# 构造Pseudo-bulk的表达量矩阵加速SingleR运行

The Human Cell Atlas 和 CZ CELLxGENE 的单细胞数据集有些metadata里有细胞类型注释。我们使用前面下载的数据集来构建一个SingleR的参考集。

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    sce <- readRDS('~/HumanCellAtlas/ProstateCellAtlas/cellxgene_Human_prostate.rds')
    table(sce$tissue)
    sce <- SeuratObject::UpdateSeuratObject(sce)
    saveRDS(sce@meta.data, 'meta.rds')
    sce@assays$RNA@counts
    umi <- sce@assays$RNA@counts
    sce <- Seurat::CreateSeuratObject(counts = umi, project = 'prostate', min.cells = 3, min.features = 200)
    sce@meta.data <- readRDS('meta.rds')
    all(colnames(sce) == rownames(sce@meta.data))
    sce <- subset(sce, tissue == 'prostate gland')
    gc()
    table(sce$`Broad cell type`)
    table(sce$`Granular cell type`)
    table(sce$`Granular cell type`)
    table(sce$`Tissue composition`)
    table(sce$`Cell types level 2`)
    table(sce$`Cell types level 3`)
    sce$cell_t_1 <- droplevels(sce$`Tissue composition`)
    all(as.character(sce$cell_t_1) == as.character(sce$`Tissue composition`))
    sce$cell_t_2 <- droplevels(sce$`Cell types level 2`)
    sce$cell_t_3 <- droplevels(sce$`Cell types level 3`)
    sce <-  Seurat::NormalizeData(sce)
    ref <- list()
    ref$cell_t_1 <- Seurat::AverageExpression(sce,
                              group.by = "cell_t_1",
                              assays = "RNA")$RNA
    ref$cell_t_2 <- Seurat::AverageExpression(sce,
                              group.by = "cell_t_2",
                              assays = "RNA")$RNA
    ref$cell_t_3 <- Seurat::AverageExpression(sce,
                              group.by = "cell_t_3",
                              assays = "RNA")$RNA
    saveRDS(ref, 'singleR_prostate.rds')

注释
构造Pseudo-bulk的表达量矩阵加速SingleR运行
https://occdn.limour.top/2392.html
Author
Limour
Posted on
October 4, 2022
Licensed under
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