六、左右倒卷肱
1、右倒卷肱
稍右轉(zhuǎn)體 撤手托球 退步卷肱 虛步推掌
2、左倒卷肱
稍左轉(zhuǎn)體 撤手托球 退步卷肱 虛步推掌
3、右倒卷肱
稍右轉(zhuǎn)體 撤手托球 退步卷肱 虛步推掌
4、左倒卷肱
稍左轉(zhuǎn)體 撤手托球 退步卷肱 虛步推掌

在此文中，我們提出了略微修改的工作流程，用于整合scRNA-seq數(shù)據(jù)集。與其使用典范的相關(guān)分析（'CCA'）來識別錨點(diǎn)，不如使用互惠的PCA（'RPCA'）。在使用RPCA確定任何兩個數(shù)據(jù)集之間的錨點(diǎn)時，我們將每個數(shù)據(jù)集投影到其他PCA空間中，并通過相同的相互鄰域要求約束這些錨點(diǎn)。這兩個工作流程的命令在很大程度上是相同的，但是這兩種方法可以在不同的上下文中應(yīng)用。

通過識別數(shù)據(jù)集之間共享的變異來源，CCA非常適合在保存細(xì)胞類型時識別錨點(diǎn)，但是整個實(shí)驗(yàn)中的基因表達(dá)存在非常大的差異。因此，基于CCA的集成可以在實(shí)驗(yàn)條件或疾病狀態(tài)引入非常強(qiáng)的表達(dá)偏移時，或者在跨模式和物種整合數(shù)據(jù)集時啟用集成分析。但是，基于CCA的集成也可能導(dǎo)致過度校正，尤其是當(dāng)大部分單元在數(shù)據(jù)集中不重疊時。

基于RPCA的整合運(yùn)行速度明顯加快，并且也代表了一種更為保守的方法，即處于不同生物學(xué)狀態(tài)的細(xì)胞整合后不太可能“對齊”。因此，我們建議在進(jìn)行綜合分析時使用RPCA，其中：一個數(shù)據(jù)集中的大部分單元格中的其他單元格都不具有匹配類型數(shù)據(jù)集源自同一平臺（例如，多個泳道的10x基因組學(xué)）*存在大量的數(shù)據(jù)集或要集成的單元格（有關(guān)集成大型數(shù)據(jù)集的更多提示，請參閱INSERT LINK）

下面，我們演示了使用倒數(shù)PCA來對齊在我們對scRNA-seq整合小插圖的介紹中首先分析的相同刺激數(shù)據(jù)和靜止數(shù)據(jù)集。盡管命令列表幾乎相同，但是此工作流要求用戶在集成之前分別在每個數(shù)據(jù)集上運(yùn)行主成分分析（PCA）。在運(yùn)行時，用戶還應(yīng)將'reduction'參數(shù)設(shè)置為'rpca' FindIntegrationAnchors()`。

library(SeuratData)
# 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 run PCA on each
# dataset using these features
features <- SelectIntegrationFeatures(object.list = ifnb.list)
ifnb.list <- lapply(X = ifnb.list, FUN = function(x) {
    x <- ScaleData(x, features = features, verbose = FALSE)
    x <- RunPCA(x, features = features, verbose = FALSE)
})

執(zhí)行整合

然后，我們使用FindIntegrationAnchors()`函數(shù)來識別錨點(diǎn)，該函數(shù)將Seurat對象的列表作為輸入，并使用這些錨點(diǎn)將兩個數(shù)據(jù)集與集成在一起IntegrateData()。

immune.anchors <- FindIntegrationAnchors(object.list = ifnb.list, anchor.features = features, reduction = "rpca")

# this command creates an 'integrated' data assay
immune.combined <- IntegrateData(anchorset = immune.anchors)

現(xiàn)在，我們可以在所有單元上運(yùn)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", group.by = "seurat_annotations", label = TRUE, 
    repel = TRUE)
p1 + p2

image

修改整合實(shí)力

結(jié)果表明，基于rpca的整合更為保守，在這種情況下，無法在整個實(shí)驗(yàn)中完美對齊細(xì)胞子集（即幼稚的T細(xì)胞和記憶T細(xì)胞）。您可以通過增加k.anchor參數(shù)來增加對齊強(qiáng)度，該參數(shù)默認(rèn)情況下設(shè)置為5。將此參數(shù)增加到20將有助于對齊這些總體。

immune.anchors <- FindIntegrationAnchors(object.list = ifnb.list, anchor.features = features, reduction = "rpca", 
    k.anchor = 20)
immune.combined <- IntegrateData(anchorset = immune.anchors)

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)

## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 13999
## Number of edges: 594589
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9128
## Number of communities: 15
## Elapsed time: 4 seconds

# Visualization
p1 <- DimPlot(immune.combined, reduction = "umap", group.by = "stim")
p2 <- DimPlot(immune.combined, reduction = "umap", label = TRUE, repel = TRUE)
p1 + p2

image

現(xiàn)在已經(jīng)集成了數(shù)據(jù)集，您可以按照scRNA-seq整合小插圖介紹中的先前步驟進(jìn)行操作，以識別細(xì)胞類型和特定于細(xì)胞類型的反應(yīng)。

對使用SCTransform標(biāo)準(zhǔn)化的數(shù)據(jù)集執(zhí)行集成

再舉一個例子，我們重復(fù)上面的分析，但是使用SCTransform標(biāo)準(zhǔn)化了數(shù)據(jù)集。我們可以選擇將method參數(shù)設(shè)置為glmGamPoi（在此處安裝），以便能夠更快地估算中的回歸參數(shù)SCTransform()`。

LoadData("ifnb")
ifnb.list <- SplitObject(ifnb, split.by = "stim")
ifnb.list <- lapply(X = ifnb.list, FUN = SCTransform, method = "glmGamPoi")
features <- SelectIntegrationFeatures(object.list = ifnb.list, nfeatures = 3000)
ifnb.list <- PrepSCTIntegration(object.list = ifnb.list, anchor.features = features)
ifnb.list <- lapply(X = ifnb.list, FUN = RunPCA, features = features)

immune.anchors <- FindIntegrationAnchors(object.list = ifnb.list, normalization.method = "SCT", 
    anchor.features = features, dims = 1:30, reduction = "rpca", k.anchor = 20)
immune.combined.sct <- IntegrateData(anchorset = immune.anchors, normalization.method = "SCT", dims = 1:30)

immune.combined.sct <- RunPCA(immune.combined.sct, verbose = FALSE)
immune.combined.sct <- RunUMAP(immune.combined.sct, reduction = "pca", dims = 1:30)

# Visualization
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

image.png