写在前面
之前我们完成了WGCNA输入数据的清洗，网络构建和模块识别。😘
而且还介绍了如何对大型数据分级处理，有效地减少了内存的负担。😷
接着就是最重要的环节了，将不同module与表型或者临床特征相联系，进一步鉴定出有意义的module，并进行module内部的分析，筛选重要基因。🤒
不得不说，东西还是挺多的，而且非常重要，我们一起来试一下吧。🥰
用到的包
rm(list = ls())
library(WGCNA)
library(tidyverse)
示例数据
load("FemaleLiver-01-dataInput.RData")
load("FemaleLiver-02-networkConstruction-auto.RData")
模块与外部特征关联
这里我们需要将module和traits联系起来，并且采用量化的方式。😘
4.1 量化模块与特征之间的关系
这里我们需要对模块的eigengenes进行提取，并与traits进行相关性分析。🧐
nGenes <- ncol(datExpr)
nSamples <- nrow(datExpr)
MEs0 <- moduleEigengenes(datExpr, moduleColors)$eigengenes
MEs <- orderMEs(MEs0)
moduleTraitCor <- cor(MEs, datTraits, use = "p")
moduleTraitPvalue <- corPvalueStudent(moduleTraitCor, nSamples)
用相关性矩阵可视化一下吧。😘
sizeGrWindow(10,6)
textMatrix = paste(signif(moduleTraitCor, 2), "\n(",
signif(moduleTraitPvalue, 1), ")", sep = "");
dim(textMatrix) = dim(moduleTraitCor)
par(mar = c(6, 8.5, 3, 3))
labeledHeatmap(Matrix = moduleTraitCor,
xLabels = names(datTraits),
yLabels = names(MEs),
ySymbols = names(MEs),
colorLabels = FALSE,
colors = greenWhiteRed(50),
textMatrix = textMatrix,
setStdMargins = FALSE,
cex.text = 0.5,
zlim = c(-1,1),
main = paste("Module-trait relationships"))
4.2 计算Gene Significance 和 Module Membership
1️⃣ 接着我们将Gene Significance（GS） 定义为量化基因与traits之间相关性的绝对值。
2️⃣ Module Membership（MM）定义为模块的eigengene与基因表达谱之间的相关性。
这里假设我们感兴趣的是weight这个特征，想找到与weight相关的module以及其中的基因。😘
weight <- as.data.frame(datTraits$weight_g);
names(weight) <- "weight"
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="")
geneTraitSignificance <- as.data.frame(cor(datExpr, weight, use = "p"))
GSPvalue <- as.data.frame(corPvalueStudent(as.matrix(geneTraitSignificance), nSamples))
names(geneTraitSignificance) <- paste("GS.", names(weight), sep="")
names(GSPvalue) <- paste("p.GS.", names(weight), sep="")
4.3 模块内部分析
对于我们找到的有意义的模块，可以进一步的分析模块内部的基因，具体是哪个基因在其中更为重要。😉
当然，这就要用到我们之前计算好的GS和MM了。😙
这里我们假设感兴趣的是magenta这个模块吧。🫶
module <- "magenta"
column <- match(module, modNames)
moduleGenes <- moduleColors==module
sizeGrWindow(7, 7)
par(mfrow = c(1,1))
verboseScatterplot(abs(geneModuleMembership[moduleGenes, column]),
abs(geneTraitSignificance[moduleGenes, 1]),
xlab = paste("Module Membership in", module, "module"),
ylab = "Gene significance for body weight",
main = paste("Module membership vs. gene significance\n"),
cex.main = 1.2, cex.lab = 1.2, cex.axis = 1.2, col = module)
4.4 批量输出
可能你也直接输出所有模块的结果，然后再挑选你需要的，那就用这段批量输出的代码吧。😘
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="")
traitNames <- names(datTraits)
geneTraitSignificance <- as.data.frame(cor(datExpr, datTraits, use = "p"))
GSPvalue <- as.data.frame(corPvalueStudent(as.matrix(geneTraitSignificance), nSamples))
names(geneTraitSignificance) <- paste("GS.", traitNames, sep="")
names(GSPvalue) <- paste("p.GS.", traitNames, sep="")
for (trait in traitNames){
traitColumn = match(trait,traitNames)
for (module2 in modNames){
column = match(module2, modNames)
moduleGenes = moduleColors==module2
if (nrow(geneModuleMembership[moduleGenes,]) > 1){
pdf(file = paste0("./module_", trait, "_", module,".pdf"),
width=7,height=7)
par(mfrow = c(1,1))
verboseScatterplot(abs(geneModuleMembership[moduleGenes, column]),
abs(geneTraitSignificance[moduleGenes, traitColumn]),
xlab = paste("Module Membership in", module, "module"),
ylab = paste("Gene significance for ",trait),
main = paste("Module membership vs. gene significance\n"),
cex.main = 1.2, cex.lab = 1.2, cex.axis = 1.2, col = module)
dev.off()
}
}
}
结果汇总输出
5.1 读入并整理注释文件
annot <- read.csv(file = "./FemaleLiver-Data/GeneAnnotation.csv");
dim(annot)
names(annot)
probes <- names(datExpr)
probes2annot <- match(probes, annot$substanceBXH)
sum(is.na(probes2annot))
5.2 整理并输出结果文件
geneInfo0 <- data.frame(substanceBXH = probes,
geneSymbol = annot$gene_symbol[probes2annot],
LocusLinkID = annot$LocusLinkID[probes2annot],
moduleColor = moduleColors,
geneTraitSignificance,
GSPvalue)
modOrder <- order(-abs(cor(MEs, weight, use = "p")))
for (mod in 1:ncol(geneModuleMembership))
{
oldNames = names(geneInfo0)
geneInfo0 = data.frame(geneInfo0, geneModuleMembership[, modOrder[mod]],
MMPvalue[, modOrder[mod]]);
names(geneInfo0) = c(oldNames, paste("MM.", modNames[modOrder[mod]], sep=""),
paste("p.MM.", modNames[modOrder[mod]], sep=""))
}
geneOrder <- order(geneInfo0$moduleColor, -abs(geneInfo0$GS.weight));
geneInfo <- geneInfo0[geneOrder, ]
write.csv(geneInfo, file = "geneInfo.csv")
DT::datatable(geneInfo)
如何引用
📍
Langfelder, P., Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008). https://doi.org/10.1186/1471-2105-9-559
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