Seurat | 完美整合单细胞测序数据（部分交集数据的整合）（一）

1写在前面

之前我们介绍了Seurat、Harmony，rliger三个包，用于3'和5'数据合并的方法。🤒
但有时候我们会遇到两个datasets只有部分重叠，这和之前介绍的方法就有一点不同了。🤨

2用到的包

rm(list = ls())
library(Seurat)
library(SeuratDisk)
library(SeuratWrappers)
library(patchwork)
library(harmony)
library(rliger)
library(RColorBrewer)
library(tidyverse)
library(reshape2)
library(ggsci)
library(ggstatsplot)
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3示例数据

这里我们提供1个3’ PBMC dataset和1个whole blood dataset。😉

umi_gz <- gzfile("./GSE149938_umi_matrix.csv.gz",'rt')  
umi <- read.csv(umi_gz,check.names = F,quote = "")

matrix_3p    <- Read10X_h5("./3p_pbmc10k_filt.h5",use.names = T)
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创建Seurat对象。🧐

srat_wb <- CreateSeuratObject(t(umi),project = "whole_blood")
srat_3p <- CreateSeuratObject(matrix_3p,project = "pbmc10k_3p")
rm(umi_gz)
rm(umi)
rm(matrix_3p)
srat_wb
srat_3p
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4修改metadata

为了方便后续分析，这里我们对metadata进行一下注释修改。 😁

colnames(srat_wb@meta.data)[1] <- "cell_type"
srat_wb@meta.data$orig.ident <- "whole_blood"
srat_wb@meta.data$orig.ident <- as.factor(srat_wb@meta.data$orig.ident)
head(srat_wb[[]])
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5基础质控

做一下标准操作，计算线粒体基因和核糖体基因。🥳

srat_wb <- SetIdent(srat_wb,value = "orig.ident")

srat_wb[["percent.mt"]] <- PercentageFeatureSet(srat_wb, pattern = "^MT-")
srat_wb[["percent.rbp"]] <- PercentageFeatureSet(srat_wb, pattern = "^RP[SL]")
srat_3p[["percent.mt"]] <- PercentageFeatureSet(srat_3p, pattern = "^MT-")
srat_3p[["percent.rbp"]] <- PercentageFeatureSet(srat_3p, pattern = "^RP[SL]")

p1 <- VlnPlot(srat_wb, ncol = 4,
              features = c("nFeature_RNA","nCount_RNA","percent.mt","percent.rbp"))

p2 <- VlnPlot(srat_3p, ncol = 4,
        features = c("nFeature_RNA","nCount_RNA","percent.mt","percent.rbp"))

p1/p2
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6交集基因

whole blood dataset使用的是Cell Ranger GRCh38-2020A进行注释，与3’ PBMC dataset差的比较多，所以我们先看一下有多少共同基因吧。🤩

# table(rownames(srat_3p) %in% rownames(srat_wb))
common_genes <- rownames(srat_3p)[rownames(srat_3p) %in% rownames(srat_wb)]

length(common_genes)
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7过滤基因

我们设置一下过滤条件，把一些表达过低或过高的细胞去掉，以及一些线粒体基因过高的细胞（细胞状态不佳）。✌️

srat_3p <- subset(srat_3p, subset = nFeature_RNA > 500 & nFeature_RNA < 5000 & percent.mt < 15)
srat_wb <- subset(srat_wb, subset = nFeature_RNA > 1000 & nFeature_RNA < 6000)

srat_3p <- srat_3p[rownames(srat_3p) %in% common_genes,]
srat_wb <- srat_wb[rownames(srat_wb) %in% common_genes,]
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8数据整合

8.1 合并为list

wb_list <- list()
wb_list[["pbmc10k_3p"]]   <- srat_3p
wb_list[["whole_blood"]]  <- srat_wb
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8.2 Normalization与特征基因

for (i in 1:length(wb_list)) {
  wb_list[[i]] <- NormalizeData(wb_list[[i]], verbose = F)
  wb_list[[i]] <- FindVariableFeatures(wb_list[[i]], selection.method = "vst", nfeatures = 2000, verbose = F)
}
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8.3 寻找Anchors并整合数据

wb_anchors <- FindIntegrationAnchors(object.list = wb_list, dims = 1:30)
wb_seurat  <- IntegrateData(anchorset = wb_anchors, dims = 1:30)
rm(wb_list)
rm(wb_anchors)
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9整合效果可视化

9.1 整合前

DefaultAssay(wb_seurat) <- "RNA"
wb_seurat <- NormalizeData(wb_seurat, verbose = F)
wb_seurat <- FindVariableFeatures(wb_seurat, selection.method = "vst", nfeatures = 2000, verbose = F)
wb_seurat <- ScaleData(wb_seurat, verbose = F)
wb_seurat <- RunPCA(wb_seurat, npcs = 30, verbose = F)
wb_seurat <- RunUMAP(wb_seurat, reduction = "pca", dims = 1:30, verbose = F)

DimPlot(wb_seurat,reduction = "umap") + 
   scale_color_npg()+
  plot_annotation(title = "10k 3' PBMC and whole blood, before integration")
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9.2 整合后

DefaultAssay(wb_seurat) <- "integrated"
wb_seurat <- ScaleData(wb_seurat, verbose = F)
wb_seurat <- RunPCA(wb_seurat, npcs = 30, verbose = F)
wb_seurat <- RunUMAP(wb_seurat, reduction = "pca", dims = 1:30, verbose = F)

DimPlot(wb_seurat, reduction = "umap") + 
  scale_color_npg()+
  plot_annotation(title = "10k 3' PBMC and white blood cells, after integration")
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10降维与聚类

10.1 聚类可视化

wb_seurat <- FindNeighbors(wb_seurat, dims = 1:30, k.param = 10, verbose = F)
wb_seurat <- FindClusters(wb_seurat, verbose = F)

ncluster <- length(unique(wb_seurat[[]]$seurat_clusters))

mycol <- colorRampPalette(brewer.pal(8, "Set2"))(ncluster)

DimPlot(wb_seurat,label = T, reduction = "umap",
        cols = mycol, repel = T) +
  NoLegend()
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10.2 具体查看及可视化

count_table <- table(wb_seurat@meta.data$seurat_clusters, 
                     wb_seurat@meta.data$orig.ident)
count_table

#### 可视化
count_table %>% 
  as.data.frame() %>% 
  ggbarstats(x = Var2, 
             y = Var1,
             counts = Freq)+
  scale_fill_npg()
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