Seurat 4.0 | 单细胞转录组数据整合(scRNA-seq integration)
对于两个或多个单细胞数据集的整合问题，Seurat 自带一系列方法用于跨数据集匹配(match) (或“对齐” ,align)共享的细胞群。这些方法首先识别处于匹配生物状态的交叉数据集细胞(“锚”,anchors)，可以用于校正数据集之间的技术差异(如，批次效应校正)，也可以用于不同实验条件下的scRNA-seq的比较分析。
作者使用了两种不同状态(静息或干扰素刺激状态,a resting or interferon-stimulated state)的人类PBMC细胞进行比较分析作为示例。
整合目标
创建一个整合的assay用于下游分析
识别两个数据集中共有的细胞类型
获取在不同状态细胞中都保守的细胞标志物 (cell markers)
比较两个数据集,发现对刺激有反应(responses to stimulation)的细胞类型
设置Seurat对象
library(Seurat) library(SeuratData) # 包含数据集 library(patchwork)
# 下载数据 InstallData("ifnb")
# 加载数据 LoadData("ifnb") # 根据stim状态拆分为两个list (stim and CTRL) ifnb.list <- SplitObject(ifnb, split.by = "stim") ifnb.list
> ifnb.list $CTRL An object of class Seurat 14053 features across 6548 samples within 1 assay Active assay: RNA (14053 features, 0 variable features) $STIM An object of class Seurat 14053 features across 7451 samples within 1 assay Active assay: RNA (14053 features, 0 variable features)
可见，本方法的原始数据准备只需把不同condition或者tech的Seurat对象整合为一个list即可进行后续的分析。
# 分别对两个数据集进行标准化并识别变量特征 ifnb.list <- lapply(X = ifnb.list, FUN = function(x) { x <- NormalizeData(x) x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000) }) # 选择跨数据集重复可变的特征进行整合 features <- SelectIntegrationFeatures(object.list = ifnb.list)
进行整合
FindIntegrationAnchors() 函数使用Seurat对象列表作为输入，来识别anchors。
IntegrateData() 函数使用识别到的anchors来整合数据集。
immune.anchors <- FindIntegrationAnchors(object.list = ifnb.list, anchor.features = features)
# 生成整合数据 immune.combined <- IntegrateData(anchorset = immune.anchors) 整合分析
#指定已校正的数据进行下游分析，注意未修改的原始数据仍在于 'RNA' assay中 DefaultAssay(immune.combined) <- "integrated" # 标准数据可视化和分类流程 immune.combined <- ScaleData(immune.combined, verbose = FALSE) immune.combined <- RunPCA(immune.combined, npcs = 30, verbose = FALSE) immune.combined <- RunUMAP(immune.combined, reduction = "pca", dims = 1:30) immune.combined <- FindNeighbors(immune.combined, reduction = "pca", dims = 1:30) immune.combined <- FindClusters(immune.combined, resolution = 0.5)
# 可视化 p1 <- DimPlot(immune.combined, reduction = "umap", group.by = "stim") p2 <- DimPlot(immune.combined, reduction = "umap", label = TRUE, repel = TRUE) p1 + p2
# split.by 展示分组聚类 DimPlot(immune.combined, reduction = "umap", split.by = "stim")
识别保守的细胞类型标记(markers)
使用 FindConservedMarkers() 函数识别在不同条件下保守的典型细胞类型标记基因. 例如，在cluster6 (NK细胞)中，我们可以计算出不受刺激条件影响的保守标记基因。
# 为了进行分组差异分析 将默认数据改为'RNA' DefaultAssay(immune.combined) <- "RNA" BiocManager::install('multtest') install.packages('metap') library(metap) DefaultAssay(immune.combined) <- "RNA" # ident.1设置聚类标签 nk.markers <- FindConservedMarkers(immune.combined, ident.1 = 6, grouping.var = "stim", verbose = FALSE) head(nk.markers)
探索每个聚类的标记基因，使用标记基因注释细胞类型。
FeaturePlot(immune.combined, features = c("CD3D", "SELL", "CREM", "CD8A", "GNLY", "CD79A", "FCGR3A", "CCL2", "PPBP"), min.cutoff = "q9")
immune.combined <- RenameIdents(immune.combined, `0` = "CD14 Mono", `1` = "CD4 Naive T", `2` = "CD4 Memory T", `3` = "CD16 Mono", `4` = "B", `5` = "CD8 T", `6` = "NK", `7` = "T activated", `8` = "DC", `9` = "B Activated", `10` = "Mk", `11` = "pDC", `12` = "Eryth", `13` = "Mono/Mk Doublets") DimPlot(immune.combined, label = TRUE)
绘制标记基因气泡图
识别不同条件(condition)下的差异基因
library(ggplot2) library(cowplot) theme_set(theme_cowplot()) ## CD4 Naive T t.cells <- subset(immune.combined, idents = "CD4 Naive T") Idents(t.cells) <- "stim" avg.t.cells <- as.data.frame(log1p(AverageExpression(t.cells, verbose = FALSE)$RNA)) avg.t.cells$gene <- rownames(avg.t.cells) ## CD14 Mono cd14.mono <- subset(immune.combined, idents = "CD14 Mono") Idents(cd14.mono) <- "stim" avg.cd14.mono <- as.data.frame(log1p(AverageExpression(cd14.mono, verbose = FALSE)$RNA)) avg.cd14.mono$gene <- rownames(avg.cd14.mono) # 标签 genes.to.label = c("ISG15", "LY6E", "IFI6", "ISG20", "MX1", "IFIT2", "IFIT1", "CXCL10", "CCL8") # 绘制 p1 <- ggplot(avg.t.cells, aes(CTRL, STIM)) + geom_point() + ggtitle("CD4 Naive T Cells") p1 <- LabelPoints(plot = p1, points = genes.to.label, repel = TRUE) p2 <- ggplot(avg.cd14.mono, aes(CTRL, STIM)) + geom_point() + ggtitle("CD14 Monocytes") p2 <- LabelPoints(plot = p2, points = genes.to.label, repel = TRUE) p1 + p2
同一类型的细胞的不同条件下差异表达的基因。
immune.combined$celltype.stim <- paste(Idents(immune.combined), immune.combined$stim, sep = "_") immune.combined$celltype <- Idents(immune.combined) Idents(immune.combined) <- "celltype.stim" table(immune.combined@active.ident) ## 对B细胞进行差异分析 b.interferon.response <- FindMarkers(immune.combined, ident.1 = "B_STIM", ident.2 = "B_CTRL", verbose = FALSE) head(b.interferon.response, n = 15)
> head(b.interferon.response, n = 15) p_val avg_log2FC pct.1 pct.2 p_val_adj ISG15 8.657899e-156 4.5965018 0.998 0.240 1.216695e-151 IFIT3 3.536522e-151 4.5004998 0.964 0.052 4.969874e-147 IFI6 1.204612e-149 4.2335821 0.966 0.080 1.692841e-145 ISG20 9.370954e-147 2.9393901 1.000 0.672 1.316900e-142 IFIT1 8.181640e-138 4.1290319 0.912 0.032 1.149766e-133 MX1 1.445540e-121 3.2932564 0.907 0.115 2.031417e-117 LY6E 2.944234e-117 3.1187120 0.894 0.152 4.137531e-113 TNFSF10 2.273307e-110 3.7772611 0.792 0.025 3.194678e-106 IFIT2 1.676837e-106 3.6547696 0.786 0.035 2.356459e-102 B2M 3.500771e-95 0.6062999 1.000 1.000 4.919633e-91 PLSCR1 3.279290e-94 2.8249220 0.797 0.117 4.608387e-90 IRF7 1.475385e-92 2.5888616 0.838 0.190 2.073358e-88 CXCL10 1.350777e-82 5.2509496 0.641 0.010 1.898247e-78 UBE2L6 2.783283e-81 2.1427434 0.851 0.300 3.911348e-77 PSMB9 2.638374e-76 1.6367800 0.941 0.573 3.707707e-72
## 可视化基因表达 FeaturePlot(immune.combined, features = c("CD3D", "GNLY", "IFI6"), split.by = "stim", max.cutoff = 3, cols = c("grey", "red"))
## 小提琴图 plots <- VlnPlot(immune.combined, features = c("LYZ", "ISG15", "CXCL10"), split.by = "stim", group.by = "celltype", pt.size = 0, combine = FALSE) wrap_plots(plots = plots, ncol = 1)
基于SCTransform标准化进行整合分析
SCTransform标准化流程主要差别在于：
使用SCTransform()标准化, 而不是 NormalizeData() 。
使用3,000 或更多的特征进入下游分析。
使用 PrepSCTIntegration()函数识别anchors。
当使用 FindIntegrationAnchors(), 和IntegrateData(), 将参数normalization.method 设置为 SCT。
运行基于sctransform的工作流程时，包括整合，不需要使用ScaleData()函数。
## 具体流程 LoadData("ifnb") ifnb.list <- SplitObject(ifnb, split.by = "stim") ifnb.list <- lapply(X = ifnb.list, FUN = SCTransform) features <- SelectIntegrationFeatures(object.list = ifnb.list, nfeatures = 3000) ifnb.list <- PrepSCTIntegration(object.list = ifnb.list, anchor.features = features) immune.anchors <- FindIntegrationAnchors(object.list = ifnb.list, normalization.method = "SCT", anchor.features = features) immune.combined.sct <- IntegrateData(anchorset = immune.anchors, normalization.method = "SCT") immune.combined.sct <- RunPCA(immune.combined.sct, verbose = FALSE) immune.combined.sct <- RunUMAP(immune.combined.sct, reduction = "pca", dims = 1:30) p1 <- DimPlot(immune.combined.sct, reduction = "umap", group.by = "stim") p2 <- DimPlot(immune.combined.sct, reduction = "umap", group.by = "seurat_annotations", label = TRUE, repel = TRUE) p1 + p2
到此数据就整合结束了，后续可以接着前面的步骤进行细胞识别等分析。
参考
https://satijalab.org/seurat/articles/integration_introduction.html
