{"id":29419,"date":"2024-02-22T18:00:21","date_gmt":"2024-02-22T10:00:21","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=29419"},"modified":"2024-02-22T18:00:23","modified_gmt":"2024-02-22T10:00:23","slug":"%e6%b5%85%e8%b0%88%e5%8d%95%e7%bb%86%e8%83%9e%e5%88%86%e6%9e%90%e6%b5%81%e7%a8%8b","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=29419","title":{"rendered":"\u6d45\u8c08\u5355\u7ec6\u80de\u5206\u6790\u6d41\u7a0b"},"content":{"rendered":"

\u5355\u7ec6\u80de\u5206\u6790\u5728\u73b0\u5728\u975e\u5e38\u706b\uff0c\u4eca\u5929\u5c0f\u679c\u4e5f\u60f3\u5e26\u7740\u5927\u5bb6\u5f00\u59cb\u5b66\u4e60\u68b3\u7406\u4e00\u4e0b\u5355\u7ec6\u80de\u5206\u6790\u6d41\u7a0b\uff0c\u7b80\u5355\u719f\u6089\u4e00\u4e0b\u5355\u7ec6\u80de\u5206\u6790\u6240\u5305\u542b\u7684\u5185\u5bb9\uff0c\u5206\u6790\u5185\u5bb9\u5982\u4e0b\uff1a<\/p>\n

    \n
  1. \u5b89\u88c5\u5355\u7ec6\u80de\u5206\u6790R\u5305<\/li>\n<\/ol>\n

    install.packages(\u201cBiocManager\u201d)<\/p>\n

    BiocManager::install(\u201cSeurat\u201d)<\/p>\n

    library(Seurat)<\/p>\n

      \n
    1. \u5355\u7ec6\u80de\u6d4b\u5e8f\u6587\u4ef6\u7684\u8bfb\u5165\u548cSeurat\u5bf9\u8c61\u6784\u5efa<\/li>\n<\/ol>\n

      #\u8bfb\u53d6\u6570\u636e\u6240\u5728\u7684\u6587\u4ef6\u5939-\u6807\u51c6\u768410X\u6587\u4ef6<\/p>\n

      pbmc.data <- Read10X(data.dir = “filtered_gene_bc_matrices\/hg19\/”)<\/p>\n

      \"Dingtalk_20230308103719\"<\/p>\n

      #\u521b\u5efaSeurat\u6587\u4ef6<\/p>\n

      pbmc <- CreateSeuratObject(counts = pbmc.data, project = “pbmc3k”, min.cells = 3, min.features = 200)<\/p>\n

      min.cells\u8868\u793a\u7b5b\u9009\u81f3\u5c11\u57283\u4e2a\u7ec6\u80de\u4e2d\u8868\u8fbe\u7684\u57fa\u56e0<\/p>\n

      min.features\u8868\u793a\u7b5b\u9009\u81f3\u5c11\u6709200\u4e2a\u57fa\u56e0\u8868\u8fbe\u7684\u7ec6\u80de<\/p>\n

        \n
      1. \u5355\u7ec6\u80de\u6570\u636e\u8d28\u63a7(QC)<\/li>\n<\/ol>\n

        #\u5148\u8ba1\u7b97\u7ebf\u7c92\u4f53\u6bd4\u4f8b<\/p>\n

        pbmc[[“percent.mt”]] <- PercentageFeatureSet(pbmc, pattern = “^MT-“)<\/p>\n

        #\u7528\u5c0f\u63d0\u7434\u56fe\u5c55\u793a\u63a7\u524d\u6307\u6807<\/p>\n

        VlnPlot(pbmc, features = c(“nFeature_RNA”, “nCount_RNA”, “percent.mt”), ncol = 3\uff0cgroup.by=\u201dorig.ident\u201d,pt.size=0))<\/p>\n

        \"Dingtalk_20230308132956\"<\/p>\n

        #\u8fdb\u884c\u8d28\u63a7(\u53ef\u4ee5\u6839\u636e\u81ea\u5df1\u7684\u6570\u636e\u8fdb\u884c\u8fc7\u6ee4)<\/p>\n

        pbmc<-subset(pbmc,nFeature_RNA>200 &nFeature_RNA<5000 &percent.mt <15)<\/p>\n

        VlnPlot(pbmc, features = c(“nFeature_RNA”, “nCount_RNA”, “percent.mt”), ncol = 3\uff0cgroup.by=\u201dorig.ident\u201d,pt.size=0)<\/p>\n

          \n
        1. \u5355\u7ec6\u80de\u6570\u636e\u6807\u51c6\u5316\u53ca\u8ba1\u7b97\u9ad8\u53d8\u57fa\u56e0<\/li>\n<\/ol>\n

          #\u6570\u636e\u6807\u51c6\u5316<\/p>\n

          pbmc <- NormalizeData(pbmc, normalization.method = “LogNormalize”, scale.factor = 10000)<\/p>\n

          \u7528log\u6807\u51c6\u5316\u7b49\u65b9\u5f0f\u964d\u4f4e\u5176\u4ed6\u5e72\u6270\u56e0\u7d20\u7684\u5f71\u54cd<\/p>\n

          #\u8ba1\u7b97\u9ad8\u53d8\u5f02\u57fa\u56e0<\/p>\n

          pbmc <- FindVariableFeatures(pbmc, selection.method = “vst”, nfeatures = 2000)<\/p>\n

          all.genes <- rownames(pbmc)<\/p>\n

          pbmc <- ScaleData(pbmc, features = all.genes)<\/p>\n

            \n
          1. \u5355\u7ec6\u80de\u6570\u636eUMAP\u964d\u7ef4\u805a\u7c7b<\/li>\n<\/ol>\n

            pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))<\/p>\n

            #\u7ec6\u80de\u805a\u7c7b<\/p>\n

            pbmc <- FindNeighbors(pbmc, dims = 1:10)<\/p>\n

            #\u8ba1\u7b97SNN<\/p>\n

            pbmc <- FindClusters(pbmc, resolution = 0.5)<\/p>\n

            #UMAP\u975e\u7ebf\u6027\u964d\u7ef4\u5206\u6790<\/p>\n

            pbmc <- RunUMAP(pbmc, dims = 1:10)<\/p>\n

            #\u7ed8\u5236UMAP\u964d\u7ef4\u805a\u7c7b\u56fe<\/p>\n

            DimPlot(pbmc, reduction = “umap”, label=T)+ NoLegend()<\/p>\n

            \"Dingtalk_20230308134139\"<\/p>\n

              \n
            1. \u5355\u7ec6\u80de\u6570\u636emarker\u57fa\u56e0\u9274\u5b9a<\/li>\n<\/ol>\n

              pbmc.markers <- FindAllMarkers(pbmc, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)<\/p>\n

              signifcant.markers<-pbmc.markers[pbmc.markers$p_val_adj<0.2,]<\/p>\n

              #\u4fdd\u5b58marker\u57fa\u56e0\u6570\u636e<\/p>\n

              write.csv(signifcant.markers,file=\u201dsignifcant.markers.csv\u201d)<\/p>\n

              #marker\u57fa\u56e0\u53ef\u89c6\u5316<\/p>\n

              #\u53ef\u4ee5\u7528\u81ea\u5df1\u611f\u5174\u8da3\u7684marker\u57fa\u56e0\u8fdb\u884c\u5c55\u793a<\/p>\n

              markers<-c(\u201cLDHB\u201d,\u201cCD3D\u201d,\u201dNOSIP\u201d,\u201dIL7R\u201d)<\/p>\n

              ##UMAP\u56fe<\/p>\n

              FeaturePlot(pbmc,features=markers)<\/p>\n

              \"Dingtalk_20230308142154\"<\/p>\n

              ##\u70b9\u56fe<\/p>\n

              DotPlot(pbmc,features=markers)+coord_flip()<\/p>\n

              \"Dingtalk_20230308142640\"<\/p>\n

              ##\u70ed\u56fe<\/p>\n

              \"Dingtalk_20230308143103\"<\/p>\n

                \n
              1. \u5355\u7ec6\u80de\u6570\u636e\u7ec6\u80de\u7fa4\u6ce8\u91ca<\/li>\n<\/ol>\n

                # \u7ed9\u6bcf\u4e2acluster\u6807\u6ce8\u7ec6\u80de\u7c7b\u578b(\u4e00\u822c\u7ec6\u80de\u7fa4\u7684\u6ce8\u91ca\u901a\u8fc7\u4eba\u5de5\u67e5\u6587\u732e\u83b7\u5f97\u7684\u6ce8\u91ca\u7ed3\u679c\u6bd4\u8f83\u51c6\u786e)<\/p>\n

                new.cluster.ids <- c(“T”, “Mono”, “T”, “B”, “T”, “Mono”, “NK”, “DC”, “Platelet”)<\/p>\n

                names(new.cluster.ids) <- levels(pbmc)<\/p>\n

                pbmc <- RenameIdents(pbmc, new.cluster.ids)<\/p>\n

                # \u6dfb\u52a0\u7ec6\u80de\u7c7b\u578b\u81f3meta data<\/p>\n

                pbmc@meta.data$celltype <- pbmc@active.ident<\/p>\n

                \"Dingtalk_20230308144016\"<\/p>\n

                \u54c8\u54c8\uff0c\u8fd9\u5c31\u662f\u5355\u7ec6\u80de\u6570\u636e\u5206\u6790\u7684\u57fa\u672c\u6d41\u7a0b\uff0c\u4e00\u822c\u5355\u7ec6\u80de\u6570\u636e\u5206\u6790\u90fd\u9700\u8981\u5f88\u5927\u7684\u8ba1\u7b97\u8d44\u6e90\u5965\uff0c\u901a\u5e38\u90fd\u4f1a\u5229\u7528\u9ad8\u6027\u80fd\u670d\u52a1\u5668\u6765\u8fdb\u884c\u6570\u636e\u5206\u6790\uff0c\u5c0f\u679c\u63a8\u8350\u5927\u5bb6\u4f7f\u7528\u672c\u516c\u53f8\u7684\u670d\u52a1\u5668\uff0c\u5355\u7ec6\u80de\u5206\u6790\u6240\u9700\u8981\u7684\u8f6f\u4ef6\u548cR\u5305\u90fd\u5df2\u5b89\u88c5\u597d\uff0c\u6709\u9700\u8981\u7684\u53ef\u4ee5\u8054\u7cfb\u5c0f\u679c\uff0c\u672c\u671f\u7684\u5206\u4eab\u5c31\u5230\u8fd9\u91cc\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

                \u5355\u7ec6\u80de\u5206\u6790\u5728\u73b0\u5728\u975e\u5e38\u706b\uff0c\u4eca\u5929\u5c0f\u679c\u4e5f\u60f3\u5e26\u7740\u5927\u5bb6\u5f00\u59cb\u5b66\u4e60\u68b3\u7406\u4e00\u4e0b\u5355\u7ec6\u80de\u5206\u6790\u6d41\u7a0b\uff0c\u7b80\u5355\u719f\u6089\u4e00\u4e0b\u5355\u7ec6\u80de\u5206\u6790\u6240\u5305\u542b\u7684\u5185\u5bb9 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":""},"categories":[1],"tags":[],"jetpack_featured_media_url":"","_links":{"self":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/29419"}],"collection":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=29419"}],"version-history":[{"count":1,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/29419\/revisions"}],"predecessor-version":[{"id":29434,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/29419\/revisions\/29434"}],"wp:attachment":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=29419"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=29419"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=29419"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}