🤩 WGCNA | 值得你深入学习的生信分析方法！~（网状分析-

2023-02-06 本文已影响0人生信漫卷

写在前面

上期我们完成了WGCNA输入数据的清洗，然后进行了样本的聚类与异常值的剔除，总体来说是非常简单的。 😘
这期我们继续完成WGCNA分析的第二步，网络构建和模块识别。🤒

用到的包

rm(list = ls())
library(WGCNA)
library(tidyverse)

示例数据

load("FemaleLiver-01-dataInput.RData")

软阈值

4.1 topology analysis

首先我们要进行soft thresholding power β的计算。🤒

powers <-  c(c(1:10), seq(from = 12, to=20, by=2))

sft <-  pickSoftThreshold(datExpr, powerVector = powers, verbose = 5)

4.2 可视化

这个时候我们可以看到最佳的soft thresholding power β为6，后面我们会用到。😚

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=0.90,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")

构建网络与模块识别（一步法）

这里我们只需要一个函数就可以完成网络构建和模块识别，即blockwiseModules，不过参数比较多，大家需要去好好理解，这里不做太多介绍了，大家看帮助文档吧。🧐

5.1 构建网络

这里会有一个maxBlockSize的参数，大家可以根据自己的电脑配置进行选择，16 GB内存最大值为20000，32 GB内存最大值为30000。🤨

net <-  blockwiseModules(datExpr, power = 6,
                         TOMType = "unsigned", 
                         minModuleSize = 30,
                         reassignThreshold = 0, mergeCutHeight = 0.25,
                         numericLabels = T, pamRespectsDendro = F,
                         saveTOMs = T,
                         saveTOMFileBase = "femaleMouseTOM",
                         verbose = 3)

5.2 查看模块数

table(net$colors)

5.3 可视化

sizeGrWindow(12,9)
mergedColors <-  labels2colors(net$colors)

plotDendroAndColors(net$dendrograms[[1]], mergedColors[net$blockGenes[[1]]], 
                    "Module colors", dendroLabels = F, hang = 0.03, addGuide = T, 
                    guideHang = 0.05)

5.4 save一下data

moduleLabels <-  net$colors

moduleColors <-  labels2colors(net$colors)

MEs <-  net$MEs

geneTree <-  net$dendrograms[[1]]

save(MEs, moduleLabels, moduleColors, geneTree, 
     file = "FemaleLiver-02-networkConstruction-auto.RData")

构建网络与模块识别（分步法）

6.1 共表达相似性与邻接距离计算

这里我们把之前算好的power应用一下。😚

softPower <-  6
adjacency <-  adjacency(datExpr, power = softPower)

6.2 计算TOM

这里我们需要把邻接矩阵转换为Topological Overlap Matrix（TOM矩阵），最大限度地减少噪音和假相关性。🧐

TOM <-  TOMsimilarity(adjacency);
dissTOM <-  1-TOM

6.3 基因聚类并可视化

geneTree <-  hclust(as.dist(dissTOM), method = "average")

plot(geneTree, 
     xlab="", sub="", 
     main = "Gene clustering on TOM-based dissimilarity", 
     labels = F, hang = 0.04)

6.4 识别模块

我们一般喜欢基因多一点的模块，这里将最小的模块设置为30。😏
补充一下 ！这里0模块是指未能归类到任意模块的基因。😷

minModuleSize <-  30

dynamicMods <-  cutreeDynamic(dendro = geneTree, distM = dissTOM,
                              deepSplit = 2, pamRespectsDendro = F,
                              minClusterSize = minModuleSize)
table(dynamicMods)

6.5 数字转为颜色并可视化

上面的模块为数字，我们需要把它转成颜色进行可视化。🤨

dynamicColors <-  labels2colors(dynamicMods)

table(dynamicColors)

sizeGrWindow(8,6)

plotDendroAndColors(geneTree, dynamicColors, 
                    "Dynamic Tree Cut",
                    dendroLabels = F, hang = 0.03,
                    addGuide = T, guideHang = 0.05,
                    main = "Gene dendrogram and module colors")

6.6 合并表达相似的模块

MEList <- moduleEigengenes (datExpr, colors = dynamicColors)
MEs <-  MEList$eigengenes

MEDiss = 1-cor(MEs);
METree <-  hclust(as.dist(MEDiss), method = "average")

6.7 合并模块的可视化

这里我们将相关性在0.75以上的模块合并在一起，当然你也可以按照你的要求来调整。🫶

sizeGrWindow(7,6)
plot(METree, main = "Clustering of module eigengenes",xlab = "", sub = "")

MEDissThres = 0.25
abline(h=MEDissThres, col = "red")
merge <-  mergeCloseModules(datExpr, dynamicColors, cutHeight = MEDissThres, verbose = 3)
mergedColors <-  merge$colors
mergedMEs <-  merge$newMEs

sizeGrWindow(12,9)
plotDendroAndColors(geneTree, cbind(dynamicColors, mergedColors), c("Dynamic Tree Cut", "Merged dynamic"), dendroLabels = F, hang = 0.03, addGuide = T, guideHang = 0.05)

moduleColors <-  mergedColors

colorOrder <-  c("grey", standardColors(50))
moduleLabels <-  match(moduleColors, colorOrder)-1
MEs <-  mergedMEs

save一下

我们save一下这个data吧，后面会再用到。😉

save(MEs, moduleLabels, moduleColors, geneTree, 
     file = "FemaleLiver-02-networkConstruction-stepByStep.RData")

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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点个在看吧各位~ ✐.ɴɪᴄᴇ ᴅᴀʏ 〰

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