In Tong-Chen/YSX: For Yishengxin Training

library(WGCNA)
library(ggplot2)
library(reshape2)
library(stringr)
library(YSX)

options(stringsAsFactors = FALSE)

if (Sys.info()['sysname'] == "Linux"){
  # 打开多线程
  enableWGCNAThreads()
} else {
# if mac
  allowWGCNAThreads()
}
# 格式如前面描述
# 常规表达矩阵，log2转换后或
# Deseq2的varianceStabilizingTransformation转换的数据
# 如果有批次效应，需要事先移除，可使用removeBatchEffect
# 如果有系统偏移(可用boxplot查看基因表达分布是否一致)，
# 需要quantile normalization
exprMat <- "LiverFemaleClean.txt"

# 如果没有，设置为空
# traitData <- NULL
traitData <- "TraitsClean.txt"

wgcnaL <- WGCNA_readindata(exprMat, traitData)

datExpr <- wgcnaL$datExpr

WGCNA_dataCheck(datExpr, saveplot="WGCNA_dataCheck.pdf", width=20)

datExpr <- WGCNA_dataFilter(datExpr)

#datExpr <- WGCNA_sampleClusterDetectOutlier(datExpr)

datExpr <- WGCNA_sampleClusterDetectOutlier(datExpr, traitColors=wgcnaL$traitColors, saveplot="WGCNA_sampleClusterDetectOutlier.pdf")

power <- WGCNA_softpower(datExpr, saveplot="WGCNA_softpower.pdf")

net <- WGCNA_coexprNetwork(datExpr, power, saveplot="WGCNA_module_generation_plot.pdf")

MEs_col <- WGCNA_saveModuleAndMe(net, datExpr, saveplot="WGCNA_module_correlation_plot.pdf")

WGCNA_MEs_traitCorrelationHeatmap(MEs_col, traitData=wgcnaL$traitData, saveplot="WGCNA_moduletrait_correlation_plot.pdf")

cyt <- WGCNA_cytoscape(net, power, datExpr)

hubgene <- WGCNA_hubgene(cyt)

WGCNA_moduleTraitPlot(MEs_col, traitData=wgcnaL$traitData, saveplot="WGCNA_moduleTraitHeatmap.pdf", width=15, height=12)

geneTraitCor <- WGCNA_ModuleGeneTraitHeatmap(datExpr, traitData=wgcnaL$traitData, net=net, saveplot="WGCNA_ModuleGeneTraitHeatmap.pdf")

WGCNA_GeneModuleTraitCoorelation(datExpr, MEs_col, geneTraitCor, traitData=wgcnaL$traitData, net)