## ---- eval=FALSE----------------------------------------------------------- # RNAseq = RNAseq[apply(RNAseq,1,function(x) sum(x==0))0] = 1 # # #calculate gene significance measure for lymphocyte score (lscore) - Welch's t-Test # GS_lscore = t(sapply(1:ncol(WGCNA_matrix2),function(x)c(t.test(WGCNA_matrix2[,x]~lscore,var.equal=F)$p.value, # t.test(WGCNA_matrix2[,x]~lscore,var.equal=F)$estimate[1], # t.test(WGCNA_matrix2[,x]~lscore,var.equal=F)$estimate[2]))) # GS_lscore = cbind(GS.lscore, abs(GS_lscore[,2] - GS_lscore[,3])) # colnames(GS_lscore) = c('p_value','mean_high_lscore','mean_low_lscore', # 'effect_size(high-low score)'); rownames(GS_lscore) = colnames(WGCNA_matrix2) ## ---- eval=FALSE----------------------------------------------------------- # #reference genes = all 5000 top mad genes # ref_genes = colnames(WGCNA_matrix2) # # #create data frame for GO analysis # library(org.Hs.eg.db) # GO = toTable(org.Hs.egGO); SYMBOL = toTable(org.Hs.egSYMBOL) # GO_data_frame = data.frame(GO$go_id, GO$Evidence,SYMBOL$symbol[match(GO$gene_id,SYMBOL$gene_id)]) # # #create GOAllFrame object # library(AnnotationDbi) # GO_ALLFrame = GOAllFrame(GOFrame(GO_data_frame, organism = 'Homo sapiens')) # # #create gene set # library(GSEABase) # gsc <- GeneSetCollection(GO_ALLFrame, setType = GOCollection()) # # #perform GO enrichment analysis and save results to list - this make take several minutes # library(GEOstats) # GSEAGO = vector('list',length(unique(modules))) # for(i in 0:(length(unique(modules))-1)){ # GSEAGO[[i+1]] = summary(hyperGTest(GSEAGOHyperGParams(name = 'Homo sapiens GO', # geneSetCollection = gsc, geneIds = colnames(RNAseq)[modules==i], # universeGeneIds = ref.genes, ontology = 'BP', pvalueCutoff = 0.05, # conditional = 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