalueStudent(PearsonCor, nSamples)

3.2 Visualize correlation matrix

Spearman

textMatrix <- paste(signif(SpearmanCor,2), '\n(', signif(SpearmanPvalue, 1), ')', sep = '')
dim(textMatrix) <- dim(SpearmanCor)
WGCNA::labeledHeatmap(Matrix = SpearmanCor, xLabels=names(DummyTraits), yLabels=names(FinalMEs), ySymbols=names(FinalMEs), 
                      colorLabels=FALSE, colors=viridis(50)[15:50], textMatrix = textMatrix, setStdMargins = FALSE, 
                      cex.text = 0.5, zlim = c(-1,1), main = paste('Module-trait relationships'))

Pearson

textMatrix <- paste(signif(PearsonCor,2), '\n(', signif(PearsonPvalue, 1), ')', sep = '')
dim(textMatrix) <- dim(PearsonCor)
WGCNA::labeledHeatmap(Matrix = PearsonCor, xLabels=names(DummyTraits), yLabels=names(FinalMEs), ySymbols=names(FinalMEs), 
                      colorLabels=FALSE, colors=viridis(50)[15:50], textMatrix = textMatrix, setStdMargins = FALSE, 
                      cex.text = 0.5, zlim = c(-1,1), main = paste('Module-trait relationships'))

4. Gene and sample -related metrics

4.1 Gene Module Membership

The Gene Module Membership quantifies the membership of each gene to all the identified modules. It is calculated by correlating for each gene its expression profile with the module eigengene of a given module.

Cor <- corAndPvalue(VstSel, FinalMEs, use='p',  method='spearman')

CorCoeff <- data.frame(Cor$cor)
names(CorCoeff) <- paste0('MM', substring(names(CorCoeff), 3))
CorCoeff$Gene <- row.names(Cor$cor)

CorP <- data.frame(Cor$p)
names(CorP) <- paste0('p_MM', substring(names(CorP), 3))
CorP$Gene <- row.names(Cor$p)
# Generation of data frame containing relevant information
GeneMM <- dplyr::inner_join(CorCoeff, CorP, by='Gene') %>% 
  dplyr::inner_join(ModuleAssignment, by='Gene')# %>%
#  dplyr::inner_join(GeneMeta, by='Gene') %>% dplyr::select(33, 32, 1:15, 17:31, 16, 34:36)

dim(GeneMM)
## [1] 10726    40

4.2 Gene Connectivity Measures

GeneCon <- intramodularConnectivity(adjacency, colors=ModuleAssignment$Module)
GeneCon$Gene <- row.names(GeneCon)

4.3 Gene Metrics dataframe

GeneMetrics <- dplyr::inner_join(GeneMM, GeneCon, by='Gene') 
GeneMetrics <- dplyr::left_join(GeneMetrics, dplyr::select(data.frame(rowData(SE_WGCNA)), c(2,4,5)), join_by('Gene'=='GeneName'))
rm(GeneMM, GeneCon)
table(GeneMetrics$Module)
## 
##        black         blue        brown         cyan      darkred        green 
##          533         1308          871          312           94          906 
##  greenyellow         grey       grey60   lightgreen  lightyellow      magenta 
##          353          404          166          160          117          470 
## midnightblue         pink       purple          red    royalblue          tan 
##          592          498          413          668          109          348 
##    turquoise 
##         2404
table(GeneMetrics$GeneBiotype)
## 
##         lncRNA protein_coding 
##           1758           8968

4.4. Sample module eigengenes and treatment information

METraits <- dplyr::mutate(data.frame(FinalMEs), Sample=row.names(FinalMEs)) %>% 
  inner_join(data.frame(DummyTotal) , by=c('Sample'='HRID'))

5. Turquoise Module

5.1 Module definition

ModName <- 'turquoise'
TopGenes <- ifelse(dim(dplyr::filter(GeneMetrics, Module==ModName))[1] > 250, 250,
                   dim(dplyr::filter(GeneMetrics, Module==ModName))[1])

The module contains 2404 genes: 2087 protein-coding and 317 lncRNAs (15 %) .

The top-250 genes according to intramodular connectivity will be shown in interactive scatterplot and table.

5.2 Module eigengene boxplot

boxME(METraits=METraits, module=ModName, ascisse='Condition', title=NULL, 
      lines='Yes', reference='DMSO')

5.3 Module membership boxplot

boxMM(GeneMM=GeneMetrics, module=ModName, title=NULL)

5.4 Module heatmap

sechm(SE_WGCNA, features=GeneMetrics[GeneMetrics$Module==ModName, ]$Gene, assayName="vst", gaps_at="Condition",
      top_annotation='Condition', hmcols=ScaledCols,  do.scale=TRUE, breaks=0.8, 
      anno_colors = metadata(SE_WGCNA)$anno_colors)
## `use_raster` is automatically set to TRUE for a matrix with more than
## 2000 rows. You can control `use_raster` argument by explicitly setting
## TRUE/FALSE to it.
## 
## Set `ht_opt$message = FALSE` to turn off this message.
## 'magick' package is suggested to install to give better rasterization.
## 
## Set `ht_opt$message = FALSE` to turn off this message.

5.5 Interactive Scatterplot

plotGeneMetrics(GeneMetrics, module=ModName, title=NULL, top=TopGenes)

5.6 Interactive table

interactiveMetrics(GeneMetrics, Module=ModName)

6. Blue Module

6.1 Module definition

ModName <- 'blue'
TopGenes <- ifelse(dim(dplyr::filter(GeneMetrics, Module==ModName))[1] > 250, 250,
                   dim(dplyr::filter(GeneMetrics, Module==ModName))[1])

The module contains 1308 genes: 947 protein-coding and 361 lncRNAs (38 %).

The top-250 genes according to intramodular connectivity will be shown in interactive scatterplot and table.

6.2 Module eigengene boxplot

boxME(METraits=METraits, module=ModName, ascisse='Condition', title=NULL, 
      lines='Yes', reference='DMSO')

6.3 Module membership boxplot

boxMM(GeneMM=GeneMetrics, module=ModName, title=NULL)

6.4 Module heatmap

sechm(SE_WGCNA, features=GeneMetrics[GeneMetrics$Module==ModName, ]$Gene, assayName="vst", gaps_at="Condition",
      top_annotation='Condition', hmcols=ScaledCols,  do.scale=TRUE, breaks=0.8, 
      anno_colors = metadata(SE_WGCNA)$anno_colors)

6.5 Interactive Scatterplot

plotGeneMetrics(GeneMetrics, module=ModName, title=NULL, top=TopGenes)

6.6 Interactive table

interactiveMetrics(GeneMetrics, Module=ModName)

7. Green Module

7.1 Module definition

ModName <- 'green'
TopGenes <- ifelse(dim(dplyr::filter(GeneMetrics, Module==ModName))[1] > 250, 250,
                   dim(dplyr::filter(GeneMetrics, Module==ModName))[1])

The module contains 906 genes: 826 protein-coding and 80 lncRNAs (10 %).

The top-250 genes according to intramodular connectivity will be shown in interactive scatterplot and table.

7.2 Module eigengene boxplot

boxME(METraits=METraits, module=ModName, ascisse='Condition', title=NULL, 
      lines='Yes', reference='DMSO')

7.3 Module membership boxplot

boxMM(GeneMM=GeneMetrics, module=ModName, title=NULL)

7.4 Module heatmap

sechm(SE_WGCNA, features=GeneMetrics[GeneMetrics$Module==ModName, ]$Gene, assayName="vst", gaps_at="Condition",
      top_annotation='Condition', hmcols=ScaledCols,  do.scale=TRUE, breaks=0.8, 
      anno_colors = metadata(SE_WGCNA)$anno_colors)

7.5 Interactive Scatterplot

plotGeneMetrics(GeneMetrics, module=ModName, title=NULL, top=TopGenes)

7.6 Interactive table

interactiveMetrics(GeneMetrics, Module=ModName)

8. Brown Module

8.1 Module definition

ModName <- 'brown'
TopGenes <- ifelse(dim(dplyr::filter(GeneMetrics, Module==ModName))[1] > 250, 250,
                   dim(dplyr::filter(GeneMetrics, Module==ModName))[1])

The module contains 871 genes: 715 protein-coding and 156 lncRNAs (22 %).

The top-250 genes according to intramodular connectivity will be shown in interactive scatterplot and table.

8.2 Module eigengene boxplot

boxME(METraits=METraits, module=ModName, ascisse='Condition', title=NULL, 
      lines='Yes', reference='DMSO')

8.3 Module membership boxplot

boxMM(GeneMM=GeneMetrics, module=ModName, title=NULL)

8.4 Module heatmap

sechm(SE_WGCNA, features=GeneMetrics[GeneMetrics$Module==ModName, ]$Gene, assayName="vst", gaps_at="Condition",
      top_annotation='Condition', hmcols=ScaledCols,  do.scale=TRUE, breaks=0.8, 
      anno_colors = metadata(SE_WGCNA)$anno_colors)

8.5 Interactive Scatterplot

plotGeneMetrics(GeneMetrics, module=ModName, title=NULL, top=TopGenes)

8.6 Interactive table

interactiveMetrics(GeneMetrics, Module=ModName)

9. Red Module

9.1 Module definition

ModName <- 'red'
TopGenes <- ifelse(dim(dplyr::filter(GeneMetrics, Module==ModName))[1] > 250, 250,
                   dim(dplyr::filter(GeneMetrics, Module==ModName))[1])

The module contains 668 genes: 541 protein-coding and 127 lncRNAs (23 %).

The top-250 genes according to intramodular connectivity will be shown in interactive scatterplot and table.

9.2 Module eigengene boxplot

boxME(METraits=METraits, module=ModName, ascisse='Condition', title=NULL, 
      lines='Yes', reference='DMSO')

9.3 Module membership boxplot

boxMM(GeneMM=GeneMetrics, module=ModName, title=NULL)

9.4 Module heatmap

sechm(SE_WGCNA, features=GeneMetrics[GeneMetrics$Module==ModName, ]$Gene, assayName="vst", gaps_at="Condition",
      top_annotation='Condition', hmcols=ScaledCols,  do.scale=TRUE, breaks=0.8, 
      anno_colors = metadata(SE_WGCNA)$anno_colors)

9.5 Interactive Scatterplot

plotGeneMetrics(GeneMetrics, module=ModName, title=NULL, top=TopGenes)

9.6 Interactive table

interactiveMetrics(GeneMetrics, Module=ModName)

10. Midnightblue Module

10.1 Module definition

ModName <- 'midnightblue'
TopGenes <- ifelse(dim(dplyr::filter(GeneMetrics, Module==ModName))[1] > 250, 250,
                   dim(dplyr::filter(GeneMetrics, Module==ModName))[1])

The module contains 592 genes: 460 protein-coding and 132 lncRNAs (29 %).

The top-250 genes according to intramodular connectivity will be shown in interactive scatterplot and table.

10.2 Module eigengene boxplot

boxME(METraits=METraits, module=ModName, ascisse='Condition', title=NULL, 
      lines='Yes', reference='DMSO')

10.3 Module membership boxplot

boxMM(GeneMM=GeneMetrics, module=ModName, title=NULL)

10.4 Module heatmap

sechm(SE_WGCNA, features=GeneMetrics[GeneMetrics$Module==ModName, ]$Gene, assayName="vst", gaps_at="Condition",
      top_annotation='Condition', hmcols=ScaledCols,  do.scale=TRUE, breaks=0.8, 
      anno_colors = metadata(SE_WGCNA)$anno_colors)

10.5 Interactive Scatterplot

plotGeneMetrics(GeneMetrics, module=ModName, title=NULL, top=TopGenes)

10.6 Interactive table

interactiveMetrics(GeneMetrics, Module=ModName)

11. Black Module

11.1 Module definition

ModName <- 'black'
TopGenes <- ifelse(dim(dplyr::filter(GeneMetrics, Module==ModName))[1] > 250, 250,
                   dim(dplyr::filter(GeneMetrics, Module==ModName))[1])

The module contains 533 genes: 473 protein-coding and 60 lncRNAs (13 %).

The top-250 genes according to intramodular connectivity will be shown in interactive scatterplot and table.

11.2 Module eigengene boxplot

boxME(METraits=METraits, module=ModName, ascisse='Condition', title=NULL, 
      lines='Yes', reference='DMSO')

11.3 Module membership boxplot

boxMM(GeneMM=GeneMetrics, module=ModName, title=NULL)

11.4 Module heatmap

sechm(SE_WGCNA, features=GeneMetrics[GeneMetrics$Module==ModName, ]$Gene, assayName="vst", gaps_at="Condition",
      top_annotation='Condition', hmcols=ScaledCols,  do.scale=TRUE, breaks=0.8, 
      anno_colors = metadata(SE_WGCNA)$anno_colors)

11.5 Interactive Scatterplot

plotGeneMetrics(GeneMetrics, module=ModName, title=NULL, top=TopGenes)

11.6 Interactive table

interactiveMetrics(GeneMetrics, Module=ModName)

12. Pink Module

12.1 Module definition

ModName <- 'pink'
TopGenes <- ifelse(dim(dplyr::filter(GeneMetrics, Module==ModName))[1] > 250, 250,
                   dim(dplyr::filter(GeneMetrics, Module==ModName))[1])

The module contains 498 genes: 436 protein-coding and 62 lncRNAs (14 %).

The top-250 genes according to intramodular connectivity will be shown in interactive scatterplot and table.

12.2 Module eigengene boxplot

boxME(METraits=METraits, module=ModName, ascisse='Condition', title=NULL, 
      lines='Yes', reference='DMSO')

12.3 Module membership boxplot

boxMM(GeneMM=GeneMetrics, module=ModName, title=NULL)

12.4 Module heatmap

sechm(SE_WGCNA, features=GeneMetrics[GeneMetrics$Module==ModName, ]$Gene, assayName="vst", gaps_at="Condition",
      top_annotation='Condition', hmcols=ScaledCols,  do.scale=TRUE, breaks=0.8, 
      anno_colors = metadata(SE_WGCNA)$anno_colors)

12.5 Interactive Scatterplot

plotGeneMetrics(GeneMetrics, module=ModName, title=NULL, top=TopGenes)

12.6 Interactive table

interactiveMetrics(GeneMetrics, Module=ModName)

13. Magenta Module

13.1 Module definition

ModName <- 'magenta'
TopGenes <- ifelse(dim(dplyr::filter(GeneMetrics, Module==ModName))[1] > 250, 250,
                   dim(dplyr::filter(GeneMetrics, Module==ModName))[1])

The module contains 470 genes: 400 protein-coding and 70 lncRNAs (18 %).

The top-250 genes according to intramodular connectivity will be shown in interactive scatterplot and table.

13.2 Module eigengene boxplot

boxME(METraits=METraits, module=ModName, ascisse='Condition', title=NULL, 
      lines='Yes', reference='DMSO')

13.3 Module membership boxplot

boxMM(GeneMM=GeneMetrics, module=ModName, title=NULL)

13.4 Module heatmap

sechm(SE_WGCNA, features=GeneMetrics[GeneMetrics$Module==ModName, ]$Gene, assayName="vst", gaps_at="Condition",
      top_annotation='Condition', hmcols=ScaledCols,  do.scale=TRUE, breaks=0.8, 
      anno_colors = metadata(SE_WGCNA)$anno_colors)

13.5 Interactive Scatterplot

plotGeneMetrics(GeneMetrics, module=ModName, title=NULL, top=TopGenes)

13.6 Interactive table

interactiveMetrics(GeneMetrics, Module=ModName)

14. Purple Module

14.1 Module definition

ModName <- 'purple'
TopGenes <- ifelse(dim(dplyr::filter(GeneMetrics, Module==ModName))[1] > 250, 250,
                   dim(dplyr::filter(GeneMetrics, Module==ModName))[1])

The module contains 413 genes: 360 protein-coding and 53 lncRNAs (15 %).

The top-250 genes according to intramodular connectivity will be shown in interactive scatterplot and table.

14.2 Module eigengene boxplot

boxME(METraits=METraits, module=ModName, ascisse='Condition', title=NULL, 
      lines='Yes', reference='DMSO')

14.3 Module membership boxplot

boxMM(GeneMM=GeneMetrics, module=ModName, title=NULL)

14.4 Module heatmap

sechm(SE_WGCNA, features=GeneMetrics[GeneMetrics$Module==ModName, ]$Gene, assayName="vst", gaps_at="Condition",
      top_annotation='Condition', hmcols=ScaledCols,  do.scale=TRUE, breaks=0.8, 
      anno_colors = metadata(SE_WGCNA)$anno_colors)

14.5 Interactive Scatterplot

plotGeneMetrics(GeneMetrics, module=ModName, title=NULL, top=TopGenes)

14.6 Interactive table

interactiveMetrics(GeneMetrics, Module=ModName)

15. Greenyellow Module

15.1 Module definition

ModName <- 'greenyellow'
TopGenes <- ifelse(dim(dplyr::filter(GeneMetrics, Module==ModName))[1] > 250, 250,
                   dim(dplyr::filter(GeneMetrics, Module==ModName))[1])

The module contains 353 genes: 307 protein-coding and 46 lncRNAs (15 %).

The top-250 genes according to intramodular connectivity will be shown in interactive scatterplot and table.

15.2 Module eigengene boxplot

boxME(METraits=METraits, module=ModName, ascisse='Condition', title=NULL, 
      lines='Yes', reference='DMSO')

15.3 Module membership boxplot

boxMM(GeneMM=GeneMetrics, module=ModName, title=NULL)

15.4 Module heatmap

sechm(SE_WGCNA, features=GeneMetrics[GeneMetrics$Module==ModName, ]$Gene, assayName="vst", gaps_at="Condition",
      top_annotation='Condition', hmcols=ScaledCols,  do.scale=TRUE, breaks=0.8, 
      anno_colors = metadata(SE_WGCNA)$anno_colors)

15.5 Interactive Scatterplot

plotGeneMetrics(GeneMetrics, module=ModName, title=NULL, top=TopGenes)

15.6 Interactive table

interactiveMetrics(GeneMetrics, Module=ModName)

16. Tan Module

16.1 Module definition

ModName <- 'tan'
TopGenes <- ifelse(dim(dplyr::filter(GeneMetrics, Module==ModName))[1] > 250, 250,
                   dim(dplyr::filter(GeneMetrics, Module==ModName))[1])

The module contains 348 genes: 297 protein-coding and 51 lncRNAs (17 %).

The top-250 genes according to intramodular connectivity will be shown in interactive scatterplot and table.

16.2 Module eigengene boxplot

boxME(METraits=METraits, module=ModName, ascisse='Condition', title=NULL, 
      lines='Yes', reference='DMSO')

16.3 Module membership boxplot

boxMM(GeneMM=GeneMetrics, module=ModName, title=NULL)

16.4 Module heatmap

sechm(SE_WGCNA, features=GeneMetrics[GeneMetrics$Module==ModName, ]$Gene, assayName="vst", gaps_at="Condition",
      top_annotation='Condition', hmcols=ScaledCols,  do.scale=TRUE, breaks=0.8, 
      anno_colors = metadata(SE_WGCNA)$anno_colors)

16.5 Interactive Scatterplot

plotGeneMetrics(GeneMetrics, module=ModName, title=NULL, top=TopGenes)

16.6 Interactive table

interactiveMetrics(GeneMetrics, Module=ModName)

17. Cyan Module

17.1 Module definition

ModName <- 'cyan'
TopGenes <- ifelse(dim(dplyr::filter(GeneMetrics, Module==ModName))[1] > 250, 250,
                   dim(dplyr::filter(GeneMetrics, Module==ModName))[1])

The module contains 312 genes: 263 protein-coding and 49 lncRNAs (19 %).

The top-250 genes according to intramodular connectivity will be shown in interactive scatterplot and table.

17.2 Module eigengene boxplot

boxME(METraits=METraits, module=ModName, ascisse='Condition', title=NULL, 
      lines='Yes', reference='DMSO')

17.3 Module membership boxplot

boxMM(GeneMM=GeneMetrics, module=ModName, title=NULL)

17.4 Module heatmap

sechm(SE_WGCNA, features=GeneMetrics[GeneMetrics$Module==ModName, ]$Gene, assayName="vst", gaps_at="Condition",
      top_annotation='Condition', hmcols=ScaledCols,  do.scale=TRUE, breaks=0.8, 
      anno_colors = metadata(SE_WGCNA)$anno_colors)

17.5 Interactive Scatterplot

plotGeneMetrics(GeneMetrics, module=ModName, title=NULL, top=TopGenes)

17.6 Interactive table

interactiveMetrics(GeneMetrics, Module=ModName)

18. Grey60 Module

18.1 Module definition

ModName <- 'grey60'
TopGenes <- ifelse(dim(dplyr::filter(GeneMetrics, Module==ModName))[1] > 250, 250,
                   dim(dplyr::filter(GeneMetrics, Module==ModName))[1])

The module contains 166 genes: 144 protein-coding and 22 lncRNAs (15 %).

The top-166 genes according to intramodular connectivity will be shown in interactive scatterplot and table.

18.2 Module eigengene boxplot

boxME(METraits=METraits, module=ModName, ascisse='Condition', title=NULL, 
      lines='Yes', reference='DMSO')

18.3 Module membership boxplot

boxMM(GeneMM=GeneMetrics, module=ModName, title=NULL)

18.4 Module heatmap

sechm(SE_WGCNA, features=GeneMetrics[GeneMetrics$Module==ModName, ]$Gene, assayName="vst", gaps_at="Condition",
      top_annotation='Condition', hmcols=ScaledCols,  do.scale=TRUE, breaks=0.8, 
      anno_colors = metadata(SE_WGCNA)$anno_colors)

18.5 Interactive Scatterplot

plotGeneMetrics(GeneMetrics, module=ModName, title=NULL, top=TopGenes)

18.6 Interactive table

interactiveMetrics(GeneMetrics, Module=ModName)

19. Lightgreen Module

19.1 Module definition

ModName <- 'lightgreen'
TopGenes <- ifelse(dim(dplyr::filter(GeneMetrics, Module==ModName))[1] > 250, 250,
                   dim(dplyr::filter(GeneMetrics, Module==ModName))[1])

The module contains 160 genes: 126 protein-coding and 34 lncRNAs (27 %).

The top-160 genes according to intramodular connectivity will be shown in interactive scatterplot and table.

19.2 Module eigengene boxplot

boxME(METraits=METraits, module=ModName, ascisse='Condition', title=NULL, 
      lines='Yes', reference='DMSO')

19.3 Module membership boxplot

boxMM(GeneMM=GeneMetrics, module=ModName, title=NULL)

19.4 Module heatmap

sechm(SE_WGCNA, features=GeneMetrics[GeneMetrics$Module==ModName, ]$Gene, assayName="vst", gaps_at="Condition",
      top_annotation='Condition', hmcols=ScaledCols,  do.scale=TRUE, breaks=0.8, 
      anno_colors = metadata(SE_WGCNA)$anno_colors)

19.5 Interactive Scatterplot

plotGeneMetrics(GeneMetrics, module=ModName, title=NULL, top=TopGenes)

19.6 Interactive table

interactiveMetrics(GeneMetrics, Module=ModName)

20. Lightyellow Module

20.1 Module definition

ModName <- 'lightyellow'
TopGenes <- ifelse(dim(dplyr::filter(GeneMetrics, Module==ModName))[1] > 250, 250,
                   dim(dplyr::filter(GeneMetrics, Module==ModName))[1])

The module contains 117 genes: 103 protein-coding and 14 lncRNAs (14 %).

The top-117 genes according to intramodular connectivity will be shown in interactive scatterplot and table.

20.2 Module eigengene boxplot

boxME(METraits=METraits, module=ModName, ascisse='Condition', title=NULL, 
      lines='Yes', reference='DMSO')

20.3 Module membership boxplot

boxMM(GeneMM=GeneMetrics, module=ModName, title=NULL)

20.4 Module heatmap

sechm(SE_WGCNA, features=GeneMetrics[GeneMetrics$Module==ModName, ]$Gene, assayName="vst", gaps_at="Condition",
      top_annotation='Condition', hmcols=ScaledCols,  do.scale=TRUE, breaks=0.8, 
      anno_colors = metadata(SE_WGCNA)$anno_colors)

20.5 Interactive Scatterplot

plotGeneMetrics(GeneMetrics, module=ModName, title=NULL, top=TopGenes)

20.6 Interactive table

interactiveMetrics(GeneMetrics, Module=ModName)

21. Royalblue Module

21.1 Module definition

ModName <- 'royalblue'
TopGenes <- ifelse(dim(dplyr::filter(GeneMetrics, Module==ModName))[1] > 250, 250,
                   dim(dplyr::filter(GeneMetrics, Module==ModName))[1])

The module contains 109 genes: 86 protein-coding and 23 lncRNAs (27 %).

The top-109 genes according to intramodular connectivity will be shown in interactive scatterplot and table.

21.2 Module eigengene boxplot

boxME(METraits=METraits, module=ModName, ascisse='Condition', title=NULL, 
      lines='Yes', reference='DMSO')

21.3 Module membership boxplot

boxMM(GeneMM=GeneMetrics, module=ModName, title=NULL)

21.4 Module heatmap

sechm(SE_WGCNA, features=GeneMetrics[GeneMetrics$Module==ModName, ]$Gene, assayName="vst", gaps_at="Condition",
      top_annotation='Condition', hmcols=ScaledCols,  do.scale=TRUE, breaks=0.8, 
      anno_colors = metadata(SE_WGCNA)$anno_colors)

21.5 Interactive Scatterplot

plotGeneMetrics(GeneMetrics, module=ModName, title=NULL, top=TopGenes)

21.6 Interactive table

interactiveMetrics(GeneMetrics, Module=ModName)

22. Darkred Module

22.1 Module definition

ModName <- 'darkred'
TopGenes <- ifelse(dim(dplyr::filter(GeneMetrics, Module==ModName))[1] > 250, 250,
                   dim(dplyr::filter(GeneMetrics, Module==ModName))[1])

The module contains 94 genes: 74 protein-coding and 20 lncRNAs (27 %).

The top-94 genes according to intramodular connectivity will be shown in interactive scatterplot and table.

22.2 Module eigengene boxplot

boxME(METraits=METraits, module=ModName, ascisse='Condition', title=NULL, 
      lines='Yes', reference='DMSO')

22.3 Module membership boxplot

boxMM(GeneMM=GeneMetrics, module=ModName, title=NULL)

22.4 Module heatmap

sechm(SE_WGCNA, features=GeneMetrics[GeneMetrics$Module==ModName, ]$Gene, assayName="vst", gaps_at="Condition",
      top_annotation='Condition', hmcols=ScaledCols,  do.scale=TRUE, breaks=0.8, 
      anno_colors = metadata(SE_WGCNA)$anno_colors)

22.5 Interactive Scatterplot

plotGeneMetrics(GeneMetrics, module=ModName, title=NULL, top=TopGenes)

22.6 Interactive table

interactiveMetrics(GeneMetrics, Module=ModName)

23. Savings

save(GeneMetrics, file=paste0(OutputFolder, 'GeneMetrics.RData'))
write.table(GeneMetrics, file=paste0(OutputFolder, 'GeneMetrics.txt'), sep='\t')
SessionInfo <- sessionInfo()
Date <- date()
save.image(paste0(OutputFolder, 'ModuleCharacterization.RData'))
Date
## [1] "Mon Jul 21 19:40:03 2025"
SessionInfo
## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] SummarizedExperiment_1.28.0 Biobase_2.58.0             
##  [3] GenomicRanges_1.50.2        GenomeInfoDb_1.34.9        
##  [5] IRanges_2.32.0              S4Vectors_0.36.1           
##  [7] BiocGenerics_0.44.0         MatrixGenerics_1.10.0      
##  [9] matrixStats_0.63.0          WGCNA_1.72-1               
## [11] fastcluster_1.2.3           dynamicTreeCut_1.63-1      
## [13] sechm_1.6.0                 viridis_0.6.2              
## [15] viridisLite_0.4.1           DT_0.27                    
## [17] ggplot2_3.4.1               dplyr_1.1.0                
## 
## loaded via a namespace (and not attached):
##   [1] Rtsne_0.16             colorspace_2.1-0       rjson_0.2.21          
##   [4] deldir_1.0-6           ellipsis_0.3.2         circlize_0.4.15       
##   [7] htmlTable_2.4.1        XVector_0.38.0         GlobalOptions_0.1.2   
##  [10] base64enc_0.1-3        clue_0.3-64            rstudioapi_0.14       
##  [13] farver_2.1.1           bit64_4.0.5            AnnotationDbi_1.60.0  
##  [16] fansi_1.0.4            codetools_0.2-19       splines_4.2.1         
##  [19] doParallel_1.0.17      impute_1.72.3          cachem_1.0.7          
##  [22] knitr_1.42             Formula_1.2-5          jsonlite_1.8.4        
##  [25] cluster_2.1.4          GO.db_3.16.0           png_0.1-8             
##  [28] compiler_4.2.1         httr_1.4.5             backports_1.4.1       
##  [31] lazyeval_0.2.2         Matrix_1.5-3           fastmap_1.1.1         
##  [34] cli_3.6.1              htmltools_0.5.4        tools_4.2.1           
##  [37] gtable_0.3.1           glue_1.6.2             GenomeInfoDbData_1.2.9
##  [40] V8_4.2.2               Rcpp_1.0.10            jquerylib_0.1.4       
##  [43] vctrs_0.6.2            Biostrings_2.66.0      preprocessCore_1.60.2 
##  [46] crosstalk_1.2.0        iterators_1.0.14       xfun_0.37             
##  [49] stringr_1.5.0          lifecycle_1.0.3        ca_0.71.1             
##  [52] zlibbioc_1.44.0        scales_1.2.1           TSP_1.2-2             
##  [55] parallel_4.2.1         RColorBrewer_1.1-3     ComplexHeatmap_2.14.0 
##  [58] yaml_2.3.7             curl_5.0.0             memoise_2.0.1         
##  [61] gridExtra_2.3          sass_0.4.5             rpart_4.1.19          
##  [64] latticeExtra_0.6-30    stringi_1.7.12         RSQLite_2.3.0         
##  [67] highr_0.10             randomcoloR_1.1.0.1    foreach_1.5.2         
##  [70] checkmate_2.1.0        seriation_1.4.1        shape_1.4.6           
##  [73] rlang_1.1.1            pkgconfig_2.0.3        bitops_1.0-7          
##  [76] evaluate_0.20          lattice_0.20-45        purrr_1.0.1           
##  [79] labeling_0.4.2         htmlwidgets_1.6.1      bit_4.0.5             
##  [82] tidyselect_1.2.0       magrittr_2.0.3         R6_2.5.1              
##  [85] generics_0.1.3         Hmisc_4.8-0            DelayedArray_0.24.0   
##  [88] DBI_1.1.3              pillar_1.8.1           foreign_0.8-84        
##  [91] withr_2.5.0            survival_3.5-3         KEGGREST_1.38.0       
##  [94] RCurl_1.98-1.10        nnet_7.3-18            tibble_3.2.1          
##  [97] crayon_1.5.2           interp_1.1-3           utf8_1.2.3            
## [100] plotly_4.10.1          rmarkdown_2.20         jpeg_0.1-10           
## [103] GetoptLong_1.0.5       grid_4.2.1             data.table_1.14.8     
## [106] blob_1.2.3             digest_0.6.31          tidyr_1.3.0           
## [109] munsell_0.5.0          registry_0.5-1         bslib_0.4.2