#差异分析的套路都是差不多的，大部分设计思想都是继承limma这个包，DESeq2也不例外。
1.
5
3
```{r message = FALSE,warning = FALSE}
#读取数据
library(limma)
library(Seurat)
library(dplyr)
library(magrittr)

读取文件，并对重复基因取均值

rt=read.table("geneMatrix.txt",sep="\t",header=T,check.names=F)
rt=as.matrix(rt)
rownames(rt)=rt[,1]
exp=rt[,2:ncol(rt)]
dimnames=list(rownames(exp),colnames(exp))
data=matrix(as.numeric(as.matrix(exp)),nrow=nrow(exp),dimnames=dimnames)
data=avereps(data)
data[1:10,1:6]

image.png

将矩阵转换为Seurat对象，并对数据进行过滤

pbmc <- CreateSeuratObject(counts = data,project = "seurat", min.cells = 3, min.features = 50, names.delim = "_",)
#使用PercentageFeatureSet函数计算线粒体基因的百分比
pbmc[["percent.mt"]] <- PercentageFeatureSet(object = pbmc, pattern = "^MT-")
VlnPlot(object = pbmc, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

image.png