1])ID=sapply(ids,function(x){x[,2]})file2id=data.frame(file_name=meta$file_name,ID=ID)head(file2id$file_name)head(count_files)count_files2=stringr::str_split(count_files,"/",simplify=T)[,2]count_files2[1]%in%file2id$
几天前,曾老师在群里给我布置了一份学徒作业,比较不同流程(limma/voom,edgeR,DESeq2 )差异分析的区别,拟使用的数据集是TCGA-BRCA的counts值矩阵。作为非肿瘤口的生信新人,秉着无知者无畏的态度试了一试。以下是具体过程。 代码主要来源于小洁老师(不是我吹,听了小洁老师的课,傻子也能学会R代码)。 R包安装#...
limma_voom_DEG <- DEG 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$change)), row.names = c("down","not","up") );tj save(DESeq2_DEG,edgeR_DEG,limma_voom_DEG,grou...
limma_voom_DEG <- DEG tj = data.frame(deseq2 = as.integer(table(DESeq2_DEG$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,grou...