探序基因已入锐竞平台
要将每个样本的矩阵都单独建立Seurat对象。
如果所有样本在一个Seurat对象中,并且在meta表格中有一列记载细胞对应的样本名,可按如下方法切分:
# 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)
})
或者:
例如在Seurat对象中,meta表格的Samples记录了细胞对应的样本名,运行如下代码切分
project.Seurat[["RNA"]] <- split(project.Seurat[["RNA"]], f = project.Seurat$Samples)
最后count矩阵会按样本切分:
> project.Seurat[["RNA"]]
Assay (v5) data with xxx features for xxx cells
First 10 features:
xxx
Layers:
counts.S1, counts.S2, counts.S3, counts.S4
切分完后:
project.Seurat <- NormalizeData(project.Seurat)
project.Seurat <- FindVariableFeatures(project.Seurat)
project.Seurat <- ScaleData(project.Seurat)
project.Seurat <- RunPCA(project.Seurat)
project.Seurat <- IntegrateLayers(
object = project.Seurat, method = RPCAIntegration,
orig.reduction = "pca", new.reduction = "rpca",
verbose = FALSE
)
大细胞量评测:
细胞量:大约90万个,Seurat版本:5.5.0,SeuratObject版本:5.4.0
报错:
Warning in svd.function(A = t(x = object), nv = npcs, ...) :
You're computing too large a percentage of total singular values, use a standard svd instead.
Warning in svd.function(A = t(x = object), nv = npcs, ...) :
You're computing too large a percentage of total singular values, use a standard svd instead.
Warning in svd.function(A = t(x = object), nv = npcs, ...) :
You're computing too large a percentage of total singular values, use a standard svd instead.
Error in idx[i, ] <- res[[i]][[1]] : 被替换的项目不是替换值长度的倍数
Calls: IntegrateLayers ... resolve.list -> signalConditionsASAP -> signalConditions
停止执行
参考:
Seurat Tutorial 5:使用 reciprocal PCA (RPCA) 快速整合
https://zhuanlan.zhihu.com/p/653865719Seurat4.0系列教程13:使用RPCA快速整合数据
https://cloud.tencent.com/developer/article/1931214单细胞:IntegrateLayers函数中这几种主流的单细胞数据整合方法
https://cloud.tencent.com/developer/article/2616679