第五計 趁火打劫
本指趁人家失火的時候去搶東西『萁牵現(xiàn)比喻乘人之危，撈一把蚪腋。是以“剛”喻己丰歌，以“柔”喻敵，言乘敵之危屉凯，就勢而取勝立帖。
scRNA-seq整合簡介
對兩個或多個單細胞數(shù)據(jù)集的聯(lián)合分析提出了獨特的挑戰(zhàn)。特別是悠砚，在標準工作流程下晓勇，識別多個數(shù)據(jù)集中存在的細胞群體可能會成問題。Seurat v4包括一組用于匹配（或“對齊”）跨數(shù)據(jù)集的共享細胞群體的方法。這些方法首先確定處于匹配生物學狀態(tài)（“錨”）的細胞的跨數(shù)據(jù)集對宵蕉，既可以用于校正數(shù)據(jù)集之間的技術差異（即批效應校正）酝静，也可以用于對基因組進行比較性scRNA-seq分析跨實驗條件。
下面羡玛，我們展示了Stuart *别智，Butler *等人，2019中所述的scRNA-seq整合方法稼稿，以對處于靜止或干擾素刺激狀態(tài)的人免疫細胞（PBMC）進行比較分析薄榛。
整合目標
以下教程旨在概述使用Seurat集成過程可能進行的復雜細胞類型的比較分析。在這里让歼，我們解決了一些關鍵目標：
- 創(chuàng)建“集成”數(shù)據(jù)分析以進行下游分析
- 識別兩個數(shù)據(jù)集中都存在的單元格類型
- 獲得在對照和刺激細胞中均保守的細胞類型標記
- 比較數(shù)據(jù)集以找到對刺激的細胞類型特異性反應
設置Seurat對象
為了方便起見彻磁，我們通過SeuratData軟件包分發(fā)此數(shù)據(jù)集。
library(Seurat)
library(SeuratData)
library(patchwork)
# install dataset
InstallData("ifnb")
# load dataset
LoadData("ifnb")
# split the dataset into a list of two seurat objects (stim and CTRL)
ifnb.list <- SplitObject(ifnb, split.by = "stim")
# normalize and identify variable features for each dataset independently
ifnb.list <- lapply(X = ifnb.list, FUN = function(x) {
x <- NormalizeData(x)
x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)
})
# select features that are repeatedly variable across datasets for integration
features <- SelectIntegrationFeatures(object.list = ifnb.list)
執(zhí)行整合
然后刃永，我們使用FindIntegrationAnchors()函數(shù)來識別錨點育八，該函數(shù)將Seurat對象的列表作為輸入，并使用這些錨點將兩個數(shù)據(jù)集與集成在一起IntegrateData()`改执。
immune.anchors <- FindIntegrationAnchors(object.list = ifnb.list, anchor.features = features)
# this command creates an 'integrated' data assay
immune.combined <- IntegrateData(anchorset = immune.anchors)
進行綜合分析
現(xiàn)在啸蜜，我們可以在所有單元上運行單個集成分析！
# specify that we will perform downstream analysis on the corrected data note that the original
# unmodified data still resides in the 'RNA' assay
DefaultAssay(immune.combined) <- "integrated"
# Run the standard workflow for visualization and clustering
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)
# Visualization
p1 <- DimPlot(immune.combined, reduction = "umap", group.by = "stim")
p2 <- DimPlot(immune.combined, reduction = "umap", label = TRUE, repel = TRUE)
p1 + p2
為了并排可視化這兩個條件辈挂，我們可以使用split.by參數(shù)來顯示每個以聚類著色的條件衬横。
DimPlot(immune.combined, reduction = "umap", split.by = "stim")
識別保守的細胞類型標記
為了鑒定在各種條件下保守的規(guī)范細胞類型標記基因，我們提供了該FindConservedMarkers()`功能终蒂。此功能對每個數(shù)據(jù)集/組執(zhí)行差異基因表達測試蜂林，并使用MetaDE R軟件包中的薈萃分析方法組合p值。例如拇泣，無論簇6中的刺激條件如何噪叙，我們都可以計算出保守標記的基因（NK細胞）。
# For performing differential expression after integration, we switch back to the original data
DefaultAssay(immune.combined) <- "RNA"
nk.markers <- FindConservedMarkers(immune.combined, ident.1 = 6, grouping.var = "stim", verbose = FALSE)
head(nk.markers)
## CTRL_p_val CTRL_avg_log2FC CTRL_pct.1 CTRL_pct.2 CTRL_p_val_adj
## GNLY 0 6.006422 0.944 0.045 0
## FGFBP2 0 3.223246 0.503 0.020 0
## CLIC3 0 3.466418 0.599 0.024 0
## PRF1 0 2.654683 0.424 0.017 0
## CTSW 0 2.991829 0.533 0.029 0
## KLRD1 0 2.781453 0.497 0.019 0
## STIM_p_val STIM_avg_log2FC STIM_pct.1 STIM_pct.2 STIM_p_val_adj
## GNLY 0.000000e+00 5.853573 0.956 0.060 0.000000e+00
## FGFBP2 7.275492e-161 2.200379 0.260 0.016 1.022425e-156
## CLIC3 0.000000e+00 3.549919 0.627 0.031 0.000000e+00
## PRF1 0.000000e+00 4.102686 0.862 0.057 0.000000e+00
## CTSW 0.000000e+00 3.139620 0.596 0.035 0.000000e+00
## KLRD1 0.000000e+00 2.880055 0.558 0.027 0.000000e+00
## max_pval minimump_p_val
## GNLY 0.000000e+00 0
## FGFBP2 7.275492e-161 0
## CLIC3 0.000000e+00 0
## PRF1 0.000000e+00 0
## CTSW 0.000000e+00 0
## KLRD1 0.000000e+00 0
我們可以為每個簇探索這些標記基因霉翔，并使用它們將我們的簇注釋為特定的細胞類型构眯。
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", `14` = "HSPC")
DimPlot(immune.combined, label = TRUE)
DotPlot()帶有split.by`參數(shù)的函數(shù)可用于查看各種條件下的保守細胞類型標記，顯示表達水平和表達任何給定基因的簇中細胞的百分比早龟。在這里惫霸，我們?yōu)?4個簇中的每個簇繪制2-3個強標記基因。
Idents(immune.combined) <- factor(Idents(immune.combined), levels = c("HSPC", "Mono/Mk Doublets",
"pDC", "Eryth", "Mk", "DC", "CD14 Mono", "CD16 Mono", "B Activated", "B", "CD8 T", "NK", "T activated",
"CD4 Naive T", "CD4 Memory T"))
markers.to.plot <- c("CD3D", "CREM", "HSPH1", "SELL", "GIMAP5", "CACYBP", "GNLY", "NKG7", "CCL5",
"CD8A", "MS4A1", "CD79A", "MIR155HG", "NME1", "FCGR3A", "VMO1", "CCL2", "S100A9", "HLA-DQA1",
"GPR183", "PPBP", "GNG11", "HBA2", "HBB", "TSPAN13", "IL3RA", "IGJ", "PRSS57")
DotPlot(immune.combined, features = markers.to.plot, cols = c("blue", "red"), dot.scale = 8, split.by = "stim") +
RotatedAxis()
確定跨條件的差異表達基因
現(xiàn)在葱弟，我們已經(jīng)排列了刺激細胞和對照細胞壹店，我們可以開始進行比較分析，并觀察刺激引起的差異芝加。廣泛觀察這些變化的一種方法是繪制受刺激細胞和對照細胞的平均表達硅卢，并在散點圖上尋找視覺異常值的基因射窒。在這里，我們采用受刺激的和對照的原始T細胞和CD14單核細胞群體的平均表達将塑，并生成散點圖脉顿，突出顯示對干擾素刺激表現(xiàn)出戲劇性反應的基因。
library(ggplot2)
library(cowplot)
theme_set(theme_cowplot())
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 <- 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)
genes.to.label = c("ISG15", "LY6E", "IFI6", "ISG20", "MX1", "IFIT2", "IFIT1", "CXCL10", "CCL8")
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
如您所見点寥，許多相同的基因在這兩種細胞類型中均被上調艾疟，可能代表保守的干擾素應答途徑。
因為我們有信心確定出跨條件的常見細胞類型敢辩，所以我們可以詢問相同條件下不同條件下哪些基因會發(fā)生變化蔽莱。首先，我們在meta.data插槽中創(chuàng)建一列戚长，以保存細胞類型和刺激信息盗冷，并將當前標識切換到該列。然后同廉，我們用于FindMarkers()`查找受激B細胞和對照B細胞之間不同的基因仪糖。請注意，此處顯示的許多頂級基因與我們之前繪制的核心干擾素應答基因相同迫肖。此外锅劝，我們看到的像CXCL10的基因對單核細胞和B細胞干擾素的反應也具有特異性，在該列表中也顯示出很高的意義咒程。
immune.combined$celltype.stim <- paste(Idents(immune.combined), immune.combined$stim, sep = "_")
immune.combined$celltype <- Idents(immune.combined)
Idents(immune.combined) <- "celltype.stim"
b.interferon.response <- FindMarkers(immune.combined, ident.1 = "B_STIM", ident.2 = "B_CTRL", verbose = FALSE)
head(b.interferon.response, n = 15)
## p_val avg_log2FC pct.1 pct.2 p_val_adj
## ISG15 5.398167e-155 4.5889194 0.998 0.240 7.586044e-151
## IFIT3 2.209577e-150 4.5032297 0.964 0.052 3.105118e-146
## IFI6 7.060888e-150 4.2375542 0.969 0.080 9.922666e-146
## ISG20 7.147214e-146 2.9387415 1.000 0.672 1.004398e-141
## IFIT1 7.650201e-138 4.1295888 0.914 0.032 1.075083e-133
## MX1 1.124186e-120 3.2883709 0.905 0.115 1.579819e-116
## LY6E 2.504364e-118 3.1297866 0.900 0.152 3.519383e-114
## TNFSF10 9.454398e-110 3.7783774 0.791 0.025 1.328627e-105
## IFIT2 1.672384e-105 3.6569980 0.783 0.035 2.350201e-101
## B2M 5.564362e-96 0.6100242 1.000 1.000 7.819599e-92
## PLSCR1 1.128239e-93 2.8205802 0.796 0.117 1.585514e-89
## IRF7 6.602529e-92 2.5832239 0.838 0.190 9.278534e-88
## CXCL10 4.402118e-82 5.2406913 0.639 0.010 6.186297e-78
## UBE2L6 2.995453e-81 2.1487435 0.852 0.300 4.209510e-77
## PSMB9 1.755809e-76 1.6379482 0.940 0.573 2.467438e-72
可視化基因表達中這些變化的另一種有用方法是split.by選擇FeaturePlot()或VlnPlot()功能鸠天。這將顯示給定基因列表的FeaturePlots讼育，并按分組變量（此處為刺激條件）進行劃分帐姻。諸如CD3D和GNLY之類的基因是典型的細胞類型標記（對于T細胞和NK / CD8 T細胞），實際上不受干擾素刺激的影響奶段，并且在對照組和受刺激組中顯示出相似的基因表達模式饥瓷。另一方面，IFI6和ISG15是核心干擾素反應基因痹籍，因此在所有細胞類型中均被上調呢铆。最后，CD14和CXCL10是顯示細胞類型特異性干擾素應答的基因蹲缠。CD14單核細胞受刺激后棺克，CD14表達下降，這可能導致在有監(jiān)督的分析框架中進行錯誤分類线定，從而強調了整合分析的價值娜谊。
FeaturePlot(immune.combined, features = c("CD3D", "GNLY", "IFI6"), split.by = "stim", max.cutoff = 3,
cols = c("grey", "red"))
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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)
