# # - eval = FALSE --------------------------------------------------------- # RNAseq = RNAseq[应用(RNAseq 1函数(x)和(x = = 0)) 0] = 1 # # #计算淋巴细胞评分(lscore)的基因显著性测度- Welch's t- test # GS_lscore = t(sapply(1:ncol(WGCNA_matrix2)，function(x)c(t.t test(WGCNA_matrix2[，x]~lscore,var.equal=F)$p。# t.test(WGCNA_matrix2[，x]~lscore,var.equal=F)$estimate[1]， # t.test(WGCNA_matrix2[，x]~lscore,var.equal=F)$estimate[2]))) # GS_lscore = cbind(GS. x)lscore, abs(GS_lscore[，2] - GS_lscore[，3])) # colnames(GS_lscore) = c('p_value'，'mean_high_lscore'，'mean_low_lscore'， # 'effect_size(高低分)');rownames (GS_lscore) = colnames (WGCNA_matrix2 ) ## ---- eval = FALSE --------------------------------------------------------- # # 参考基因= 5000前疯狂基因# ref_genes = colnames (WGCNA_matrix2) # # #创建数据框架去分析#库(org.Hs.eg.db) # = toTable (org.Hs.egGO);SYMBOL = toTable(org. h . egsymbol) # GO_data_frame = data.frame(GO$go_id, GO$Evidence,SYMBOL$ SYMBOL [match(GO$gene_id,SYMBOL$gene_id)]) # # #创建GOAllFrame对象# library(AnnotationDbi) # GO_ALLFrame = GOAllFrame(GOFrame(GO_data_frame, organism = 'Homo sapiens')) # # #创建基因集# library(GSEABase) # gsc <- GeneSetCollection(GO_ALLFrame，setType = GOCollection()) # # #执行去富集分析和结果保存到列表——这使花几分钟#库(GEOstats) # GSEAGO =向量(“列表”,长度(独特(模块)))#,(我在0(长度(独特(模块))1)){# GSEAGO [[i + 1]] =总结(hyperGTest (GSEAGOHyperGParams (name =“智人”,# geneSetCollection = gsc geneIds = colnames (RNAseq)(模块= =我),# universeGeneIds = ref.genes本体=“英国石油公司”,pvalueCutoff = 0.05, #条件= FALSE,testDirection = 'over'))) # print(i) #} # cutoff_size = 100 # # GO_module_name = rep(NA,length(unique(modules)))) # for (i in 1:length(unique(modules))){# GO.module.name[i] = # GSEAGO[[i]][GSEAGO[[i]]$Size .name $Size .name = # GSEAGO[[i]