scRNAseq-seurat

By Kaiyi

示例数据与代码链接：https://pan.baidu.com/s/1yp_xd7kyPoI4vqPHiPBO6w 提取码：ybu9

本章节只包含基础的分析流程，更多进阶分析参见下一章节“scRNA-scanpy”；

本章节示例代码基于seurat 4.3.0.1版本，出现报错可考虑重新安装为4.3.0版本seurat。

R包安装

## 设置镜像，加快安装速度
options(repos=structure(c(CRAN="https://mirrors.tuna.tsinghua.edu.cn/CRAN/"))) 
## 查看镜像源
options()$repos

if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")

install.packages("devtools")
install.packages("R.utils")
install.packages("Seurat")
install.packages("pheatmap") 
install.packages("tidyverse") 
install.packages("magrittr") # 管道使用 R 包
install.packages("Matrix")
devtools::install_github('satijalab/seurat-data') 
remotes::install_github('satijalab/seurat-wrappers') ## 可能比较慢, 请耐心等待
remotes::install_github('chris-mcginnis-ucsf/DoubletFinder')
BiocManager::install("batchelor")
install.packages("harmony")
BiocManager::install("Biostrings",version = '3.14', force = TRUE)
BiocManager::install("celldex") 
BiocManager::install("SingleR")
BiocManager::install("clusterProfiler") ## 自动安装 "GO.db"
BiocManager::install("org.Hs.eg.db") ## 可能比较耗时, 请耐心等待

R包加载

library(Seurat)
library(SeuratData)
library(patchwork)
library(ggplot2)
library(batchelor)
library(SeuratWrappers)
library(magrittr)
library(tidyverse)
library(clusterProfiler)
library(GO.db)
library(org.Hs.eg.db)
library(DOSE)
library(DoubletFinder)
library(SingleR)
library(celldex)
library(harmony)
library(pheatmap)

1、单细胞数据读入

#方法一
# 读入数据,使用目录向量合并
rm(list=ls())
obj_dir <- c("E:/Protocol and tutorial/scRNAseq/2.单细胞转录组基础分析/HNC01TIL","E:/Protocol and tutorial/scRNAseq/2.单细胞转录组基础分析/Tonsil2") 
names(obj_dir) = c("HNC01TIL","Tonsil2")

counts <- Read10X(data.dir = obj_dir)
#前提是提供了3个10X文件，名字分别为barcodes.tsv.gz，features.tsv.gz，matrix.mtx.gz
#如果只提供了txt格式的表达矩阵，可以用counts = read.table("matrix.txt", header = T, row.names = 1)

scRNA1 = CreateSeuratObject(counts, min.cells=1) #创建Seurat对象
scRNA1 = subset(scRNA1, downsample=1000,seed=42) #从两个样本中各提取1000个细胞，减少后续运算所需的内存与时间
dim(scRNA1)
table([e-mail]$orig.ident)

#保存读入的rds
saveRDS(scRNA1, "Step1_merged_sample.rds")

#生成seurat对象list
scRNAlist <- SplitObject(scRNA1, split.by = "orig.ident")

#方法二
#使用merge函数合并seurat对象
scRNA2list <- list()
#以下代码会把每个样本的数据创建一个seurat对象，并存放到列表scRNAlist里
for(i in 1:length(obj_dir)){
  counts <- Read10X(data.dir = obj_dir[i])
  
  scRNA2list[[i]] <- CreateSeuratObject(counts, min.cells=1)
}
#使用merge函数将2个seurat对象合并成一个seurat对象
scRNA2 <- merge(scRNA2list[[1]], y=scRNA2list[[2]])
scRNA2 = subset(scRNA2, downsample=1000,seed=42)
table([e-mail]$orig.ident)
sum(row.names([e-mail]) == row.names([e-mail])) #检验两个object之间的细胞是否一致

2、单细胞数据质控

2.1 去除双细胞

# remove doublets
obj = SplitObject(scRNA1, split.by = "orig.ident") #将整合的SeuratObject重新分为两个SeuratObject
obj_rm=list() #创造一个空的list，用于后续盛放去除双细胞后Seurat对象
doublets_plot = list()
pc.num = 1:20 #定义pc维数
dir.create("SingleCell_QC") #在当前目录下新建一个文件夹，用于存放QC结果
RemoveDoublets <-function(
    object,
    doublet.rate,
    sample_name,
    pN=0.25,
    pc.num=1:30,
    use.SCT=FALSE
  ){
    ## 寻找最优pK值
    sweep.res.list <- paramSweep_v3(object, PCs = pc.num, sct = F) #使用log标准化，sct=F，如使用SCT标准化，则为T
    sweep.stats <- summarizeSweep(sweep.res.list, GT = FALSE)  
    bcmvn <- find.pK(sweep.stats) #可以看到最佳参数的点
    pK_bcmvn <- bcmvn$pK[which.max(bcmvn$BCmetric)] %>% as.character() %>% as.numeric()
    ## 排除不能检出的同源doublets，优化期望的doublets数量
    homotypic.prop <- modelHomotypic(object$seurat_clusters)   # 最好提供celltype
    nExp_poi <- round(doublet.rate*ncol(object)) 
    nExp_poi.adj <- round(nExp_poi*(1-homotypic.prop))
    seu.scored <- doubletFinder_v3(object, PCs = pc.num, pN = 0.25, pK = pK_bcmvn, 
                                   nExp = nExp_poi.adj, reuse.pANN = F, sct = F)
    # pick out doublets
    cname <-colnames(seu.scored[[]])
    DF<-cname[grep('^DF',cname)]
    seu.scored[["doublet"]] <- as.numeric(seu.scored[[DF]]=="Doublet")
    
    # remove doublets
    seu.removed <- subset(seu.scored, subset = doublet != 1)
    p1 <- DimPlot(seu.scored, group.by = DF)
    res.list <- list("plot"=p1, "obj"=seu.removed)
    return(res.list)
  }

for( i in names(obj)){
    obj[[i]] <- NormalizeData(obj[[i]])
    obj[[i]] <- FindVariableFeatures(obj[[i]], selection.method = "vst", nfeatures = 2000)
    obj[[i]] <- ScaleData(obj[[i]])
    obj[[i]] <- RunPCA(obj[[i]])
    obj[[i]] <- RunUMAP(obj[[i]], dims = 1:20)
    obj[[i]] <- FindNeighbors(obj[[i]], dims = pc.num) %>% FindClusters(resolution = 0.3)
    tmp <- RemoveDoublets(obj[[i]], doublet.rate=0.008,sample_name=i,pc.num=pc.num)
    #直接查表，1000细胞对应的doublets rate是0.8%，10000细胞对应的doublets rate是～7.6%
    obj_rm[[i]] <- tmp$obj
    doublets_plot[[i]] <- tmp$plot
  }

doublets_plot[["Tonsil2"]]

doublets_plot[["HNC01TIL"]]

2.2 去除线粒体、红细胞的影响

#质控
scRNA <- merge(obj_rm[[1]], y=obj_rm[[2]]) #重新整合去除双细胞后的两个SeuratObject
Idents(scRNA) <- 'orig.ident' #确立细胞身份的主要标签，此处是样本来源（即Tonsil2和HNC01TIL）

# 计算线粒体基因比例
scRNA[["percent.mt"]] = PercentageFeatureSet(scRNA, 
                                                   pattern = "^MT-") #因为人的线粒体基因是MT-开头的，所以可以通过这种方式匹配，如果是小鼠的话，是以“mt-”开头的。其他特殊物种可以直接提供线粒体基因的列表

#计算红细胞基因比例
HB.genes <- c("HBA1","HBA2","HBB","HBD","HBE1","HBG1","HBG2","HBM","HBQ1","HBZ")
HB_m <- match(HB.genes, rownames(scRNA@assays$RNA)) 
HB.genes <- rownames(scRNA@assays$RNA)[HB_m] 
HB.genes <- HB.genes[!is.na(HB.genes)] 
scRNA[["percent.HB"]]<-PercentageFeatureSet(scRNA, features=HB.genes) #此处就是根据提供的红细胞基因列表计算红细胞基因表达的比例

# 数据过滤前，对 nFeature数、nCount 数,线粒体基因比例和红细胞基因比例进行可视化
beforeQC_vlnplot = VlnPlot(scRNA, 
                           features = c("nFeature_RNA", 
                                        "nCount_RNA", 
                                        "percent.mt",
                                        "percent.HB"), 
                           ncol = 4, 
                           pt.size = 0.1)
dir.create("SingleCell_QC")
ggsave("SingleCell_QC/BeforeQC_nFeature_nCount_percent.mt_percent.HB_vlnplot.pdf", plot = beforeQC_vlnplot)
ggsave("SingleCell_QC/BeforeQC_nFeature_nCount_percent.mt_percent.HB_vlnplot.png", plot = beforeQC_vlnplot)
beforeQC_vlnplot

#=== 对 nFeature、线粒体基因比例与 nCount 的关系进行可视化 ===#
plot1 <- FeatureScatter(scRNA, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(scRNA, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot3 <- FeatureScatter(scRNA, feature1 = "nCount_RNA", feature2 = "percent.HB")

pearplot <- CombinePlots(plots = list(plot1, plot2, plot3), nrow=1, legend="none") 
ggsave("SingleCell_QC/pearplot_before_qc.pdf", plot = pearplot, width = 12, height = 5) 
ggsave("SingleCell_QC/pearplot_before_qc.png", plot = pearplot, width = 12, height = 5)
plot1
plot2
plot3

# 根据基因数和线粒体比例进行数据过滤
minGene=500 #这里设置了比较严格的条件，一般可以设置200，保留较多的细胞用于后续分析，保留细胞类型多样性，由于今天演示，为保证细胞的基因表达数量足够，设置得较高。
maxGene=3000 #也可以设置为6000
pctMT=10 #也可以设置成20

scRNA = subset(scRNA, 
                     subset = nFeature_RNA > minGene & nFeature_RNA < maxGene & percent.mt < pctMT)

afterQC_vlnplot = VlnPlot(scRNA, 
                           features = c("nFeature_RNA", 
                                        "nCount_RNA", 
                                        "percent.mt",
                                        "percent.HB"), 
                           ncol = 4, 
                           pt.size = 0.1)

ggsave("SingleCell_QC/afterQC_nFeature_nCount_percent.mt_percent.HB_vlnplot.pdf", plot = afterQC_vlnplot)
ggsave("SingleCell_QC/afterQC_nFeature_nCount_percent.mt_percent.HB_vlnplot.png", plot = afterQC_vlnplot)
afterQC_vlnplot
saveRDS(scRNA,"Step2_After_QC.rds")

3、数据归一化与标准化

#方法一：NormalizeData+FindVariableFeatures+ScaleData
scRNA <- NormalizeData(scRNA)
scRNA <- FindVariableFeatures(scRNA, selection.method = "vst")
scRNA <- ScaleData(scRNA, features = VariableFeatures(scRNA))

#方法二：也可以使用SCTransform进行归一化
scRNA <- SCTransform(scRNA, vars.to.regress = c("nCount_RNA","nCount_RNA","percent.mt", "percent.HB"), verbose = F) #排除线粒体基因等干扰

#检查seurat对象的结构
str(scRNA)

#稀疏矩阵的格式，没有经过归一化
scRNA@assays$RNA@counts[1:6,1:60]

#稀疏矩阵的格式，经过归一化
scRNA@assays$RNA@data[1:6,1:60]

#meta.data内的信息
head([e-mail])

4、数据批次矫正与聚类

#查看pca结果
scRNA <- RunPCA(scRNA, features = VariableFeatures(scRNA))
plot1 <- DimPlot(scRNA, reduction = "pca", group.by="orig.ident")
plot2 <- ElbowPlot(scRNA, ndims=30, reduction="pca") 

plot3 <- plot1+plot2
dir.create("Clustering")
ggsave("Clustering/pca.png", plot = plot3, width = 8, height = 4)
plot3

##细胞聚类
scRNA <- FindNeighbors(scRNA, dims = pc.num) #pc.num可以更改
scRNA <- FindClusters(scRNA, resolution = 0.5) #resolution可以更改，数字越小，分群越少
table([e-mail]$seurat_clusters)
metadata <- [e-mail]
cell_cluster <- data.frame(cell_ID=rownames(metadata), cluster_ID=metadata$seurat_clusters)
write.csv(cell_cluster,'Clustering/cell_cluster.csv',row.names = F, quote = F)

#UMAP
scRNA <- RunUMAP(scRNA, dims = pc.num) #这一步有时候有点慢，pc.num可以更改

#group_by_cluster
plot3 = DimPlot(scRNA, reduction = "umap", label=T) 
ggsave("Clustering/UMAP.png",plot3)
plot3

#group_by_sample
plot4 = DimPlot(scRNA, reduction = "umap", group.by='orig.ident')

#split_by_sample
plot5 = DimPlot(scRNA, reduction = "umap", split.by='orig.ident')

plotc <- plot4+plot5 
ggsave("Clustering/UMAP_cluster_sample.png", plot = plotc, width = 10, height = 5)
plotc
saveRDS(scRNA,"Step3_Clustering.rds")

4.1 批次效应矫正方法一：MNN

#去批次后进行聚类
scRNAlist <- SplitObject(scRNA, split.by = "orig.ident")
scRNAlist <- lapply(scRNAlist, FUN = function(x) NormalizeData(x)) #如果前面用的是SCTransform就不能这样做了
scRNAlist <- lapply(scRNAlist, FUN = function(x) FindVariableFeatures(x))
scRNA_mnn <- RunFastMNN(object.list = scRNAlist)
scRNA_mnn <- FindVariableFeatures(scRNA_mnn)
scRNA_mnn <- RunUMAP(scRNA_mnn, reduction = "mnn", dims = 1:20) #稍微有点慢，dims也是可以改变的
scRNA_mnn <- FindNeighbors(scRNA_mnn, reduction = "mnn", dims = 1:20)
scRNA_mnn <- FindClusters(scRNA_mnn)
p1 <- DimPlot(scRNA_mnn, group.by = "orig.ident", pt.size=0.1) + 
  ggtitle("Integrated by fastMNN")
p2 <- DimPlot(scRNA, group.by="orig.ident", pt.size=0.1) + 
  ggtitle("No integrated")
p = p1 + p2 + plot_layout(guides='collect')
#比较去批次前后聚类结果的变化
p
saveRDS(scRNA_mnn,"Step4_MNN.rds")

p3 <- DimPlot(scRNA_mnn,  pt.size=0.1) 

p4 <- DimPlot(scRNA_mnn, split.by="orig.ident", pt.size=0.1) 

p = p3 + p4 + plot_layout(guides='collect')
#比较各个样本内cluster的组成情况
p

ggsave('Clustering/fastMNN.png', p, width=8, height=4)

4.2 批次效应矫正方法二：harmony

#harmony
library(harmony)
scRNA_harmony <- RunHarmony(scRNA, group.by.vars = "orig.ident")
scRNA_harmony <- RunUMAP(scRNA_harmony, reduction = "harmony", dims = 1:20) #稍微有点慢
scRNA_harmony <- FindNeighbors(scRNA_harmony, reduction = "harmony", dims = 1:20) %>% FindClusters()

p1 <- DimPlot(scRNA_harmony, group.by = "orig.ident", pt.size=0.1) + 
  ggtitle("Integrated by harmony")
p2 <- DimPlot(scRNA, group.by="orig.ident", pt.size=0.1) + 
  ggtitle("No integrated")
#比较去批次前后聚类结果的变化
p = p1 + p2 + plot_layout(guides='collect')

p

p3 = DimPlot(scRNA_harmony, reduction = "umap") 
p4 = DimPlot(scRNA_harmony, reduction = "umap", split.by='orig.ident') 
#比较各个样本内cluster的组成情况
plotc <- p3+p4 + plot_layout(guides='collect')
ggsave("Clustering/scRNA_harmony.png", plot = plotc, width = 10, height = 5)
plotc
saveRDS(scRNA_harmony,"Step4_harmony.rds")

4.3 批次效应矫正方法三：锚点整合IntegrateData

#数据整合
# 选取两个样本之间共有的feature
features <- SelectIntegrationFeatures(object.list = scRNAlist)
#以共有features为基础寻找锚点
scRNA.anchors <- FindIntegrationAnchors(object.list = scRNAlist,anchor.features = features)
#利用锚点整合数据
scRNA3 <- IntegrateData(anchorset = scRNA.anchors)
#重跑前面聚类的一般流程
scRNA3 <- ScaleData(scRNA3, verbose = FALSE)
scRNA3 <- RunPCA(scRNA3, npcs = 30, verbose = FALSE)
scRNA3 <- RunUMAP(scRNA3, reduction = "pca", dims = 1:20)
scRNA3 <- FindNeighbors(scRNA3, reduction = "pca", dims = 1:20)
scRNA3 <- FindClusters(scRNA3, resolution = 0.5)
#比较整合前后聚类结果的变化
p1 <- DimPlot(scRNA3, group.by = "orig.ident", pt.size=0.1) + 
  ggtitle("Integrated")
p2 <- DimPlot(scRNA, group.by="orig.ident", pt.size=0.1) + 
  ggtitle("No integrated")
p = p1 + p2 + plot_layout(guides='collect')

p

p3 <- DimPlot(scRNA3, reduction = "umap")
p4 <- DimPlot(scRNA3, reduction = "umap", split.by = "orig.ident")
Integrated_dimplot <- p3 + p4 +  plot_layout(guides='collect')
#比较各个样本内cluster的组成情况
Integrated_dimplot
ggsave('Clustering/Integrated.png', Integrated_dimplot, width=8, height=4)

5、marker基因鉴定

#鉴定 marker 基因
dir.create("Marker")
# 鉴定各 cluster 的 Marker 基因
all.markers = FindAllMarkers(scRNA_mnn, 
                                     min.pct = 0.25, 
                                     logfc.threshold = 0.25, 
                                     only.pos = TRUE)
head(all.markers)

# cluster5 和cluster0、3进行比较
cluster5.markers <- FindMarkers(scRNA_mnn, 
                                ident.1 = 5, 
                                ident.2 = c(0, 3), 
                                min.pct = 0.25, 
                                only.pos = TRUE)
head(cluster5.markers)

# 根据 FoldChange 进行排序选取每群细胞的 top10 Marker 基因
top10 = all.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_log2FC)

# 保存 Marker 基因
write.table(all.markers, 
            "Marker/all_Markers_of_each_clusters.xls", 
            col.names = T, 
            row.names = F, 
            sep = "\t")
write.table(top10, 
            "Marker/top10_Markers_of_each_clusters.xls", 
            col.names = T, 
            row.names = F, 
            sep = "\t")

# 绘制热图
scRNA_mnn <- ScaleData(scRNA_mnn, features = row.names(scRNA_mnn))
heatmap_plot = DoHeatmap(object = scRNA_mnn, 
                         features = as.vector(top10$gene), 
                         group.by = "seurat_clusters", 
                         group.bar = T, 
                         size = 2) +
  theme(axis.text.y = element_text(size = 4))

# 保存绘图结果
ggsave("Marker/top10_marker_of_each_cluster_heatmap.pdf", width = 12, height = 12,
       plot = heatmap_plot)
ggsave("Marker/top10_marker_of_each_cluster_heatmap.png", width = 12, height = 12,
       plot = heatmap_plot)
heatmap_plot

top1 = all.markers %>% group_by(cluster) %>% top_n(n = 1, wt = avg_log2FC)
# 绘制单个基因的小提琴图
VlnPlot(scRNA_mnn, 
                  cols = NULL, 
                  features = "RGS13", 
                  pt.size = 0, 
                  group.by = "seurat_clusters")

#批量绘制小提琴图
dir.create("Marker/vlnplot/")
for (gene in as.vector(top1$gene)) {
  vlnplot = VlnPlot(scRNA_mnn, 
                    features = gene, 
                    pt.size = 0, 
                    group.by = "seurat_clusters")
  ggsave(paste0("Marker/vlnplot/",gene,"_vlnplot.pdf"), plot = vlnplot)
  ggsave(paste0("Marker/vlnplot/",gene,"_vlnplot.png"), plot = vlnplot)
}

# 绘制单个基因的featureplot
FeaturePlot(scRNA_mnn, 
            features = "RGS13", 
            pt.size = 0.5,
            reduction = "umap",
            cols = c("lightgrey", "#DE1F1F"))


#批量绘制 featureplot 图
dir.create("Marker/featureplot/")
for (gene in as.vector(top1$gene)) {
  featureplot = FeaturePlot(scRNA_mnn, 
                            features = gene, 
                            pt.size = 0.5, 
                            cols = c("lightgrey", "#DE1F1F"))
  ggsave(paste0("Marker/featureplot/",gene,"_featureplot.pdf"), plot = featureplot)
  ggsave(paste0("Marker/featureplot/",gene,"_featureplot.png"), plot = featureplot)
}

6、差异基因鉴定

#将数据集的idents从clusters切换回orig.ident(各样本)
Idents(scRNA_mnn) <- 'orig.ident'
# 鉴定各样本的差异基因
Diff_exp = FindAllMarkers(scRNA_mnn, 
                               min.pct = 0.25, 
                               logfc.threshold = 0.25, 
                               only.pos = TRUE)
head(Diff_exp)
top10 = Diff_exp %>% group_by(cluster) %>% top_n(n = 10, wt = avg_log2FC)
dir.create("Diffexp")
write.table(Diff_exp, 
            "Diffexp/group_HNC01TIL-vs-Tonsil2-diff.xls", 
            col.names = T, 
            row.names = F, 
            sep = "\t")
write.table(top10, 
            "Diffexp/group_HNC01TIL-vs-Tonsil2-top10-diff.xls", 
            col.names = T, 
            row.names = F, 
            sep = "\t")

heatmap_plot = DoHeatmap(object = scRNA_mnn, 
                         features = as.vector(top10$gene), 
                         group.by = "orig.ident", 
                         group.bar = T, 
                         size = 2) +
  theme(axis.text.y = element_text(size = 4))

# 保存绘图结果
ggsave("Diffexp/top10_marker_of_each_cluster_heatmap.pdf",
       plot = heatmap_plot)
ggsave("Diffexp/top10_marker_of_each_cluster_heatmap.png", 
       plot = heatmap_plot)
heatmap_plot

7、富集分析

#富集分析
genes_symbol <- as.character(Diff_exp$gene)
#转换ID
eg = bitr(genes_symbol, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")
id = as.character(eg[,2])
dir.create("enrichment")
#GO
ego <- enrichGO(gene = id,
                OrgDb = org.Hs.eg.db,
                ont = "MF",
                pAdjustMethod = "BH",
                pvalueCutoff = 0.05,
                qvalueCutoff = 0.05,
                readable = TRUE)
GO_dot <- dotplot(ego)
GO_bar <- barplot(ego)
res_plot <- CombinePlots(list(GO_dot,GO_bar), nrow=1)
ggsave("enrichment/GO_results.pdf", plot=res_plot, width = 12,height = 7)
ggsave("enrichment/GO_results.png", plot=res_plot, width = 12,height = 7)

GO_dot

GO_bar

8、细胞类型鉴定

8.1 方法一：SingleR（有时候不太准确）

dir.create("SingleR")
Idents(scRNA_mnn) <- 'seurat_clusters'
# 读入参考数据集
load("HumanPrimaryCellAtlas_hpca.se_human.RData")
norm_count = GetAssayData(scRNA_mnn, 
                          slot="data") #标准化后的矩阵
pred<- SingleR(test = norm_count, 
               ref = hpca.se, 
               labels = hpca.se$label.main)
#将注释结果添加到seurat对象里
scRNA_mnn$singleR_celltype = pred$labels

# 对细胞类型注释结果进行可视化
celltype_plot = DimPlot(scRNA_mnn, 
                        reduction = "umap", 
                        group.by = "singleR_celltype")
ggsave("SingleR/celltype_plot.pdf", celltype_plot)
ggsave("SingleR/celltype_plot.png", celltype_plot)
celltype_plot

# 通过热图了解每个细胞对应细胞类型的打分情况
score_heatmap <-  plotScoreHeatmap(pred, clusters = [e-mail]$seurat_clusters, order.by = "clusters")
ggsave("SingleR/score_heatmap.pdf", score_heatmap, width = 8, height = 10)
ggsave("SingleR/score_heatmap.png", score_heatmap, width = 8, height = 10)
score_heatmap

# 通过热图了解每个cluster对应细胞类型的打分情况
tab <- table(cluster=[e-mail]$seurat_clusters, label=pred$labels) 
cluster_type <- pheatmap::pheatmap(log10(tab+10)) # using a larger pseudo-count for smoothing. 
ggsave("SingleR/cluster_type.pdf", cluster_type)
ggsave("SingleR/cluster_type.png", cluster_type)
cluster_type

8.2 方法二：根据细胞marker手动注释

#比如可以找到一些T细胞marker，用散点图或者小提琴图展示出来，观察其分布情况
#也可以根据各细胞亚群的top差异基因（FindAllMarkers的结果）直接注释

genes_to_check = c('PTPRC', 'CD3D', 'CD3E','FOXP3',
                   'CD4','IL7R','NKG7','CD8A',"GZMB",'GNLY','MKI67','TCF7','IFNG','IL17A','PDCD1','TOX','CD160','KLRC2','FGFBP2', 'CD27','CCR7')
DotPlot(scRNA_mnn, group.by = 'seurat_clusters',
        features = unique(genes_to_check)) + RotatedAxis()

在meta.data中添加细胞注释

# 将singleR得到的细胞类型加入到对象的meta.data中，后续用于高级分析
scRNA_mnn <- RenameIdents(scRNA_mnn, '0'='T_cells','1'='B_cell','2'='T_cells','3'='B_cell','4'='T_cells','5'='T_cells','6'='T_cells','7'='Monocyte','8'='B_cell','9'='Monocyte','10'='B_cell','11'='Pre-B_cell_CD34-','12'='CMP')
scRNA_mnn[["celltype"]] <- Idents(scRNA_mnn)
head([e-mail])

saveRDS(scRNA_mnn, "Step5_CellTyping.rds")

p1 <- DimPlot(scRNA_mnn, group.by = "seurat_clusters")
p2 <- DimPlot(scRNA_mnn, group.by = "celltype")
#查看cluster与细胞类型的对应关系
p3 = p1+p2
ggsave("SingleR/celltype_contrast.pdf",p3,width = 12, height = 6)
ggsave("SingleR/celltype_contrast.png",p3, width = 12, height = 6)
p3

#查看各样本中细胞类型的组成情况
DimPlot(scRNA_mnn, group.by = "celltype", split.by = "orig.ident")