b.limour.top/source/_posts/使用seurat-v3标准整合流程移除批次效应.md

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title: 使用Seurat v3标准整合流程移除批次效应 tags: [] id: '1973' categories:

• - uncategorized date: 2022-08-13 08:22:46 ---

之前试了文献提供的去除批次效应的代码，发现效果非常差，还是用标准流程走一下看看吧

读入数据

library(scran);
library(tidyverse);
library(Seurat);
sce <- readRDS('scRNA.rds')
sce <- as.Seurat(sce)
sceL <- SplitObject(object = sce, split.by = 'Batch')
table(sce[['Batch']])

分开的10x数据文件夹

root_path = "."
f_read10x <- function(dirN, ...){
    tp_samples <- list.files(file.path(root_path, dirN))
    tp_dir <- file.path(root_path, dirN, tp_samples)
    names(tp_dir) <- tp_samples
    scRNA <- list()
    for (lc_ba in names(tp_dir)){
        counts <- Read10X(data.dir = tp_dir[[lc_ba]])
        scRNA[[lc_ba]] <- CreateSeuratObject(counts, project = lc_ba, ...)
        scRNA[[lc_ba]][["percent.mt"]] <- PercentageFeatureSet(scRNA[[lc_ba]], pattern = "^MT-")
        scRNA[[lc_ba]][["percent.ERCC"]] <- PercentageFeatureSet(scRNA[[lc_ba]], pattern = "^ERCC-")
    }
    scRNA
}

标准整合流程

# sceL <- lapply(X = sceL, FUN = function(x){NormalizeData(testA.seu, normalization.method = "LogNormalize", scale.factor = 10000)})
sceL <- lapply(X = sceL, FUN = function(x){FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)})
sceL.anchors <- FindIntegrationAnchors(object.list = sceL, dims = 1:30)
sceL.integrated <- IntegrateData(anchorset = sceL.anchors, dims = 1:30)
saveRDS(sceL.integrated, 'sceL.integrated.rds')

最终效果

sce <- as.SingleCellExperiment(sceL.integrated)
library(scater);
options(repr.plot.width=7, repr.plot.height=6)
plotTSNE(sce, colour_by="Batch")

emm，效果差强人意