RNA-seq——四、根据序列比对结果筛选差异基因

写在前面——经过前面的一系列分析，我们得到了几个counts数据，接下来就需要根据这些数据来进行分析。本文使用Rstudio，从序列比对结果中筛选出差异基因，目的是（根据不同基因的表达量）找出实验组与对照组的差异。

本文使用的数据见RNA-seq——上游分析练习（数据下载+hisat2+samtools+htseq-count）
参考：
RNA-seq(6): reads计数，合并矩阵并进行注释
RNA-seq(7): DEseq2筛选差异表达基因并注释(bioMart)

1. 合并矩阵并进行注释

rm(list = ls())
options(stringsAsFactors = FALSE)

# 读取数据
control_1 <- read.table("SRR3589959.count", col.names = c("gene_id", "control_1"))
control_2 <- read.table("SRR3589961.count", col.names = c("gene_id", "control_2"))
treat_1 <- read.table("SRR3589960.count", col.names = c("gene_id", "treat_1"))
treat_2 <- read.table("SRR3589962.count", col.names = c("gene_id", "treat_2"))

# 将数据合并
raw_count <- merge(merge(control_1, control_2, by = "gene_id"), 
                   merge(treat_1, treat_2, by = "gene_id"))
# head(raw_count)

# 去除无用信息
raw_count_filt <- raw_count[-1:-5,]

# 修改行名
ENSEMBL <- gsub("\\.\\d*", "", raw_count_filt$gene_id)
row.names(raw_count_filt) <- ENSEMBL
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

处理完的raw_count_filt：

# 将ensembl_gene_id转化为gene_symbol
library(biomaRt)
library(curl)

my_ensembl_gene_id <- row.names(raw_count_filt)

mart <- useDataset("mmusculus_gene_ensembl", useMart("ensembl"))
mms_symbols <- getBM(attributes = c("ensembl_gene_id", "external_gene_name", 
                                    "description"), 
                     filters = "ensembl_gene_id", values = my_ensembl_gene_id, 
                     mart = mart)

# 方便按照ensembl_gene_id来合并两个数据集
raw_count_filt <- cbind(ENSEMBL, raw_count_filt)
colnames(raw_count_filt)[1] <- c("ensembl_gene_id")

# 合并
readcount <- merge(raw_count_filt, mms_symbols, by = "ensembl_gene_id")

# 保存
write.csv(readcount, file = "readcount.csv")

# 查看Akap8的表达情况
readcount[readcount$external_gene_name=="Akap8",]

# 整理数据，方便后续使用
rownames(readcount) <- readcount$ensembl_gene_id
mycounts <- readcount[,3:6]
write.csv(mycounts, file = "mycounts.csv")
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29

整理好的数据包含Ensembl ID以及每组的基因比对结果。

2. 筛选差异基因（DESeq2）

rm(list = ls())

library(tidyverse)
library(DESeq2)

# 读取上一步整理好的数据
mycounts <- read.csv("mycounts.csv")
rownames(mycounts) <- mycounts$X
mycounts <- mycounts[,-1]

# 设置factor
condition <- factor(c(rep("control", 2), rep("treat", 2)), levels = c("control", "treat"))

# 生成colData
colData <- data.frame(row.names = colnames(mycounts), condition)

# 使用DESeq2分析
dds <- DESeqDataSetFromMatrix(mycounts, colData, design = ~ condition)
dds <- DESeq(dds)

res <- results(dds, contrast = c("condition", "control", "treat"))
res <- res[order(res$pvalue),]
# summary(res)
# table(res$padj<0.05)
write.csv(res, file = "all_results.csv")

# 筛选差异基因
# P和log2FC的值可以自己设置，值不同，筛选出来的差异基因数目也不同
diff_gene_deseq2 <- subset(res, padj < 0.05 & abs(log2FoldChange) >1)
write.csv(diff_gene_deseq2, file = "DEG_treat_vs_control.csv")

# 将Ensembl ID转化为Gene Symbol
# 方法一
# library(clusterProfiler)
# library(org.Mm.eg.db)
# 
# name <- bitr(rownames(diff_gene_deseq2), fromType = "ENSEMBL", toType = "SYMBOL",
#              OrgDb = "org.Mm.eg.db")

# 方法二
library(biomaRt)
library(curl)

my_ensembl_gene_id <- row.names(diff_gene_deseq2)

mart <- useDataset("mmusculus_gene_ensembl", useMart("ensembl"))
mms_symbols <- getBM(attributes = c("ensembl_gene_id", "external_gene_name", 
                                    "description"), 
                     filters = "ensembl_gene_id", values = my_ensembl_gene_id, 
                     mart = mart)

ensembl_gene_id <- rownames(diff_gene_deseq2)
diff_gene_deseq2 <- cbind(ensembl_gene_id, diff_gene_deseq2)
colnames(diff_gene_deseq2)[1] <- c("ensembl_gene_id")

# 得到最终结果
diff_name <- merge(diff_gene_deseq2, mms_symbols, by = "ensembl_gene_id")

diff_name[diff_name$external_gene_name=="Akap8",]
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59

至此，我们便得到了差异基因，之后就可以根据这些差异基因，进行基因的富集分析，可以更加细致的了解到实验组的变化。更重要的是，之后可以作出一些精美的图片，帮助我们发文章~~/(ㄒoㄒ)/~~