1. 安装加载DEseq2包

rm(list = ls())
# BiocManager::install('DESeq2') 
library('DESeq2')

2. 载入数据

count <- read.csv(file = "../gene_sample_count_with_symbol.csv",header = T,row.names = 1)
head(count)
> head(count)
                   GFP3 GFP4 NLS_10 NLS_5
ENSMUSG00000028180 2572 2916   2484  3173
ENSMUSG00000028182   26   23     13    20
ENSMUSG00000028185    5    0      0     0
ENSMUSG00000028184  613  463    646   879
ENSMUSG00000028187  973  765    815  1067
ENSMUSG00000028186  191    5      7    11

3.构建分组矩阵

group <- read.csv(file = "../group.csv",header = T,row.names = 1)
as.factor(group$Group)
> group
        Group
GFP3   AAVGFP
GFP4   AAVGFP
NLS_10 AAVNLS
NLS_5  AAVNLS

4.制作dds对象，构建差异基因分析所需的数据格式

dds <- DESeqDataSetFromMatrix(countData=count, colData=group, design=~Group)

5.进行差异分析及ID转换

dds <- DESeq(dds)
result <- as.data.frame(results(dds)) # results()从DESeq分析中提取出结果表
result$ENSEMBL <- rownames(result)
ens <- rownames(result)
#加载clusterProfiler
library(clusterProfiler)
ens2s = bitr(ens, fromType="ENSEMBL", toType="SYMBOL", OrgDb="org.Mm.eg.db")
ens2s
result <- merge(result,ens2s,by="ENSEMBL")

6.增加上调和下调阈值

result$type= ifelse(result$pvalue < 0.05 & result$log2FoldChange > 0.59,"up",
(ifelse(result$pvalue < 0.05 & result$log2FoldChange < -0.59,"down","not")))
write.csv(result,'result1.csv',row.names = TRUE)

7.提取显著差异表达基因的矩阵

DGE <-subset(result,pvalue < 0.05 & (log2FoldChange > 0.59 | log2FoldChange < -0.59))
DEG <- DGE[order(DGE$log2FoldChange),]
write.csv(DEG,'DEG.csv',row.names = TRUE)