# 使用metacell进行分群聚类

## 预处理

    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    20
    21
    22
    23
    24
    25
    26
    27
    28
    29
    30
    31

    f_QC_plot <- function(sce){
        options(repr.plot.width = 12, repr.plot.height = 6)
        print(Seurat::VlnPlot(sce, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3))
        plot1 <- Seurat::FeatureScatter(sce, feature1 = "nCount_RNA", feature2 = "percent.mt")
        plot2 <- Seurat::FeatureScatter(sce, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
        plot1 + plot2
    }
    f_read10x <- function(sce, project='sce'){
        sce <- Seurat::CreateSeuratObject(sce, project = project,
                                min.cells = 3, min.features = 200)

        sce[["percent.mt"]] <- Seurat::PercentageFeatureSet(sce, pattern = "^MT-")
        sce[["percent.rp"]] <- Seurat::PercentageFeatureSet(sce, pattern = "^RP[SL]")
        sce <- subset(sce, nFeature_RNA >= quantile(nFeature_RNA, 0.025)
                      & nFeature_RNA <= quantile(nFeature_RNA, 0.975)
                      & nCount_RNA >= quantile(nCount_RNA, 0.025)
                      & nCount_RNA <= quantile(nCount_RNA, 0.975)
                      & percent.mt <= quantile(percent.mt, 0.975))
        sce <- Seurat::NormalizeData(sce)
        g2m_genes <- Seurat::CaseMatch(search=Seurat::cc.genes$g2m.genes,
                                       match=rownames(sce))
        s_genes <- Seurat::CaseMatch(search=Seurat::cc.genes$s.genes,
                                     match=rownames(sce))
        sce <- Seurat::CellCycleScoring(sce, g2m.features=g2m_genes, s.features=s_genes)
        sce$CC.Difference <- sce$S.Score - sce$G2M.Score
        sce <- sce[!grepl(pattern = "(^MT-^RP[SL])",x = rownames(sce)),]
        sce <- Seurat::SCTransform(sce, vst.flavor = "v2",
                               vars.to.regress = c("CC.Difference", "percent.mt", "percent.rp"),
                               verbose = F)
        sce
    }

    1
    2

    sce <- Seurat::Read10X('filtered_feature_bc_matrix')
    sce <- f_read10x(sce, project = 'SRX8890106')

## 安装补充包

- conda activate seurat
- ~/dev/xray/xray -c ~/etc/xui2.json &
- wget -e “https_proxy=http://127.0.0.1:20809“ https://github.com/tanaylab/metacell/archive/refs/heads/master.zip -O metacell-master.zip
- devtools::install_local(‘metacell-master.zip’)
- conda install -c bioconda bioconductor-singlecellexperiment -y

## seurat转metacell

    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    16
    17

    ###### 构建metacell对象
    ## 初始化
    # 设置存放数据的目录
    if(!dir.exists("scdb")){dir.create("scdb")}
    metacell::scdb_init("scdb", force_reinit=T)
    # 设置存放图形的目录
    if(!dir.exists("figs")){dir.create("figs")}
    metacell::scfigs_init("figs")
    ## 提取高变基因
    var.genes <- Seurat::VariableFeatures(sce)
    var.genes <- structure(rep(1:length(var.genes)), names=var.genes)
    var.genes <- metacell::gset_new_gset(sets = var.genes, desc = "seurat variable genes")
    metacell::scdb_add_gset("SRX8890106", var.genes)
    ## 提取counts矩阵
    mat <- Seurat::as.SingleCellExperiment(sce)
    mat <- metacell::scm_import_sce_to_mat(mat)
    metacell::scdb_add_mat("SRX8890106", mat)

## 聚类MetaCell

    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    20
    21

    ## 构建平衡KNN图
    metacell::mcell_add_cgraph_from_mat_bknn(mat_id = "SRX8890106",
                                   gset_id = "SRX8890106",
                                   graph_id = "SRX8890106_k100",
                                   K = 100,
                                   dsamp = F)  # 20,000 cells之内不必抽样
    ## 共聚类
    metacell::mcell_coclust_from_graph_resamp(coc_id = "SRX8890106_n1000", graph_id = "SRX8890106_k100",
                                    min_mc_size = 20,  p_resamp = 0.75, n_resamp=1000)
    ## 生成初级metacell
    metacell::mcell_mc_from_coclust_balanced(coc_id = "SRX8890106_n1000", mat_id = "SRX8890106", mc_id = "SRX8890106",
                                   K = 20, min_mc_size = 20, alpha = 2)
    ## 修剪metacell
    metacell::mcell_plot_outlier_heatmap(mc_id = "SRX8890106", mat_id = "SRX8890106", T_lfc = 3)
    metacell::mcell_mc_split_filt(new_mc_id = "SRX8890106", mc_id = "SRX8890106", mat_id = "SRX8890106", T_lfc = 3, plot_mats = T)
    ## 2D图展示Cells与MCs
    metacell::mc_colorize_default('SRX8890106')
    metacell::mcell_mc2d_force_knn(mc2d_id="SRX8890106", mc_id="SRX8890106", graph_id="SRX8890106_k100")
    tgconfig::set_param("mcell_mc2d_height", 1000, "metacell")
    tgconfig::set_param("mcell_mc2d_width", 1000, "metacell")
    metacell::mcell_mc2d_plot(mc2d_id = "SRX8890106")

## 导出MetaCell到seurat

    1
    2
    3
    4

    mc <- metacell::scdb_mc('SRX8890106')
    sce$metacell <- 0
    sce$metacell[names(mc@mc)] <- mc@mc
    saveRDS(sce@meta.data, 'SRX8890106_meta.rds')

分群 预处理
使用metacell进行分群聚类
https://occdn.limour.top/2366.html
Author
Limour
Posted on
October 1, 2022
Licensed under
使用DoubletFinder标注Doublet Previous
使用FindTransferAnchors对样本进行预注释 Next

#### Search

Keyword