这个数据集GSE129516，就是拿到了如下所示的数据文件：
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GEO下载的
我首先读取了一个文件，看了看，就是表达矩阵，所以直接CreateSeuratObject即可，都省去了3个文件的组合命令。
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表达矩阵例子
首先批量读取每个10x样品的表达矩阵
保证当前工作目录下面有后缀是matrices.csv.gz的文件，就是前面下载的6个文件：
rm(list=ls())
options(stringsAsFactors = F)
library(Seurat)
fs=list.files(pattern = 'matrices.csv.gz')
fs
sceList <- lapply(fs, function(x){
a=read.csv( x )
a[1:4,1:4]
raw.data=a[,-1]
rownames(raw.data)=a[,1]
library(stringr)
p=str_split(x,'_',simplify = T)[,2]
sce <- CreateSeuratObject(counts = raw.data,project = p )
})` 每个matrices.csv.gz文件都读取后，提供CreateSeuratObject构建成为对象。如果是读取10x数据需要三个文件：barcodes.tsv, genes.tsv, matrix.mtx，那个更简单哦！
然后使用seurat最出名的多个10x合并算法
多个单细胞对象的整合，这里直接使用标准代码即可：
pro='integrated'
for (i in 1:length(sceList)) {
sceList[[i]] <- NormalizeData(sceList[[i]], verbose = FALSE)
sceList[[i]] <- FindVariableFeatures(sceList[[i]], selection.method = "vst",
nfeatures = 2000, verbose = FALSE)
}
sceList
sce.anchors <- FindIntegrationAnchors(object.list = sceList, dims = 1:30)
sce.integrated <- IntegrateData(anchorset = sce.anchors, dims = 1:30)
因为是6个10X样品，所以这个步骤会略微有点耗费时间哦！
接着走标准的降维聚类分群
因为是构建好的对象，所以后续分析都是常规代码：
library(ggplot2)
library(cowplot)
# switch to integrated assay. The variable features of this assay are automatically
# set during IntegrateData
DefaultAssay(sce.integrated) <- "integrated"
# Run the standard workflow for visualization and clustering
sce.integrated <- ScaleData(sce.integrated, verbose = FALSE)
sce.integrated <- RunPCA(sce.integrated, npcs = 30, verbose = FALSE)
sce.integrated <- RunUMAP(sce.integrated, reduction = "pca", dims = 1:30)
p1 <- DimPlot(sce.integrated, reduction = "umap", group.by = "orig.ident")
p2 <- DimPlot(sce.integrated, reduction = "umap", group.by = "orig.ident", label = TRUE,
repel = TRUE) + NoLegend()
plot_grid(p1, p2)
p2
ggsave(filename = paste0(pro,'_umap.pdf') )
sce=sce.integrated
DimHeatmap(sce, dims = 1:12, cells = 100, balanced = TRUE)
ElbowPlot(sce)
sce <- FindNeighbors(sce, dims = 1:15)
sce <- FindClusters(sce, resolution = 0.2)
table([email protected]$integrated_snn_res.0.2)
sce <- FindClusters(sce, resolution = 0.8)
table([email protected]$integrated_snn_res.0.8)
DimPlot(sce, reduction = "umap")
ggsave(filename = paste0(pro,'_umap_seurat_res.0.8.pdf') )
DimPlot(sce, reduction = "umap",split.by = 'orig.ident')
ggsave(filename = paste0(pro,'_umap_seurat_res.0.8_split.pdf') )
save(sce,file = 'integrated_after_seurat.Rdata')` 最后对聚类的不同细胞亚群进行注释
前面呢是标准的聚类分群，每个细胞亚群仅仅是一个编号，实际上还需要给予它们一定的生物学意义，我们这里采用SingleR的标准代码：
rm(list=ls())
options(stringsAsFactors = F)
library(Seurat)
load(file = 'integrated_after_seurat.Rdata')
DefaultAssay(sce) <- "RNA"
# for B cells : cluster, 1,21
VlnPlot(object = sce, features ='Cd19',log =T )
VlnPlot(object = sce, features ='Cd79a',log =T )
library(SingleR)
sce_for_SingleR <- GetAssayData(sce, slot="data")
[email protected]$seurat_clusters
mouseImmu <- ImmGenData()
pred.mouseImmu <- SingleR(test = sce_for_SingleR, ref = mouseImmu, labels = mouseImmu$label.main,
method = "cluster", clusters = clusters,
assay.type.test = "logcounts", assay.type.ref = "logcounts")
mouseRNA <- MouseRNAseqData()
pred.mouseRNA <- SingleR(test = sce_for_SingleR, ref = mouseRNA, labels = mouseRNA$label.fine ,
method = "cluster", clusters = clusters,
assay.type.test = "logcounts", assay.type.ref = "logcounts")
cellType=data.frame(ClusterID=levels([email protected]$seurat_clusters),
mouseImmu=pred.mouseImmu$labels,
mouseRNA=pred.mouseRNA$labels )
head(cellType)
[email protected]$singleR=cellType[match(clusters,cellType$ClusterID),'mouseRNA']
pro='first_anno'
DimPlot(sce,reduction = "umap",label=T, group.by = 'singleR')
ggplot2::ggsave(filename = paste0(pro,'_tSNE_singleR.pdf'))
DimPlot(sce,reduction = "umap",label=T,split.by ='orig.ident',group.by = 'singleR')
ggplot2::ggsave(filename = paste0(pro,'_tSNE_singleR_by_orig.ident.pdf'))
save(sce,file = 'last_sce.Rdata')` 出图如下：
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降维聚类分群注释
可以看到效果还是杠杆的，而且我全程都是标准代码，就是follow群主的教程即可，我的R也是半吊子水平，只有你敢动手，这个图你也可以自己亲手做出来哦。
原文链接
人人都能学会的单细胞聚类分群注释