一、多个亚群各自marker基因联合进行GO以及KEGG分析
在前面几节我们已经知道各个细胞亚群的 marker 基因，接下来我们对这些marker基因进行功能注释和富集分析
读取数据
rm(list=ls())
library(Seurat)
library(gplots)
library(ggplot2)
load('sce.markers.all_10_celltype.Rdata')
ID 转换
library(clusterProfiler)
library(org.Hs.eg.db)
ids=bitr(sce.markers$gene,'SYMBOL','ENTREZID','org.Hs.eg.db') ## 将SYMBOL转成ENTREZID
sce.markers=merge(sce.markers,ids,by.x='gene',by.y='SYMBOL')
View(sce.markers)
可见已经在数据中添加ENTREZID列
kegg 注释
函数 split() 可以按照分组因子，把向量，矩阵和数据框进行适当的分组。它的返回值是一个列表，代表分组变量每个水平的观测。
gcSample=split(sce.markers$ENTREZID, sce.markers$cluster)
## KEGG
xx <- compareCluster(gcSample,
fun = "enrichKEGG",
organism = "hsa", pvalueCutoff = 0.05
)
p <- dotplot(xx)
p + theme(axis.text.x = element_text(
angle = 45,
vjust = 0.5, hjust = 0.5
))
GO 注释
## GO
xx <- compareCluster(gcSample,
fun = "enrichGO",
OrgDb = "org.Hs.eg.db",
ont = "BP",
pAdjustMethod = "BH",
pvalueCutoff = 0.01,
qvalueCutoff = 0.05
)
p <- dotplot(xx)
p + theme(axis.text.x = element_text(
angle = 45,
vjust = 0.5, hjust = 0.5
))
二、差异分析后的GO以及KEGG分析
具体差异分析方法前面已经讲过，经过差异分析后会得到上下调基因，此时可对上下调基因进行GO和KEGG分析。
读取数据
rm(list = ls())
library(Seurat)
library(ggplot2)
library(patchwork)
library(dplyr)
load(file = 'basic.sce.pbmc.Rdata')
sce=pbmc
sce = sce[, Idents(sce) %in% c("FCGR3A+ Mono", "CD14+ Mono")] # 挑选细胞
差异分析
deg=FindMarkers(object = sce,
ident.1 = 'FCGR3A+ Mono',
ident.2 = 'CD14+ Mono',
test.use='MAST' ) ## MAST在单细胞领域较为常用
head(deg)
save(deg,file = 'deg-by-MAST-for-mono-2-cluster.Rdata')
火山图
参考https://www.jianshu.com/p/ba05e790d8c3
单细胞的logFC不像普通测序那样大于1很多，但是p值小于0.05贼多，所以火山图参考一篇CNS 文章，只画了p值的分界线
degdf <- deg
degdf$symbol <- rownames(deg)
logFC_t=0
P.Value_t = 1e-28
degdf$change = ifelse(degdf$p_val_adj < P.Value_t & degdf$avg_log2FC < 0,"down",
ifelse(degdf$p_val_adj < P.Value_t & degdf$avg_log2FC > 0,"up","stable"))
ggplot(degdf, aes(avg_log2FC, -log10(p_val_adj))) +
geom_point(alpha=0.4, size=3.5, aes(color=change)) +
ylab("-log10(Pvalue)")+
scale_color_manual(values=c("blue", "grey","red"))+
geom_hline(yintercept = -log10(P.Value_t),lty=4,col="black",lwd=0.8) +
theme_bw()`
功能注释
## 获取上下调基因
gene_up=rownames(deg[deg$avg_log2FC > 0,])
gene_down=rownames(deg[deg$avg_log2FC < 0,])
## 把SYMBOL改为ENTREZID
library(org.Hs.eg.db)
gene_up=as.character(na.omit(AnnotationDbi::select(org.Hs.eg.db,
keys = gene_up,
columns = 'ENTREZID',
keytype = 'SYMBOL')[,2]))
gene_down=as.character(na.omit(AnnotationDbi::select(org.Hs.eg.db,
keys = gene_down,
columns = 'ENTREZID',
keytype = 'SYMBOL')[,2]))
library(clusterProfiler)
## 以上调基因为例，下调基因同理
## KEGG
gene_up <- unique(gene_up)
kk.up <- enrichKEGG(gene = gene_up,
organism = "hsa",
pvalueCutoff = 0.9,
qvalueCutoff = 0.9)
dotplot(kk.up)
## GO
go.up <- enrichGO(gene = gene_up,
OrgDb = org.Hs.eg.db,
ont = "BP" ,
pAdjustMethod = "BH",
pvalueCutoff = 0.99,
qvalueCutoff = 0.99,
readabl = TRUE)
dotplot(go.up)
差异分析后的GSEA分析
## 上一步差异分析得到差异基因列表deg后取出，p值和log2FC
nrDEG = deg[,c('avg_log2FC', 'p_val')]
colnames(nrDEG)=c('log2FoldChange','pvalue') ##更改列名
library(org.Hs.eg.db)
library(clusterProfiler)
## 把SYMBOL转换为ENTREZID，可能有部分丢失
gene <- bitr(rownames(nrDEG),
fromType = "SYMBOL",
toType = "ENTREZID",
OrgDb = org.Hs.eg.db)
## 基因名、ENTREZID、logFC一一对应起来
gene$logFC <- nrDEG$log2FoldChange[match(gene$SYMBOL,rownames(nrDEG))]
## 构建genelist
geneList=gene$logFC
names(geneList)=gene$ENTREZID
geneList=sort(geneList,decreasing = T) # 降序，按照logFC的值来排序
## GSEA分析
kk_gse <- gseKEGG(geneList = geneList,
organism = 'hsa',
nPerm = 1000,
minGSSize = 10,
pvalueCutoff = 0.9,
verbose = FALSE)
kk_gse=DOSE::setReadable(kk_gse, OrgDb='org.Hs.eg.db',keyType='ENTREZID')
sortkk<-kk_gse[order(kk_gse$enrichmentScore, decreasing = T),]
library(enrichplot)
gseaplot2(kk_gse,
"hsa04510",
color = "firebrick",
rel_heights=c(1, .2, .6))
## 展示排名前四的通路
gseaplot2(kk_gse, row.names(sortkk)[1:4])
## 把p值标上去
gseaplot2(kk_gse,
"hsa04510",
color = "firebrick",
rel_heights=c(1, .2, .6),
pvalue_table = TRUE)
https://mp.weixin.qq.com/s/ov-tLoxxK2laBm4NHjUS0Q
https://mp.weixin.qq.com/s/JhFJJiQL-9Z6uDq4DjsgVA
https://mp.weixin.qq.com/s/QbSgYG_y1wsrMqdzGBRrQg
