先保存，下次有時(shí)間再整理

rm(list = ls())
load(file = 'step2_select.Rdata')
# 每次都要檢測(cè)數(shù)據(jù)
dat<- as.data.frame(t(M_expr))
dat[1:4,1:4]  
library(hgu133plus2.db)
ls("package:hgu133plus2.db")
ids=toTable(hgu133plus2SYMBOL)
head(ids) 
ids$symbol

dat<- dat[rownames(dat)%in%ids$symbol,]

table(rownames(dat) %in% ids$symbol)

ids<- ids[ids$symbol%in%rownames(dat),]
ids<- ids[!duplicated(ids$symbol),]

ids$median=apply(dat,1,median)
# 
#   ids=ids[order(ids$symbol,ids$median,decreasing = T),]
#   ids=ids[!duplicated(ids$symbol),]
# dat=dat[ids$symbol,]
# rownames(dat)=ids$symbol
# dat[1:4,1:4]  
Kmeans_results$group_list[Kmeans_results$`km.res$cluster`==1]<- c('cluster1')
Kmeans_results$group_list[Kmeans_results$`km.res$cluster`==2]<- c('cluster2')
group_list<- Kmeans_results$group_list
X=dat
##############################################################################################
X<- as.matrix(X)

library(GSVA)



options(stringsAsFactors = F)
gene_Set <- read.delim("C:/Users/chenyuqiao/Desktop/LN RNAseq/Step GSVA/395gene GeneSet.txt", header=FALSE, row.names=1)
gene_Set<- as.data.frame(t(gene_Set))
gene_list<- as.list(gene_Set)

es.max <- gsva(X, gene_list, mx.diff=FALSE, verbose=FALSE, parallel.sz=1)






adjPvalueCutoff <- 0.05  #################這里要調(diào)整
logFCcutoff <- log2(2)

  table(group_list)
  dim(es.max)
  design <- model.matrix(~0+factor(group_list))
  colnames(design)=levels(factor(group_list))
  rownames(design)=colnames(es.max)
  design
  library(limma)
  contrast.matrix<-makeContrasts(paste0(unique(group_list),collapse = "-"),
                                 levels = design)
  # contrast.matrix<-makeContrasts("TNBC-noTNBC",
  #                                levels = design)
  
  contrast.matrix ##這個(gè)矩陣聲明，我們要把progres.組跟stable進(jìn)行差異分析比較
  
 
    ##step1
    fit <- lmFit(es.max,design)
    ##step2
    fit2 <- contrasts.fit(fit, contrast.matrix) 
    ##這一步很重要，大家可以自行看看效果
    
    fit2 <- eBayes(fit2)  ## default no trend !!!
    ##eBayes() with trend=TRUE
    ##step3
    res <- decideTests(fit2, p.value=adjPvalueCutoff)
    summary(res)
    tempOutput = topTable(fit2, coef=1, n=Inf)
    nrDEG = na.omit(tempOutput) 
    #write.csv(nrDEG2,"limma_notrend.results.csv",quote = F)
    head(nrDEG)
    
    data_gene_set<- as.data.frame(es.max)
    

    nrDEG=nrDEG[nrDEG$P.Value<0.5 & abs(nrDEG$logFC) > 0.1,]   #######在這里調(diào)閾值，保證輸出
    print(dim(nrDEG))

      n=rownames(nrDEG)
      match(n,rownames(nrDEG))
      data_gene_set=data_gene_set[match(n,rownames(data_gene_set)),]  ######n=rownames(nrDEG),篩選表達(dá)矩陣數(shù)據(jù)
      ac=data.frame(g=group_list)   ########對(duì)應(yīng)后面的圖中的annotation_col
      rownames(ac)=colnames(data_gene_set)
      # rownames(nrDEG)=substring(rownames(nrDEG),1,50)
      library(pheatmap)
      pheatmap(data_gene_set)
      dev.off
      pheatmap(data_gene_set)
      pheatmap::pheatmap(data_gene_set, 
                         fontsize_row = 8,height = 11,
                         annotation_col = ac,show_colnames = F,filename = '11111111111.pdf')
      
      # pheatmap::pheatmap(nrDEG, 
      #                    fontsize_row = 8,height = 11,
      #                    annotation_col = ac,show_colnames = F,
      #                    filename = '11111111111.pdf')
      #