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
#基因差异表达分析
library(Seurat)
library(tidyverse)
library(patchwork)
library(monocle)
library(clusterProfiler)
library(org.Hs.eg.db)
rm(list=ls())
scRNA1 <- readRDS("scRNA1.rds")
mycds <- readRDS("mycds.rds")
#比较cluster0和cluster1的差异表达基因
dge.cluster <- FindMarkers(scRNA1,ident.1 = 0,ident.2 = 1)
colnames(dge.cluster )[2] <- 'avg_logFC'
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
##不知道为什么每次这里都出错，我记得保存了这一步的数据了呀。。。
library(SingleR)
load("D:/genetic_r/singer-cell-learning/filtered_feature_bc_matrix/ref_Human_all.RData")
refdata <- ref_Human_all
testdata <- GetAssayData(scRNA1, slot="data")
###把scRNA数据中的seurat_clusters提取出来，注意这里是因子类型的
clusters <- scRNA1@meta.data$seurat_clusters
###开始用singler分析
cellpred <- SingleR(test = testdata, ref = refdata, labels = refdata$label.main,
method = "cluster", clusters = clusters,
assay.type.test = "logcounts", assay.type.ref = "logcounts")
###制作细胞类型的注释文件
celltype = data.frame(ClusterID=rownames(cellpred), celltype=cellpred$labels, stringsAsFactors = FALSE)
##把singler的注释写到metadata中 有两种方法
scRNA1@meta.data$celltype = "NA"
for(i in 1:nrow(celltype)){
scRNA1@meta.data[which(scRNA1@meta.data$seurat_clusters == celltype$ClusterID[i]),'celltype'] <- celltype$celltype[i]}
###因为我把singler的注释加载到metadata中时候，命名的名字叫celltype，所以画图时候，group.by="celltype"
table(scRNA1@meta.data$celltype)
#B_cell Monocyte NK_cell T_cells
#175 319 53 466
dge.celltype <- FindMarkers(scRNA1, ident.1 = 'B_cell', ident.2 = 'T_cells', group.by = 'celltype')
colnames(dge.celltype)[2] <- 'avg_logFC'
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(scRNA1, 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')
colnames(dge.State)[2] <- 'avg_logFC'
sig_dge.State <- subset(dge.State, p_val_adj<0.01&abs(avg_logFC)>1)
差异基因GO富集分析
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)
ego_all <- data.frame(ego_ALL)
##电脑卡住了。。。。不运行这个了。。
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) #电脑卡住了。。暂时不画这个图
#截取每个description的前70个字符，方便后面作图排版
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") ##电脑又卡住了。。。hhh，就不画这个图了
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
差异基因kegg富集分析
genelist <- bitr(row.names(sig_dge.celltype), fromType="SYMBOL",
toType="ENTREZID", OrgDb='org.Hs.eg.db')
genelist <- pull(genelist,ENTREZID)
ekegg <- enrichKEGG(gene = genelist, organism = 'hsa')
p1 <- barplot(ekegg, showCategory=20)
p2 <- dotplot(ekegg, showCategory=20)
plotc = p1/p2
plotc
kegg.5.15.png
