本文是参考学习单细胞转录组基础分析七：差异基因富集分析
的学习笔记。可能根据学习情况有所改动。
此前的分析我们按转录特征把细胞分成了很多类别，例如seurat聚类分析得到的按cluster分类，singleR分析得到的按细胞类型分类，monocle分析得到的按拟时状态（state）分类。不同的细胞类型之间，有哪些表达差异基因呢，这些差异基因有特别的意义吗？
基因差异表达分析
library(Seurat)
差异基因GO富集分析
#差异基因GO富集分析
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差异基因kegg富集分析
genelist <- bitr(row.names(sig_dge.celltype), fromType="SYMBOL",
图片
> library(Seurat)
> library(tidyverse)
> library(patchwork)
> library(monocle)
> library(clusterProfiler)
> library(org.Hs.eg.db)
> rm(list=ls())
> dir.create("enrich")
Warning message:
In dir.create("enrich") : 'enrich' already exists
> scRNA <- readRDS("scRNA4.rds")
> mycds <- readRDS("mycds.rds")
> #比较cluster0和cluster1的差异表达基因
> dge.cluster <- FindMarkers(scRNA,ident.1 = 0,ident.2 = 1)
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=09s
> sig_dge.cluster <- subset(dge.cluster, p_val_adj<0.01&abs(avg_logFC)>1)
> #比较B_cell和T_cells的差异表达基因
> #B T 未定义
> dge.celltype <- FindMarkers(scRNA, ident.1 = 'B_cell', ident.2 = 'T_cells', group.by = 'celltype')
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s
> sig_dge.celltype <- subset(dge.celltype, p_val_adj<0.01&abs(avg_logFC)>1)
> #比较拟时State1和State3的差异表达基因
> p_data <- subset(pData(mycds),select='State')
> scRNAsub <- subset(scRNA, cells=row.names(p_data))
> scRNAsub <- AddMetaData(scRNAsub,p_data,col.name = 'State')
> dge.State <- FindMarkers(scRNAsub, ident.1 = 1, ident.2 = 3, group.by = 'State')
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s
> sig_dge.State <- subset(dge.State, p_val_adj<0.01&abs(avg_logFC)>1)
> #差异基因GO富集分析
> ego_ALL <- enrichGO(gene = row.names(sig_dge.celltype),
+ #universe = row.names(dge.celltype),
+ OrgDb = 'org.Hs.eg.db',
+ keyType = 'SYMBOL',
+ ont = "ALL",
+ pAdjustMethod = "BH",
+ pvalueCutoff = 0.01,
+ qvalueCutoff = 0.05)
> write.csv(ego_all,'enrich/enrichGO.csv')
Error in is.data.frame(x) : object 'ego_all' not found
> ego_all <- data.frame(ego_ALL)
> write.csv(ego_all,'enrich/enrichGO.csv')
> ego_CC <- enrichGO(gene = row.names(sig_dge.celltype),
+ #universe = row.names(dge.celltype),
+ OrgDb = 'org.Hs.eg.db',
+ keyType = 'SYMBOL',
+ ont = "CC",
+ pAdjustMethod = "BH",
+ pvalueCutoff = 0.01,
+ qvalueCutoff = 0.05)
> ego_MF <- enrichGO(gene = row.names(sig_dge.celltype),
+ #universe = row.names(dge.celltype),
+ OrgDb = 'org.Hs.eg.db',
+ keyType = 'SYMBOL',
+ ont = "MF",
+ pAdjustMethod = "BH",
+ pvalueCutoff = 0.01,
+ qvalueCutoff = 0.05)
> ego_BP <- enrichGO(gene = row.names(sig_dge.celltype),
+ #universe = row.names(dge.celltype),
+ OrgDb = 'org.Hs.eg.db',
+ keyType = 'SYMBOL',
+ ont = "BP",
+ pAdjustMethod = "BH",
+ pvalueCutoff = 0.01,
+ qvalueCutoff = 0.05)
> ego_CC@result$Description <- substring(ego_CC@result$Description,1,70)
> ego_MF@result$Description <- substring(ego_MF@result$Description,1,70)
> ego_BP@result$Description <- substring(ego_BP@result$Description,1,70)
> p_BP <- barplot(ego_BP,showCategory = 10) + ggtitle("barplot for Biological process")
> p_CC <- barplot(ego_CC,showCategory = 10) + ggtitle("barplot for Cellular component")
> p_MF <- barplot(ego_MF,showCategory = 10) + ggtitle("barplot for Molecular function")
> plotc <- p_BP/p_CC/p_MF
> p_BP
> p_CC
> p_MF
> plotc
> ggsave('enrich/enrichGO.png', plotc, width = 12,height = 10)
> genelist <- bitr(row.names(sig_dge.celltype), fromType="SYMBOL",
+ toType="ENTREZID", OrgDb='org.Hs.eg.db')
'select()' returned 1:1 mapping between keys and columns
Warning message:
In bitr(row.names(sig_dge.celltype), fromType = "SYMBOL", toType = "ENTREZID", :
2.33% of input gene IDs are fail to map...
> genelist <- pull(genelist,ENTREZID)
> ekegg <- enrichKEGG(gene = genelist, organism = 'hsa')
Reading KEGG annotation online:
Reading KEGG annotation online:
> p1 <- barplot(ekegg, showCategory=20)
> p2 <- dotplot(ekegg, showCategory=20)
wrong orderBy parameter; set to default `orderBy = "x"`
> plotc = p1/p2
> p1
> p2
> plotc
> ggsave("enrich/enrichKEGG.png", plot = plotc, width = 12, height = 10)
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