单细胞转录组分析：多样本合并与批次校正

##==数据质控==#
scRNA <- scRNA3  #以后的分析使用整合的数据进行
##meta.data添加信息
proj_name <- data.frame(proj_name=rep("demo2",ncol(scRNA)))
rownames(proj_name) <- row.names(scRNA@meta.data)
scRNA <- AddMetaData(scRNA, proj_name)

##切换数据集
DefaultAssay(scRNA) <- "RNA"

##计算线粒体和红细胞基因比例
scRNA[["percent.mt"]] <- PercentageFeatureSet(scRNA, pattern = "^MT-")
#计算红细胞比例
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) 
#head(scRNA@meta.data)
col.num <- length(levels(as.factor(scRNA@meta.data$orig.ident)))

##绘制小提琴图
#所有样本一个小提琴图用group.by="proj_name"，每个样本一个小提琴图用group.by="orig.ident"
violin <-VlnPlot(scRNA, group.by = "proj_name",  
         features = c("nFeature_RNA", "nCount_RNA", "percent.mt","percent.HB"), 
         cols =rainbow(col.num), 
         pt.size = 0.01, #不需要显示点，可以设置pt.size = 0
         ncol = 4)   
         theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank()) 
ggsave("QC/vlnplot_before_qc.pdf", plot = violin, width = 12, height = 6) 
ggsave("QC/vlnplot_before_qc.png", plot = violin, width = 12, height = 6)  
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("QC/pearplot_before_qc.pdf", plot = pearplot, width = 12, height = 5) 
ggsave("QC/pearplot_before_qc.png", plot = pearplot, width = 12, height = 5)

##设置质控标准
print(c("请输入允许基因数和核糖体比例，示例如下：", "minGene=500", "maxGene=4000", "pctMT=20"))
minGene=500
maxGene=3000
pctMT=10

##数据质控
scRNA <- subset(scRNA, subset = nFeature_RNA > minGene & nFeature_RNA < maxGene & percent.mt < pctMT)
col.num <- length(levels(as.factor(scRNA@meta.data$orig.ident)))
violin <-VlnPlot(scRNA, group.by = "proj_name",
         features = c("nFeature_RNA", "nCount_RNA", "percent.mt","percent.HB"), 
         cols =rainbow(col.num), 
         pt.size = 0.1, 
         ncol = 4)   
         theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank()) 
ggsave("QC/vlnplot_after_qc.pdf", plot = violin, width = 12, height = 6) 
ggsave("QC/vlnplot_after_qc.png", plot = violin, width = 12, height = 6)

质控后的数据

##==鉴定细胞类型==##
library(SingleR)
dir.create("CellType")
refdata <- MonacoImmuneData()
testdata <- GetAssayData(scRNA, slot="data")
clusters <- scRNA@meta.data$seurat_clusters
#使用Monaco参考数据库鉴定
cellpred <- SingleR(test = testdata, ref = refdata, labels = refdata$label.main, 
                     method = "cluster", clusters = clusters, 
                     assay.type.test = "logcounts", assay.type.ref = "logcounts")
celltype = data.frame(ClusterID=rownames(cellpred), celltype=cellpred$labels, stringsAsFactors = F)
write.csv(celltype,"CellType/celltype_Monaco.csv",row.names = F)
scRNA@meta.data$celltype_Monaco = "NA"
for(i in 1:nrow(celltype)){
scRNA@meta.data[which(scRNA@meta.data$seurat_clusters == celltype$ClusterID[i]),'celltype_Monaco'] <- celltype$celltype[i]}
p1 = DimPlot(scRNA, group.by="celltype_Monaco", repel=T, label=T, label.size=5, reduction='tsne')
p2 = DimPlot(scRNA, group.by="celltype_Monaco", repel=T, label=T, label.size=5, reduction='umap')
p3 = p1 p2  plot_layout(guides = 'collect')
ggsave("CellType/tSNE_celltype_Monaco.png", p1, width=7 ,height=6)
ggsave("CellType/UMAP_celltype_Monaco.png", p2, width=7 ,height=6)
ggsave("CellType/celltype_Monaco.png", p3, width=10 ,height=5)
#使用DICE参考数据库鉴定
refdata <- DatabaseImmuneCellExpressionData()
# load('~/database/SingleR_ref/ref_DICE_1561s.RData')
# refdata <- ref_DICE
testdata <- GetAssayData(scRNA, slot="data")
clusters <- scRNA@meta.data$seurat_clusters
cellpred <- SingleR(test = testdata, ref = refdata, labels = refdata$label.main, 
                     method = "cluster", clusters = clusters, 
                     assay.type.test = "logcounts", assay.type.ref = "logcounts")
celltype = data.frame(ClusterID=rownames(cellpred), celltype=cellpred$labels, stringsAsFactors = F)
write.csv(celltype,"CellType/celltype_DICE.csv",row.names = F)
scRNA@meta.data$celltype_DICE = "NA"
for(i in 1:nrow(celltype)){
scRNA@meta.data[which(scRNA@meta.data$seurat_clusters == celltype$ClusterID[i]),'celltype_DICE'] <- celltype$celltype[i]}
p4 = DimPlot(scRNA, group.by="celltype_DICE", repel=T, label=T, label.size=5, reduction='tsne')
p5 = DimPlot(scRNA, group.by="celltype_DICE", repel=T, label=T, label.size=5, reduction='umap')
p6 = p3 p4  plot_layout(guides = 'collect')
ggsave("CellType/tSNE_celltype_DICE.png", p4, width=7 ,height=6)
ggsave("CellType/UMAP_celltype_DICE.png", p5, width=7 ,height=6)
ggsave("CellType/celltype_DICE.png", p6, width=10 ,height=5)
#对比两种数据库鉴定的结果
p8 = p1 p4
ggsave("CellType/Monaco_DICE.png", p8, width=12 ,height=5)

##保存数据
saveRDS(scRNA,'scRNA.rds')