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NEED TO EDIT AND CHANGE, RAW SCRIPTS FROM BERT
We are only showing the code for the head tissue, the code for the thorax tissue is mostly identical.
knitr::opts_chunk$set(echo = TRUE)packages <- c("WGCNA")
sapply(packages, library, character.only = T)
options(stringsAsFactors = FALSE);We start by loading all the files containing the read count data, selecting the needed columns, and merging all into one table.
setwd("C:/Users/Bert/Documents/Work/Papers/Transcriptomics-behavior/Revamped paper/WGCNA")
SPIC_CT1_count <- read.table("Transrate_SPIC_CT1_idxstats.tabular")
SPIC_CT2_count <- read.table("Transrate_SPIC_CT2_idxstats.tabular")
SPIC_CT3_count <- read.table("Transrate_SPIC_CT3_idxstats.tabular")
SPIC_CT4_count <- read.table("Transrate_SPIC_CT4_idxstats.tabular")
SPIC_CT5_count <- read.table("Transrate_SPIC_CT5_idxstats.tabular")
SPIC_IT1_count <- read.table("Transrate_SPIC_IT1_idxstats.tabular")
SPIC_IT2_count <- read.table("Transrate_SPIC_IT2_idxstats.tabular")
SPIC_IT3_count <- read.table("Transrate_SPIC_IT3_idxstats.tabular")
SPIC_IT4_count <- read.table("Transrate_SPIC_IT4_idxstats.tabular")
SPIC_IT5_count <- read.table("Transrate_SPIC_IT5_idxstats.tabular")
SAME_CT1_count <- read.table("Transrate_SAME_CT1_idxstats.tabular")
SAME_CT2_count <- read.table("Transrate_SAME_CT2_idxstats.tabular")
SAME_CT3_count <- read.table("Transrate_SAME_CT3_idxstats.tabular")
SAME_CT4_count <- read.table("Transrate_SAME_CT4_idxstats.tabular")
SAME_CT5_count <- read.table("Transrate_SAME_CT5_idxstats.tabular")
SAME_IT1_count <- read.table("Transrate_SAME_IT1_idxstats.tabular")
SAME_IT2_count <- read.table("Transrate_SAME_IT2_idxstats.tabular")
SAME_IT3_count <- read.table("Transrate_SAME_IT4_idxstats.tabular")
SAME_IT4_count <- read.table("Transrate_SAME_IT5_idxstats.tabular")
SAME_IT5_count <- read.table("Transrate_SAME_IT6_idxstats.tabular")
SCUB_CT1_count <- read.table("Transrate_SCUB_CT1_idxstats.tabular")
SCUB_CT2_count <- read.table("Transrate_SCUB_CT2_idxstats.tabular")
SCUB_CT3_count <- read.table("Transrate_SCUB_CT3_idxstats.tabular")
SCUB_CT4_count <- read.table("Transrate_SCUB_CT4_idxstats.tabular")
SCUB_IT1_count <- read.table("Transrate_SCUB_IT1_idxstats.tabular")
SCUB_IT2_count <- read.table("Transrate_SCUB_IT2_idxstats.tabular")
SCUB_IT3_count <- read.table("Transrate_SCUB_IT3_idxstats.tabular")
SCUB_IT5_count <- read.table("Transrate_SCUB_IT5_idxstats.tabular")
SNIT_CT1_count <- read.table("Transrate_SNIT_CT1_idxstats.tabular")
SNIT_CT3_count <- read.table("Transrate_SNIT_CT3_idxstats.tabular")
SNIT_CT5_count <- read.table("Transrate_SNIT_CT5_idxstats.tabular")
SNIT_IT1_count <- read.table("Transrate_SNIT_IT1_idxstats.tabular")
SNIT_IT2_count <- read.table("Transrate_SNIT_IT2_idxstats.tabular")
SNIT_IT3_count <- read.table("Transrate_SNIT_IT3_idxstats.tabular")
SNIT_IT4_count <- read.table("Transrate_SNIT_IT4_idxstats.tabular")
SNIT_IT5_count <- read.table("Transrate_SNIT_IT5_idxstats.tabular")
mylist = do.call("list", mget(grep("count", ls(), value=T)))
mylist2 <- mylist[sapply(mylist, function(x) dim(x)[1]) > 0]
mylist2 <- lapply(mylist, function(x) { x <- x[c(1,3)] })
Complete <- mylist2[[1]]
colnames(Complete)[2] <- print(names(mylist2)[[1]])
for(i in 2:length(mylist2)){
Complete <- merge(Complete,mylist2[[i]], by = 'V1')
colnames(Complete)[i+1] <- print(names(mylist2)[[i]])
}Now we remove any genes with an average readcount under 5
Complete_low <- Complete
row.names(Complete_low) <- Complete_low$V1
Complete_low <- Complete_low[,-c(1)]
Complete_low <- subset(transform(Complete_low, Mean=round(rowMeans(Complete_low),1)), Mean >=5)
Complete_low <- Complete_low[,-c(37)]Now we convert our read counts to the TPM format for use in WGCNA
mylist_TPM<-mylist
for(i in 1:length(mylist_TPM)){
TPM_1 <- mylist_TPM[[i]]
TPM_1$x <- TPM_1$V3/TPM_1$V2
TPM <- TPM_1[c(5)]
row.names(TPM) <- TPM_1[,1]
TPM <- t(t(TPM)*1e6/colSums(TPM))
TPM <- as.data.frame(as.table(TPM))
mylist_TPM[[i]] <- TPM[c(1,3)]
}
TPM <- mylist_TPM[[1]]
colnames(TPM)[2] <- print(names(mylist_TPM)[[1]])
for(i in 2:length(mylist_TPM)){
TPM <- merge(TPM,mylist_TPM[[i]], by = 'Var1')
colnames(TPM)[i+1] <- print(names(mylist_TPM)[[i]])
}
Complete_low$Var1 <- row.names(Complete_low)
Complete_TPM <- subset(TPM, Var1 %in% Complete_low$Var1)
row.names(Complete_TPM) <- Complete_TPM$Var1
Complete_TPM <- Complete_TPM[-c(1)]Finally, we select the top 60 % of the most varying genes in the whole dataset and convert the dataframe for use in WGCNA
# Select most varying genes over the whole dataset
DE_genes <- apply(Complete_TPM, 1, var)
DE_genes <- names(sort(DE_genes, decreasing=TRUE))[1:(nrow(Complete_TPM)*0.6)]
Complete_DE <- Complete_TPM[DE_genes,]
datExpr <- as.data.frame(t(Complete_DE));
datExpr[length(mylist),] <- as.numeric(datExpr[length(mylist),])We first set the correct soft-thresholding power
powers = c(c(1:10), seq(from = 12, to=20, by=2))
sft = pickSoftThreshold(datExpr, powerVector = powers, verbose = 5)
sizeGrWindow(9, 5)
par(mfrow = c(1,2));
cex1 = 0.9;
plot(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
xlab="Soft Threshold (power)",ylab="Scale Free Topology Model Fit,signed R^2",type="n",
main = paste("Scale independence"));
text(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
labels=powers,cex=cex1,col="red");
abline(h=0.80,col="red")
plot(sft$fitIndices[,1], sft$fitIndices[,5],
xlab="Soft Threshold (power)",ylab="Mean Connectivity", type="n",
main = paste("Mean connectivity"))
text(sft$fitIndices[,1], sft$fitIndices[,5], labels=powers, cex=cex1,col="red")Based on these graphs, we select the power.We can now run the rest of WGCNA.
Power <- 8
CutHeight <- 0.2
net = blockwiseModules(datExpr, power = Power, maxBlockSize = 36000,
TOMType = "unsigned", minModuleSize = 30,
reassignThreshold = 0, mergeCutHeight = CutHeight,
numericLabels = TRUE, pamRespectsDendro = FALSE,
verbose = 3)
mergedColors = labels2colors(net$colors)
#PlotName <- paste0("Modules_", Var, "_", CutHeight , "_",ProjectName, "_",Sys.Date(),".pdf")
#pdf(PlotName)
plotDendroAndColors(net$dendrograms[[1]], mergedColors[net$blockGenes[[1]]],
"Module colors",
dendroLabels = FALSE, hang = 0.03,
addGuide = TRUE, guideHang = 0.05)
#dev.off()
moduleLabels = net$colors
moduleColors = labels2colors(net$colors)
MEs = net$MEs;
geneTree = net$dendrograms[[1]];We now import the trait data and run some tests.
# Import trait data
traitData = read.csv("C:/Users/Bert/Documents/Work/Papers/Transcriptomics-behavior/Revamped paper/WGCNA/datTraits_50samples_20_xi_2020_thorax.csv");
femaleSamples = rownames(datExpr);
traitRows = match(femaleSamples, traitData$Grasshopper);
datTraits = traitData[traitRows, -1];
rownames(datTraits) = traitData[traitRows, 1];
# PLOT SAMPLE DENDOGRAM AND TRAIT HEAT MAP
# Re-cluster samples
sampleTree2 = hclust(dist(datExpr), method = "average")
# Convert traits to a color representation: white means low, red means high, grey means missing entry
traitColors = numbers2colors(datTraits, signed = FALSE);
# Plot the sample dendrogram and the colors underneath.
plotDendroAndColors(sampleTree2, traitColors,
groupLabels = names(datTraits),
main = "Sample dendrogram and trait heatmap")We now calculate the correlations between the phenotypic traits and the modules of co-expressed genes
nGenes = ncol(datExpr);
nSamples = nrow(datExpr);
MEs0 = moduleEigengenes(datExpr, moduleColors)$eigengenes
MEs = orderMEs(MEs0)
moduleTraitCor = cor(MEs, datTraits, use = "p");
moduleTraitPvalue = corPvalueStudent(moduleTraitCor, nSamples)
textMatrix = paste(signif(moduleTraitCor, 2), "\n(",
signif(moduleTraitPvalue, 1), ")", sep = "");
dim(textMatrix) = dim(moduleTraitCor)
par(mar = c(6, 8.5, 3, 3));
#PlotName <- paste0("Correlations3_", Var, "_", CutHeight , "_",ProjectName, "_",Sys.Date(),".pdf")
#pdf(PlotName)
labeledHeatmap(Matrix = moduleTraitCor,
xLabels = names(datTraits),
yLabels = names(MEs),
ySymbols = names(MEs),
colorLabels = TRUE,
colors = blueWhiteRed(50),
textMatrix = textMatrix,
setStdMargins = FALSE,
cex.text = 0.14,
zlim = c(-1,1),
xLabelsAngle = 90,
yLabelsAngle = 45,
font.lab.x = 0.5,
font.lab.y = 0.5,
cex.lab.x = 0.5,
cex.lab.y = 0.4,
main = paste("Module-trait relationships")
)
#dev.off()We can now show the relationship between the different modules.
MEs = moduleEigengenes(datExpr, moduleColors)$eigengenes
Distance = as.data.frame(datTraits$Moved.distance);
names(Distance) = "moved.distance"
MET = orderMEs(cbind(MEs, Distance))
sizeGrWindow(5,7.5);
par(cex = 0.9)
plotEigengeneNetworks(MET, "", marDendro = c(0,4,1,2), marHeatmap = c(3,4,1,2), cex.lab = 0.8, xLabelsAngle
= 90)
sizeGrWindow(6,6);
par(cex = 1.0)
plotEigengeneNetworks(MET, "Eigengene dendrogram", marDendro = c(0,4,2,0),
plotHeatmaps = FALSE)
par(cex = 1.0)
plotEigengeneNetworks(MET, "Eigengene adjacency heatmap", marHeatmap = c(3,4,2,2),
plotDendrograms = FALSE, xLabelsAngle = 90)Now we export the different modules.
Trait1 = as.data.frame(datTraits$Stimulus.zone.time);
names(Trait1) = "Stimulus zone time"
Trait2 = as.data.frame(datTraits$Non.stimulus.zone.time);
names(Trait2) = "Non stimulus zone time"
Trait3 = as.data.frame(datTraits$Stimulus.wall.time);
names(Trait3) = "Time on stimulus wall"
Trait4 = as.data.frame(datTraits$Moved.distance);
names(Trait4) = "Moved distance"
Trait5 = as.data.frame(datTraits$Female.pronotum.length);
names(Trait5) = "Pronotum length"
Trait6 = as.data.frame(datTraits$Female.F.C);
names(Trait6) = "F/C"
Trait7 = as.data.frame(datTraits$Black.patterning.PCA1);
names(Trait7) = "PC1 Black patterns"
geneTraitSignificance1 = as.data.frame(cor(datExpr, Trait1, use = "p"));
geneTraitSignificance2 = as.data.frame(cor(datExpr, Trait2, use = "p"));
geneTraitSignificance3 = as.data.frame(cor(datExpr, Trait3, use = "p"));
geneTraitSignificance4 = as.data.frame(cor(datExpr, Trait4, use = "p"));
geneTraitSignificance5 = as.data.frame(cor(datExpr, Trait5, use = "p"));
geneTraitSignificance6 = as.data.frame(cor(datExpr, Trait6, use = "p"));
geneTraitSignificance7 = as.data.frame(cor(datExpr, Trait7, use = "p"));
GSPvalue1 = as.data.frame(corPvalueStudent(as.matrix(geneTraitSignificance1), nSamples));
GSPvalue2 = as.data.frame(corPvalueStudent(as.matrix(geneTraitSignificance2), nSamples));
GSPvalue3 = as.data.frame(corPvalueStudent(as.matrix(geneTraitSignificance3), nSamples));
GSPvalue4 = as.data.frame(corPvalueStudent(as.matrix(geneTraitSignificance4), nSamples));
GSPvalue5 = as.data.frame(corPvalueStudent(as.matrix(geneTraitSignificance5), nSamples));
GSPvalue6 = as.data.frame(corPvalueStudent(as.matrix(geneTraitSignificance6), nSamples));
GSPvalue7 = as.data.frame(corPvalueStudent(as.matrix(geneTraitSignificance7), nSamples));
names(geneTraitSignificance1) = paste("GS.", names(Trait1), sep="");
names(GSPvalue1) = paste("p.GS.", names(Trait1), sep="");
names(geneTraitSignificance2) = paste("GS.", names(Trait2), sep="");
names(GSPvalue2) = paste("p.GS.", names(Trait2), sep="");
names(geneTraitSignificance3) = paste("GS.", names(Trait3), sep="");
names(GSPvalue3) = paste("p.GS.", names(Trait3), sep="");
names(geneTraitSignificance4) = paste("GS.", names(Trait4), sep="");
names(GSPvalue4) = paste("p.GS.", names(Trait4), sep="");
names(geneTraitSignificance5) = paste("GS.", names(Trait5), sep="");
names(GSPvalue5) = paste("p.GS.", names(Trait5), sep="");
names(geneTraitSignificance6) = paste("GS.", names(Trait6), sep="");
names(GSPvalue6) = paste("p.GS.", names(Trait6), sep="");
names(geneTraitSignificance7) = paste("GS.", names(Trait7), sep="");
names(GSPvalue7) = paste("p.GS.", names(Trait7), sep="");
modNames = substring(names(MEs), 3)
geneModuleMembership = as.data.frame(cor(datExpr, MEs, use = "p"));
MMPvalue = as.data.frame(corPvalueStudent(as.matrix(geneModuleMembership), nSamples));
names(geneModuleMembership) = paste("MM", modNames, sep="");
names(MMPvalue) = paste("p.MM", modNames, sep="");
geneInfo0 = data.frame(moduleColor = moduleColors,
geneTraitSignificance1, GSPvalue1,
geneTraitSignificance2, GSPvalue2,
geneTraitSignificance3, GSPvalue3,
geneTraitSignificance4, GSPvalue4,
geneTraitSignificance5, GSPvalue5,
geneTraitSignificance6, GSPvalue6,
geneTraitSignificance7, GSPvalue7)
modOrder = order(-abs(cor(MEs, Trait1, use = "p")));
for (mod in 1:ncol(geneModuleMembership)){
oldNames = names(geneInfo0)
geneInfo0 = data.frame(geneInfo0, geneModuleMembership[, modOrder[mod]],
MMPvalue[, modOrder[mod]]);
names(geneInfo0) = c(oldNames, paste("MM.", modNames[modOrder[mod]], sep=""),
paste("p.MM.", modNames[modOrder[mod]], sep=""))
}
geneOrder = order(geneInfo0$moduleColor, -abs(geneInfo0[2]));
geneInfo = geneInfo0[geneOrder, ]
#TableName <- paste0("geneInfo2_", CutHeight , "_", ".txt")
#write.table(geneInfo,TableName,row.names = TRUE, col.names = TRUE, quote = FALSE,sep = '\t')
#for(i in 1:length(modNames)){
# TableName <- paste0(modNames[i], "_", length(which(moduleColors==modNames[i])), "genes_", CutHeight , "_",ProjectName, ".txt")
# write.table(names(datExpr)[moduleColors==modNames[i]],TableName, row.names=FALSE, col.names=FALSE, quote = FALSE)
#}Finally, we export data for input into VisANT. We are only showing it for the module cyan.
TOM = TOMsimilarityFromExpr(datExpr, power = 6);
module = "cyan";
probes = names(datExpr)
inModule = (moduleColors==module);
modProbes = probes[inModule];
modTOM = TOM[inModule, inModule];
dimnames(modTOM) = list(modProbes, modProbes)
vis = exportNetworkToVisANT(modTOM,
file = paste("VisANTInput-", module, ".txt", sep=""),
weighted = TRUE,
threshold = 0)
sessionInfo()R version 4.3.1 (2023-06-16)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.4.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] vctrs_0.6.5 httr_1.4.7 cli_3.6.2 knitr_1.45
[5] rlang_1.1.3 xfun_0.44 stringi_1.8.4 processx_3.8.4
[9] promises_1.3.0 jsonlite_1.8.8 glue_1.7.0 rprojroot_2.0.4
[13] git2r_0.33.0 htmltools_0.5.8.1 httpuv_1.6.15 ps_1.7.6
[17] sass_0.4.9 fansi_1.0.6 rmarkdown_2.27 jquerylib_0.1.4
[21] tibble_3.2.1 evaluate_0.23 fastmap_1.2.0 yaml_2.3.8
[25] lifecycle_1.0.4 whisker_0.4.1 stringr_1.5.1 compiler_4.3.1
[29] fs_1.6.4 pkgconfig_2.0.3 Rcpp_1.0.12 rstudioapi_0.16.0
[33] later_1.3.2 digest_0.6.35 R6_2.5.1 utf8_1.2.4
[37] pillar_1.9.0 callr_3.7.6 magrittr_2.0.3 bslib_0.7.0
[41] tools_4.3.1 cachem_1.1.0 getPass_0.2-4