🤩 WGCNA | 值得你深入学习的生信分析方法！~（网状分析-

2023-02-14 本文已影响0人生信漫卷

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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