b.limour.top/source/_posts/singler-四-搭建测试环境.md

---

title: SingleR (四) 搭建测试环境 tags: [] id: '703' categories:

• - Seurat教程

• • 生物信息学

    date: 2021-09-21 01:00:14

    library(Seurat) ##
    library(SingleR)
    library(ggplot2)
    library(reshape2)
    library(SeuratData)
    pbmc <- pbmc3k
    library(BiocParallel) # 并行计算加速
    hpca.se=HumanPrimaryCellAtlasData() ##第一次载入会下载数据集，可能会慢一些，后面在用时就不用下载了
    Blue.se=BlueprintEncodeData() 
    
    # 配置结果保存路径
    output_path = "~/zlliu/R_output/21.09.21.SingleR"
    if (!file.exists(output_path)){dir.create(output_path)}
     
    # 设置工作目录，输出文件将保存在此目录下
    setwd(output_path)
    getwd()
    
    hM1.se  <- list()
    hmca <- list()
    
    # 2、进行预测
    data_for_SingleR = pbmc[["RNA"]]@data
    
    lc_hM1.se <- Blue.se
    lc_hmca <- hpca.se
    
    hM1.se$meta$subclass_label <- Blue.se$label.main
    hmca$meta$subclass_label <- hpca.se$label.main
    
    # 进行分类预测
    pred_1 <- SingleR(test = data_for_SingleR, ref = list(m1=lc_hM1.se, mca=lc_hmca),
                      labels = list(hM1.se$meta$subclass_label, hmca$meta$subclass_label),
                      BPPARAM=MulticoreParam(32)) # 32CPUs
    
    pred_2 <- SingleR(test = data_for_SingleR, ref = lc_hM1.se,
                      labels = hM1.se$meta$subclass_label,
                      BPPARAM=MulticoreParam(32)) # 32CPUs
    
    pred_3 <- SingleR(test = data_for_SingleR, ref = lc_hmca,
                      labels = hmca$meta$subclass_label,
                      BPPARAM=MulticoreParam(32)) # 32CPUs
    
    # 保存结果
    f_merge  <- function(lc_mergedList){
    Reduce(function(...) merge(..., by="CB"), lc_mergedList)
    }
    
    f_pred2meta <- function(lc_pred, lc_colname){
    lc_result  <- as.data.frame(lc_pred$labels)
    lc_result$CB  <- rownames(lc_pred)
    colnames(lc_result) <- c(lc_colname, 'CB')
    lc_result
    }
    
    pred_1  <- f_pred2meta(pred_1, "hM1_hmca_class")
    pred_2  <- f_pred2meta(pred_2, "hM1_class")
    pred_3  <- f_pred2meta(pred_3, "hmca_class")