deseq2

rm(list = ls())
load("TCGA-stamdeg_before.Rdata")
library(stringr)
cancer_type="TCGA-stam"
exp=mRNA_exp
table(str_sub(colnames(exp),14,15))
group_list = ifelse(as.numeric(str_sub(colnames(exp),14,15)) < 10,'tumor','normal')
group_list = factor(group_list,levels = c("normal","tumor"))
table(group_list)
library(DESeq2)
colData <- data.frame(row.names =colnames(exp), 
                      condition=group_list)
if(!file.exists(paste0(cancer_type,"dd.Rdata"))){
  dds <- DESeqDataSetFromMatrix(
  countData = exp,
  colData = colData,
  design = ~ condition)
  dds <- DESeq(dds)
  save(dds,file = paste0(cancer_type,"dd.Rdata"))
}

load(file="TCGA-stamdd.Rdata")

# 两两比较
res <- results(dds, contrast = c("condition",rev(levels(group_list))))
resOrdered <- res[order(res$padj),] # 按照P值排序
DEG <- as.data.frame(resOrdered)
head(DEG)

#添加change列标记基因上调下调
#logFC_cutoff <- with(DEG,mean(abs(log2FoldChange)) + 2*sd(abs(log2FoldChange)) )
logFC_cutoff <- 1
DEG$change = as.factor(
  ifelse(DEG$padj < 0.05 & abs(DEG$log2FoldChange) > logFC_cutoff,
         ifelse(DEG$log2FoldChange> logFC_cutoff ,'UP','DOWN'),'NOT')
)
head(DEG)

table(DEG$change)
DESeq2_DEG <- DEG
save(DESeq2_DEG,file = "DESeq2_DEG.Rdata")
load(file = "DESeq2_DEG.Rdata")

edg

rm(list = ls())
load("TCGA-stamdeg_before.Rdata")
library(stringr)
cancer_type="TCGA-stam"
exp=mRNA_exp
table(str_sub(colnames(exp),14,15))
group_list = ifelse(as.numeric(str_sub(colnames(exp),14,15)) < 10,'tumor','normal')
group_list = factor(group_list,levels = c("normal","tumor"))
table(group_list)
library(edgeR)

dge <- DGEList(counts=exp,group=group_list)
dge$samples$lib.size <- colSums(dge$counts)
dge <- calcNormFactors(dge) 

design <- model.matrix(~0+group_list)
rownames(design)<-colnames(dge)
colnames(design)<-levels(group_list)

dge <- estimateGLMCommonDisp(dge,design)
dge <- estimateGLMTrendedDisp(dge, design)
dge <- estimateGLMTagwiseDisp(dge, design)

fit <- glmFit(dge, design)
fit2 <- glmLRT(fit, contrast=c(-1,1)) 

DEG=topTags(fit2, n=nrow(exp))
DEG=as.data.frame(DEG)
#logFC_cutoff <- with(DEG,mean(abs(logFC)) + 2*sd(abs(logFC)) )
logFC_cutoff <- 1
DEG$change = as.factor(
  ifelse(DEG$FDR < 0.05 & abs(DEG$logFC) > logFC_cutoff,
         ifelse(DEG$logFC > logFC_cutoff ,'UP','DOWN'),'NOT')
)
head(DEG)
table(DEG$change)
edgeR_DEG <- DEG
save(edgeR_DEG ,file = "edgeR_DEG.Rdata")
load(file = "edgeR_DEG.Rdata")

limma

rm(list = ls())
load("TCGA-stamdeg_before.Rdata")
library(stringr)
cancer_type="TCGA-stam"
exp=mRNA_exp
table(str_sub(colnames(exp),14,15))
group_list = ifelse(as.numeric(str_sub(colnames(exp),14,15)) < 10,'tumor','normal')
group_list = factor(group_list,levels = c("normal","tumor"))
table(group_list)

library(limma)

design <- model.matrix(~0+group_list)
colnames(design)=levels(group_list)
rownames(design)=colnames(exp)

dge <- DGEList(counts=exp)
dge <- calcNormFactors(dge)
logCPM <- cpm(dge, log=TRUE, prior.count=3)

v <- voom(dge,design, normalize="quantile")
fit <- lmFit(v, design)

constrasts = paste(rev(levels(group_list)),collapse = "-")
cont.matrix <- makeContrasts(contrasts=constrasts,levels = design) 
fit2=contrasts.fit(fit,cont.matrix)
fit2=eBayes(fit2)

DEG = topTable(fit2, coef=constrasts, n=Inf)
DEG = na.omit(DEG)
#logFC_cutoff <- with(DEG,mean(abs(logFC)) + 2*sd(abs(logFC)) )
logFC_cutoff <- 1
DEG$change = as.factor(
  ifelse(DEG$adj.P.Val < 0.05 & abs(DEG$logFC) > logFC_cutoff,
         ifelse(DEG$logFC > logFC_cutoff ,'UP','DOWN'),'NOT')
)
head(DEG)

#y=as.numeric(exp[rownames(DEG)[10],])
#x=group_list
#boxplot(y~x)
limma_voom_DEG <- DEG
save(limma_voom_DEG ,file = "limma_voom_DEG.Rdata")

区分

rm(list = ls())
cancer_type="TCGA-stam"
load("TCGA-stamdeg_before.Rdata")
library(stringr)
exp=mRNA_exp
table(str_sub(colnames(exp),14,15))
group_list = ifelse(as.numeric(str_sub(colnames(exp),14,15)) < 10,'tumor','normal')
group_list = factor(group_list,levels = c("normal","tumor"))
table(group_list)
cancer_type="TCGA-stam"
table(group_list)
load(file = "edgeR_DEG.Rdata")
load(file="limma_voom_DEG.Rdata")
load(file = "DESeq2_DEG.Rdata")


tj = data.frame(deseq2 = as.integer(table(DESeq2_DEG$change)),
           edgeR = as.integer(table(edgeR_DEG$change)),
           limma_voom = as.integer(table(limma_voom_DEG$change)),
           row.names = c("down","not","up")
          );tj
save(DESeq2_DEG,edgeR_DEG,limma_voom_DEG,group_list,tj,file = paste0(cancer_type,"DEG.Rdata"))

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