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14px;\">\u8fd9\u91cc\u5c0f\u679c\u4f7f\u7528\u7684\u662fseurat\u5206\u6790\u7684pbmc\u6570\u636e\uff0c\u539f\u6570\u636e\u662f\u4ece10X Genomics\u7f51\u7ad9\u4e0a\u514d\u8d39\u83b7\u5f97\u7684\u5916\u5468\u8840\u5355\u4e2a\u6838\u7ec6\u80de\uff08PBMC\uff09\u6570\u636e\u96c6\uff0c\u53ef\u4ee5\u5728\u6b64\u94fe\u63a5\u4e0b\u8f7dhttps:\/\/cf.10xgenomics.com\/samples\/cell\/pbmc3k\/pbmc3k_filtered_gene_bc_matrices.tar.gz\u3002\u7ecf\u8fc7\u6570\u636e\u9884\u5904\u7406\u3001\u964d\u7ef4\u805a\u7c7b\u540e\uff0c\u8fdb\u884c\u6ce8\u91ca\u3002<\/span><\/p>\n<p style=\"white-space: normal;\"><span style=\"font-size: 14px;\">\u539f\u6570\u636e\u7684\u5904\u7406\u4ee3\u7801\u5982\u4e0b\uff1a<\/span><\/p>\n<\/section>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"c\"><code><span class=\"code-snippet_outer\">library(dplyr)<\/span><\/code><code><span class=\"code-snippet_outer\">library(Seurat)<\/span><\/code><code><span class=\"code-snippet_outer\">library(patchwork)<\/span><\/code><code><span class=\"code-snippet_outer\">#\u6570\u636e\u5bfc\u5165<\/span><\/code><code><span class=\"code-snippet_outer\">dir=<span class=\"code-snippet__string\">\"..\/filtered_feature_bc_matrix\/hg19\/\"<\/span>#\u539f\u6570\u636e\u6240\u5728\u6587\u4ef6\u5939<\/span><\/code><code><span class=\"code-snippet_outer\">counts <- Read10X(data.dir = dir)<\/span><\/code><code><span class=\"code-snippet_outer\">scdata <- CreateSeuratObject(counts = counts)<\/span><\/code><code><span class=\"code-snippet_outer\">#\u8d28\u63a7<\/span><\/code><code><span class=\"code-snippet_outer\">scdata[[<span class=\"code-snippet__string\">\"percent.mt\"<\/span>]] <- PercentageFeatureSet(scdata, pattern = <span class=\"code-snippet__string\">\"^MT-\"<\/span>)# \u8ba1\u7b97\u7ebf\u7c92\u4f53\u57fa\u56e0\u8868\u8fbe\u7684\u6bd4\u4f8b<\/span><\/code><code><span class=\"code-snippet_outer\">VlnPlot(scdata, features = c(<span class=\"code-snippet__string\">\"nFeature_RNA\"<\/span>, <span class=\"code-snippet__string\">\"nCount_RNA\"<\/span>, <span class=\"code-snippet__string\">\"percent.mt\"<\/span>), ncol = <span class=\"code-snippet__number\">3<\/span>) #\u53ef\u89c6\u5316QC metrics as a violin plot<\/span><\/code><code><span class=\"code-snippet_outer\">plot1 <- FeatureScatter(scdata, feature1 = <span class=\"code-snippet__string\">\"nCount_RNA\"<\/span>, feature2 = <span class=\"code-snippet__string\">\"percent.mt\"<\/span>)<\/span><\/code><code><span class=\"code-snippet_outer\">plot2 <- FeatureScatter(scdata, feature1 = <span class=\"code-snippet__string\">\"nCount_RNA\"<\/span>, feature2 = <span class=\"code-snippet__string\">\"nFeature_RNA\"<\/span>)<\/span><\/code><code><span class=\"code-snippet_outer\">plot1 + plot2<\/span><\/code><code><span class=\"code-snippet_outer\">scdata <- subset(scdata, subset = nFeature_RNA > <span class=\"code-snippet__number\">200<\/span> & percent.mt < <span class=\"code-snippet__number\">10<\/span>)<\/span><\/code><code><span class=\"code-snippet_outer\">#\u8fc7\u6ee4\u7ec6\u80de\u3002\u6211\u4eec\u8fc7\u6ee4\u6389\u57fa\u56e0\u6570\uff08nFeature_RNA\uff09\u5c0f\u4e8e<span class=\"code-snippet__number\">200<\/span>\u7684\u7ec6\u80de\uff0c\u4ee5\u53ca\u7ebf\u7c92\u4f53\u57fa\u56e0\u7ec4\u6bd4\u4f8b\u5927\u4e8e<span class=\"code-snippet__number\">10<\/span>%\u7684\u7ec6\u80de\u3002<\/span><\/code><code><span class=\"code-snippet_outer\">scdata <- NormalizeData(scdata, normalization.method = <span class=\"code-snippet__string\">\"LogNormalize\"<\/span>, scale.factor = <span class=\"code-snippet__number\">10000<\/span>)<\/span><\/code><code><span class=\"code-snippet_outer\">scdata <- FindVariableFeatures(scdata, selection.method = <span class=\"code-snippet__string\">\"vst\"<\/span>, nfeatures = <span class=\"code-snippet__number\">2000<\/span>) #\u7279\u5f81\u9009\u62e9<\/span><\/code><code><span class=\"code-snippet_outer\">all.genes <- rownames(scdata) #\u6570\u636e\u7f29\u653e<\/span><\/code><code><span class=\"code-snippet_outer\">scdata <- ScaleData(scdata, features = all.genes)<\/span><\/code><code><span class=\"code-snippet_outer\">#\u964d\u7ef4<\/span><\/code><code><span class=\"code-snippet_outer\">scdata <- RunPCA(scdata,features = VariableFeatures(object = scdata))<\/span><\/code><code><span class=\"code-snippet_outer\">DimPlot(scdata, reduction = <span class=\"code-snippet__string\">\"pca\"<\/span>)<\/span><\/code><code><span class=\"code-snippet_outer\">#UMAP<\/span><\/code><code><span class=\"code-snippet_outer\">scdata <- RunUMAP(scdata, dims = <span class=\"code-snippet__number\">1<\/span>:<span class=\"code-snippet__number\">10<\/span>)<\/span><\/code><code><span class=\"code-snippet_outer\">DimPlot(scdata, reduction = <span class=\"code-snippet__string\">\"umap\"<\/span>,label = TRUE)<\/span><\/code><code><span class=\"code-snippet_outer\">#\u805a\u7c7b<\/span><\/code><code><span class=\"code-snippet_outer\">scdata <- FindNeighbors(scdata, dims = <span class=\"code-snippet__number\">1<\/span>:<span class=\"code-snippet__number\">10<\/span>)<\/span><\/code><code><span class=\"code-snippet_outer\">scdata <- FindClusters(scdata, resolution = <span class=\"code-snippet__number\">0.5<\/span>)<\/span><\/code><code><span class=\"code-snippet_outer\">\u5bf9\u6570\u636e\u5904\u7406\u540e\uff0c\u8fdb\u884cSingleR\u7684\u6ce8\u91ca\uff1a<\/span><\/code><code><span class=\"code-snippet_outer\">#SingleR\u6ce8\u91ca<\/span><\/code><code><span class=\"code-snippet_outer\">library(SingleR)<\/span><\/code><code><span class=\"code-snippet_outer\">#\u8f7d\u5165\u53c2\u8003\u6570\u636e\u96c6<\/span><\/code><code><span class=\"code-snippet_outer\">ref_use <- HumanPrimaryCellAtlasData()<\/span><\/code><code><span class=\"code-snippet_outer\">singler_res <- SingleR(test=as.matrix(scdata@assays$RNA@data), #\u8f93\u5165\u8868\u8fbe\u77e9\u9635<\/span><\/code><code><span class=\"code-snippet_outer\"> ref=ref_use, #\u53c2\u8003\u6570\u636e<\/span><\/code><code><span class=\"code-snippet_outer\"> labels=ref_use$label.fine #\u6807\u7b7e)<\/span><\/code><code><span class=\"code-snippet_outer\">table(singler_res$labels) #\u67e5\u770b\u6ce8\u91ca\u7ed3\u679c <\/span><\/code><code><span class=\"code-snippet_outer\">#\u4fdd\u5b58SingleR\u6ce8\u91ca\u7ed3\u679c<\/span><\/code><code><span class=\"code-snippet_outer\">write.table(singler_res,<span class=\"code-snippet__string\">\"..\/output\/Singler_out.xls\"<\/span>,sep=<span class=\"code-snippet__string\">'t'<\/span>,quote = F)<\/span><\/code><code><span class=\"code-snippet_outer\">celltype = data.frame(ClusterID=rownames(singler_res), celltype=singler_res$labels, stringsAsFactors = F)<\/span><\/code><code><span class=\"code-snippet_outer\">scdata@meta.data$singleR=celltype[match(rownames(scdata@meta.data),celltype$ClusterID),<span class=\"code-snippet__string\">'celltype'<\/span>]<\/span><\/code><code><span class=\"code-snippet_outer\">head(scdata@meta.data)<\/span><\/code><code><span class=\"code-snippet_outer\">DimPlot(scdata, reduction = <span class=\"code-snippet__string\">\"umap\"<\/span>, group.by = <span class=\"code-snippet__string\">\"singleR\"<\/span>,pt.size = <span class=\"code-snippet__number\">1.5<\/span>)<\/span><\/code><\/pre>\n<\/section>\n<section powered-by=\"xiumi.us\">\n<p style=\"white-space: normal;\"><br \/><\/p>\n<p style=\"white-space: normal;\"><span style=\"font-size: 14px;\">\u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0cSingleR\u5c06\u6b64\u6570\u636e\u6ce8\u91ca\u6210\u4e867\u5927\u7c7b\uff0c\u5206\u522b\u7528\u4e0d\u540c\u989c\u8272\u6807\u8bc6\u3002<\/span><\/p>\n<p style=\"white-space: normal;\"><span style=\"font-size: 14px;\"> <\/span><\/p>\n<p style=\"white-space: normal;\"><span style=\"font-size: 14px;\">\u6700\u540e\uff0c\u5c0f\u679c\u7ed9\u5927\u5bb6\u4ecb\u7ecd\u4e00\u4e2a\u5728\u7ebf\u5c0f\u5de5\u5177\uff1a\u57fa\u4e8e\u57fa\u56e0\u529f\u80fd\u6ce8\u91ca\u4fe1\u606f\u6316\u6398\u5173\u952e\u4f5c\u7528\u57fa\u56e0\uff08http:\/\/www.biocloudservice.com\/122\/122.php\uff09\uff0c\u7b80\u5355\u597d\u7528\uff0c\u5927\u5bb6\u8bb0\u5f97\u7528\u8d77\u6765\u54e6\u3002<\/span><span style=\"text-align: center;letter-spacing: 0.578px;\"><\/span><\/p>\n<\/section>\n<\/section>\n<section style=\"height: 0px;overflow: hidden;\"><span style=\"font-size: 14px;letter-spacing: 0.578px;caret-color: red;\"><\/span><\/section>\n<section>\n<section>\n<section>\n<section style=\"letter-spacing: 0.578px;\">\n<section style=\"letter-spacing: 0.578px;\">\n<section style=\"letter-spacing: 0.578px;\">\n<p style=\"text-align: center;\"><br \/><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<section data-tools=\"135\u7f16\u8f91\u5668\" data-id=\"105054\" style=\"margin-bottom: 0px;letter-spacing: 0.578px;font-size: 16px;\">\n<section style=\"margin: 10px auto;text-align: center;\">\n<section style=\"padding-top: 30px;padding-bottom: 30px;background-color: rgb(242, 246, 255);border-radius: 7px;\">\n<section style=\"display: flex;justify-content: space-evenly;align-items: center;\">\n<section data-width=\"26%\" style=\"width: 150.266px;\">\n<section style=\"padding: 7px;border-radius: 15px;box-shadow: rgba(169, 187, 223, 0.45) 4px 4px 8px, rgb(255, 255, 255) -2.5px -2.5px 5px;\">\n<section data-width=\"100%\" style=\"width: 136.266px;\"><img decoding=\"async\" class=\"rich_pages wxw-img\" data-ratio=\"1.005281690140845\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/2_0WFLKBMicbm6iawtkrNrMxpSVU9GBQ.png?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/mmbiz_png\/zcicibWZessAxec5nH6tybm0EXxCaibxbtN261llpmfF6btPBTUKkibEdnRz5Y0WFLKBMicbm6iawtkrNrMxpSVU9GBQ\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"568\" data-width=\"100%\" style=\"vertical-align: inherit;width: 136.266px;display: block;border-radius: 7px;\" title=\"\u5c0f\u4e91-\u5fae\u4fe1\u4e8c\u7ef4\u7801.png\" data-recalc-dims=\"1\" \/><\/section>\n<\/section>\n<\/section>\n<section data-width=\"62%\" style=\"width: 358.359px;\">\n<section style=\"display: inline-block;\">\n<section style=\"padding: 7px 15px;border-radius: 20px;box-shadow: rgba(169, 187, 223, 0.45) 6px 6px 8px inset, rgb(255, 255, 255) -2.5px -2.5px 5px inset;\">\n<section data-brushtype=\"text\" style=\"font-size: 14px;letter-spacing: 1.5px;color: rgb(111, 133, 172);\">\n<p style=\"text-indent: 0em;line-height: 1.75em;\"><span style=\"letter-spacing: 0.5px;\">\u5fae\u4fe1\u53f7 | 18502195490<\/span><\/p>\n<\/section>\n<\/section>\n<section style=\"margin-top: 12px;padding: 7px 15px;border-radius: 20px;box-shadow: rgba(169, 187, 223, 0.45) 6px 6px 8px inset, rgb(255, 255, 255) -2.5px -2.5px 5px inset;\">\n<section data-brushtype=\"text\" style=\"font-size: 14px;letter-spacing: 1.5px;color: rgb(111, 133, 172);\">\n<p style=\"text-indent: 0em;line-height: 1.75em;\"><span style=\"letter-spacing: 0.5px;\">\u77e5\u4e4e | \u751f\u4fe1\u679c<\/span><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<section style=\"margin-bottom: 0px;letter-spacing: 0.578px;caret-color: rgba(0, 0, 0, 0);font-size: 16px;\">\n<section>\n<section style=\"margin-top: 0.5em;\">\n<section style=\"padding: 0.5em;border-width: 1px;border-style: solid;box-shadow: rgb(226, 226, 226) 0px 16px 1px -13px;border-color: rgb(0, 150, 136);\">\n<section style=\"text-align: left;\">\n<section 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onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/mmbiz_png\/zcicibWZessAyB69UyXDLG0wQcISNwmL6DlpIKVBnCSOXHTWJhLcxic2qtibaib2l1ye6HNIkwTG696C2pf9yAtdeGA\/640?wx_fmt=png'\" data-type=\"jpeg\" data-ratio=\"1\" data-w=\"400\" style=\"height: 111px;vertical-align: middle;width: 111px;\" data-recalc-dims=\"1\" \/><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<section data-role=\"list\">\n<section style=\"font-size: 16px;\">\n<section style=\"margin: 10px 0% 8px;text-align: left;justify-content: flex-start;display: flex;flex-flow: row nowrap;\" powered-by=\"xiumi.us\">\n<section style=\"display: inline-block;width: 100%;vertical-align: top;border-left: 3px solid rgb(219, 219, 219);border-bottom-left-radius: 0px;padding-left: 8px;align-self: flex-start;flex: 0 0 auto;\">\n<section style=\"color: rgba(0, 0, 0, 0.5);text-align: justify;\" powered-by=\"xiumi.us\">\n<p style=\"white-space: normal;\">\u5c0f\u679c\u6765\u55bd\uff0c\u5927\u5bb6\u6700\u8fd1\u5728\u505a\u4ec0\u4e48\u751f\u4fe1\u5206\u6790\u5462\uff1f\u5c0f\u679c\u6700\u8fd1\u5728\u505a\u5355\u7ec6\u80de\u8f6c\u5f55\u7ec4\u5206\u6790\u7684\u7ec6\u80de\u6ce8\u91ca\u90e8\u5206\uff0c\u4eca\u5929\u5c31\u548c\u5927\u5bb6\u5206\u4eab\u4e00\u4e0b\u3002<\/p>\n<\/section>\n<\/section>\n<\/section>\n<p style=\"white-space: normal;\" powered-by=\"xiumi.us\"><br \/><\/p>\n<p style=\"white-space: normal;\" powered-by=\"xiumi.us\"><span style=\"font-size: 14px;\">\u5355\u7ec6\u80de\u8f6c\u5f55\u7ec4\u6ce8\u91ca\u4e00\u76f4\u662f\u5355\u7ec6\u80deRNA\u6d4b\u5e8f\u5206\u6790\u4e2d\u7684\u91cd\u8981\u6b65\u9aa4\u4e4b\u4e00\u3002\u7ec6\u80de\u6ce8\u91ca\u6709\u591a\u79cd\u65b9\u6cd5\uff0c\u53ef\u4ee5\u4eba<\/span><span style=\"font-size: 14px;letter-spacing: 0.578px;\">\u5de5\u6ce8\u91ca\uff0c\u4e5f\u53ef\u4ee5\u81ea\u52a8\u6ce8\u91ca\u3002SingleR\u7684\u51fa\u73b0\u4f7f\u5f97\u5355\u7ec6\u80de\u6570\u636e\u7684\u6ce8\u91ca\u53d8\u5f97\u66f4\u52a0\u5feb\u6377\u3001\u7cbe\u786e\u548c\u53ef\u9760\u3002\u540c\u65f6\uff0cSingleR\u4e5f\u4e3a\u540e\u7eed\u7684\u5355\u7ec6\u80de\u8f6c\u5f55\u7ec4\u5206\u6790\u63d0\u4f9b\u4e86\u66f4\u4e3a\u51c6\u786e\u548c\u5177\u4f53\u7684\u7ec6\u80de\u8eab\u4efd\u4fe1\u606f\u3002<\/span><\/p>\n<p style=\"white-space: normal;\" powered-by=\"xiumi.us\"><span style=\"font-size: 14px;letter-spacing: 0.578px;\"><br \/><\/span><\/p>\n<section powered-by=\"xiumi.us\">\n<p style=\"text-align: center;white-space: normal;\"><strong><em><span style=\"font-size: 20px;\">SingleR\u662f\u4ec0\u4e48\u5462\uff1f<\/span><\/em><\/strong><\/p>\n<p style=\"text-align: center;white-space: normal;\"><strong><em><span style=\"font-size: 20px;\"><br \/><\/span><\/em><\/strong><\/p>\n<p style=\"white-space: normal;\"><span style=\"font-size: 14px;\">SingleR\u662f\u4e00\u79cd\u7528\u4e8e\u5355\u7ec6\u80de\u8f6c\u5f55\u7ec4\u6ce8\u91ca\u7684\u81ea\u52a8\u5316\u65b9\u6cd5\uff0c\u662f\u57fa\u4e8eR\u4f7f\u7528\u7684\u4e00\u4e2a\u5de5\u5177\u3002\u5b83\u4f7f\u7528\u53c2\u8003\u6570\u636e\u96c6\u4e2d\u7684\u57fa\u56e0\u8868\u8fbe\u6a21\u5f0f\u6765\u9884\u6d4b\u6bcf\u4e2a\u5355\u7ec6\u80de\u5728\u4e0d\u540c\u7c7b\u578b\u548c\u72b6\u6001\u4e0b\u7684\u7ec6\u80de\u8eab\u4efd\uff0c\u4ece\u800c\u5b9e\u73b0\u5bf9\u5355\u7ec6\u80de\u6570\u636e\u8fdb\u884c\u6ce8\u91ca\u3002<\/span><\/p>\n<p style=\"white-space: normal;\"><span style=\"font-size: 14px;\"><br \/><\/span><\/p>\n<p style=\"white-space: normal;\"><span style=\"font-size: 14px;\">SingleR\u7684\u57fa\u672c\u601d\u8def\u662f\u5c06\u5f85\u6ce8\u91ca\u7684\u5355\u7ec6\u80de\u6570\u636e\u4e0e\u5927\u89c4\u6a21\u53c2\u8003\u6570\u636e\u5e93\u8fdb\u884c\u6bd4\u8f83\uff0c\u5206\u6790\u5176\u5171\u6027\u548c\u5dee\u5f02\u6027\uff0c\u4ece\u800c\u5f97\u5230\u6700\u4f18\u7684\u7ec6\u80de\u8eab\u4efd\u6ce8\u91ca\u3002SingleR\u6240\u4f7f\u7528\u7684\u53c2\u8003\u8d44\u6599\u5305\u62ec HPA\u3001ImmGen \u548c Blueprint \u7b49\u516c\u5f00\u7684 scRNA-seq \u6570\u636e\u5e93\u3002SingleR\u7684\u4f18\u70b9\u5728\u4e8e\u5feb\u901f\u3001\u7b80\u5355\u3001\u6709\u8f83\u9ad8\u7684\u51c6\u786e\u6027\u3002\u6b64\u5916\uff0c\u5b83\u4e0d\u9700\u8981\u7528\u6237\u5bf9\u6ce8\u91ca\u8fdb\u884c\u624b\u52a8\u5e72\u9884\uff0c\u907f\u514d\u4e86\u4eba\u4e3a\u56e0\u7d20\u5bf9\u7ed3\u679c\u7684\u5f71\u54cd\u3002<\/span><\/p>\n<p style=\"white-space: normal;\"><span style=\"font-size: 14px;\"><br \/><\/span><\/p>\n<p style=\"white-space: normal;\"><span style=\"font-size: 14px;\">\u8fd9\u91cc\u5c0f\u679c\u4f7f\u7528\u7684\u662fseurat\u5206\u6790\u7684pbmc\u6570\u636e\uff0c\u539f\u6570\u636e\u662f\u4ece10X Genomics\u7f51\u7ad9\u4e0a\u514d\u8d39\u83b7\u5f97\u7684\u5916\u5468\u8840\u5355\u4e2a\u6838\u7ec6\u80de\uff08PBMC\uff09\u6570\u636e\u96c6\uff0c\u53ef\u4ee5\u5728\u6b64\u94fe\u63a5\u4e0b\u8f7dhttps:\/\/cf.10xgenomics.com\/samples\/cell\/pbmc3k\/pbmc3k_filtered_gene_bc_matrices.tar.gz\u3002\u7ecf\u8fc7\u6570\u636e\u9884\u5904\u7406\u3001\u964d\u7ef4\u805a\u7c7b\u540e\uff0c\u8fdb\u884c\u6ce8\u91ca\u3002<\/span><\/p>\n<p style=\"white-space: normal;\"><span style=\"font-size: 14px;\">\u539f\u6570\u636e\u7684\u5904\u7406\u4ee3\u7801\u5982\u4e0b\uff1a<\/span><\/p>\n<\/section>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"c\"><code><span class=\"code-snippet_outer\">library(dplyr)<\/span><\/code><code><span class=\"code-snippet_outer\">library(Seurat)<\/span><\/code><code><span class=\"code-snippet_outer\">library(patchwork)<\/span><\/code><code><span class=\"code-snippet_outer\">#\u6570\u636e\u5bfc\u5165<\/span><\/code><code><span class=\"code-snippet_outer\">dir=<span class=\"code-snippet__string\">\"..\/filtered_feature_bc_matrix\/hg19\/\"<\/span>#\u539f\u6570\u636e\u6240\u5728\u6587\u4ef6\u5939<\/span><\/code><code><span class=\"code-snippet_outer\">counts <- Read10X(data.dir = dir)<\/span><\/code><code><span class=\"code-snippet_outer\">scdata <- CreateSeuratObject(counts = counts)<\/span><\/code><code><span class=\"code-snippet_outer\">#\u8d28\u63a7<\/span><\/code><code><span class=\"code-snippet_outer\">scdata[[<span class=\"code-snippet__string\">\"percent.mt\"<\/span>]] <- PercentageFeatureSet(scdata, pattern = <span class=\"code-snippet__string\">\"^MT-\"<\/span>)# \u8ba1\u7b97\u7ebf\u7c92\u4f53\u57fa\u56e0\u8868\u8fbe\u7684\u6bd4\u4f8b<\/span><\/code><code><span class=\"code-snippet_outer\">VlnPlot(scdata, features = c(<span class=\"code-snippet__string\">\"nFeature_RNA\"<\/span>, <span class=\"code-snippet__string\">\"nCount_RNA\"<\/span>, <span class=\"code-snippet__string\">\"percent.mt\"<\/span>), ncol = <span class=\"code-snippet__number\">3<\/span>) #\u53ef\u89c6\u5316QC metrics as a violin plot<\/span><\/code><code><span class=\"code-snippet_outer\">plot1 <- FeatureScatter(scdata, feature1 = <span class=\"code-snippet__string\">\"nCount_RNA\"<\/span>, feature2 = <span class=\"code-snippet__string\">\"percent.mt\"<\/span>)<\/span><\/code><code><span class=\"code-snippet_outer\">plot2 <- FeatureScatter(scdata, feature1 = <span class=\"code-snippet__string\">\"nCount_RNA\"<\/span>, feature2 = <span class=\"code-snippet__string\">\"nFeature_RNA\"<\/span>)<\/span><\/code><code><span class=\"code-snippet_outer\">plot1 + plot2<\/span><\/code><code><span class=\"code-snippet_outer\">scdata <- subset(scdata, subset = nFeature_RNA > <span class=\"code-snippet__number\">200<\/span> & percent.mt < <span class=\"code-snippet__number\">10<\/span>)<\/span><\/code><code><span class=\"code-snippet_outer\">#\u8fc7\u6ee4\u7ec6\u80de\u3002\u6211\u4eec\u8fc7\u6ee4\u6389\u57fa\u56e0\u6570\uff08nFeature_RNA\uff09\u5c0f\u4e8e<span class=\"code-snippet__number\">200<\/span>\u7684\u7ec6\u80de\uff0c\u4ee5\u53ca\u7ebf\u7c92\u4f53\u57fa\u56e0\u7ec4\u6bd4\u4f8b\u5927\u4e8e<span class=\"code-snippet__number\">10<\/span>%\u7684\u7ec6\u80de\u3002<\/span><\/code><code><span class=\"code-snippet_outer\">scdata <- NormalizeData(scdata, normalization.method = <span class=\"code-snippet__string\">\"LogNormalize\"<\/span>, scale.factor = <span class=\"code-snippet__number\">10000<\/span>)<\/span><\/code><code><span class=\"code-snippet_outer\">scdata <- FindVariableFeatures(scdata, selection.method = <span class=\"code-snippet__string\">\"vst\"<\/span>, nfeatures = <span class=\"code-snippet__number\">2000<\/span>) #\u7279\u5f81\u9009\u62e9<\/span><\/code><code><span class=\"code-snippet_outer\">all.genes <- rownames(scdata) #\u6570\u636e\u7f29\u653e<\/span><\/code><code><span class=\"code-snippet_outer\">scdata <- ScaleData(scdata, features = all.genes)<\/span><\/code><code><span class=\"code-snippet_outer\">#\u964d\u7ef4<\/span><\/code><code><span class=\"code-snippet_outer\">scdata <- RunPCA(scdata,features = VariableFeatures(object = scdata))<\/span><\/code><code><span class=\"code-snippet_outer\">DimPlot(scdata, reduction = <span class=\"code-snippet__string\">\"pca\"<\/span>)<\/span><\/code><code><span class=\"code-snippet_outer\">#UMAP<\/span><\/code><code><span class=\"code-snippet_outer\">scdata <- RunUMAP(scdata, dims = <span class=\"code-snippet__number\">1<\/span>:<span class=\"code-snippet__number\">10<\/span>)<\/span><\/code><code><span class=\"code-snippet_outer\">DimPlot(scdata, reduction = <span class=\"code-snippet__string\">\"umap\"<\/span>,label = TRUE)<\/span><\/code><code><span class=\"code-snippet_outer\">#\u805a\u7c7b<\/span><\/code><code><span class=\"code-snippet_outer\">scdata <- FindNeighbors(scdata, dims = <span class=\"code-snippet__number\">1<\/span>:<span class=\"code-snippet__number\">10<\/span>)<\/span><\/code><code><span class=\"code-snippet_outer\">scdata <- FindClusters(scdata, resolution = <span class=\"code-snippet__number\">0.5<\/span>)<\/span><\/code><code><span class=\"code-snippet_outer\">\u5bf9\u6570\u636e\u5904\u7406\u540e\uff0c\u8fdb\u884cSingleR\u7684\u6ce8\u91ca\uff1a<\/span><\/code><code><span class=\"code-snippet_outer\">#SingleR\u6ce8\u91ca<\/span><\/code><code><span class=\"code-snippet_outer\">library(SingleR)<\/span><\/code><code><span class=\"code-snippet_outer\">#\u8f7d\u5165\u53c2\u8003\u6570\u636e\u96c6<\/span><\/code><code><span class=\"code-snippet_outer\">ref_use <- HumanPrimaryCellAtlasData()<\/span><\/code><code><span class=\"code-snippet_outer\">singler_res <- SingleR(test=as.matrix(scdata@assays$RNA@data), #\u8f93\u5165\u8868\u8fbe\u77e9\u9635<\/span><\/code><code><span class=\"code-snippet_outer\"> ref=ref_use, #\u53c2\u8003\u6570\u636e<\/span><\/code><code><span class=\"code-snippet_outer\"> labels=ref_use$label.fine #\u6807\u7b7e)<\/span><\/code><code><span class=\"code-snippet_outer\">table(singler_res$labels) #\u67e5\u770b\u6ce8\u91ca\u7ed3\u679c <\/span><\/code><code><span class=\"code-snippet_outer\">#\u4fdd\u5b58SingleR\u6ce8\u91ca\u7ed3\u679c<\/span><\/code><code><span class=\"code-snippet_outer\">write.table(singler_res,<span class=\"code-snippet__string\">\"..\/output\/Singler_out.xls\"<\/span>,sep=<span class=\"code-snippet__string\">'t'<\/span>,quote = F)<\/span><\/code><code><span class=\"code-snippet_outer\">celltype = data.frame(ClusterID=rownames(singler_res), celltype=singler_res$labels, stringsAsFactors = F)<\/span><\/code><code><span class=\"code-snippet_outer\">scdata@meta.data$singleR=celltype[match(rownames(scdata@meta.data),celltype$ClusterID),<span class=\"code-snippet__string\">'celltype'<\/span>]<\/span><\/code><code><span class=\"code-snippet_outer\">head(scdata@meta.data)<\/span><\/code><code><span class=\"code-snippet_outer\">DimPlot(scdata, reduction = <span class=\"code-snippet__string\">\"umap\"<\/span>, group.by = <span class=\"code-snippet__string\">\"singleR\"<\/span>,pt.size = <span class=\"code-snippet__number\">1.5<\/span>)<\/span><\/code><\/pre>\n<\/section>\n<section powered-by=\"xiumi.us\">\n<p style=\"white-space: normal;\"><br \/><\/p>\n<p style=\"white-space: normal;\"><span style=\"font-size: 14px;\">\u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0cSingleR\u5c06\u6b64\u6570\u636e\u6ce8\u91ca\u6210\u4e867\u5927\u7c7b\uff0c\u5206\u522b\u7528\u4e0d\u540c\u989c\u8272\u6807\u8bc6\u3002<\/span><\/p>\n<p style=\"white-space: normal;\"><span style=\"font-size: 14px;\"> <\/span><\/p>\n<p style=\"white-space: normal;\"><span style=\"font-size: 14px;\">\u6700\u540e\uff0c\u5c0f\u679c\u7ed9\u5927\u5bb6\u4ecb\u7ecd\u4e00\u4e2a\u5728\u7ebf\u5c0f\u5de5\u5177\uff1a\u57fa\u4e8e\u57fa\u56e0\u529f\u80fd\u6ce8\u91ca\u4fe1\u606f\u6316\u6398\u5173\u952e\u4f5c\u7528\u57fa\u56e0\uff08http:\/\/www.biocloudservice.com\/122\/122.php\uff09\uff0c\u7b80\u5355\u597d\u7528\uff0c\u5927\u5bb6\u8bb0\u5f97\u7528\u8d77\u6765\u54e6\u3002<\/span><span style=\"text-align: center;letter-spacing: 0.578px;\"><\/span><\/p>\n<\/section>\n<\/section>\n<section style=\"height: 0px;overflow: hidden;\"><span style=\"font-size: 14px;letter-spacing: 0.578px;caret-color: red;\"><\/span><\/section>\n<section>\n<section>\n<section>\n<section style=\"letter-spacing: 0.578px;\">\n<section style=\"letter-spacing: 0.578px;\">\n<section style=\"letter-spacing: 0.578px;\">\n<p style=\"text-align: center;\"><br 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src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/2_0WFLKBMicbm6iawtkrNrMxpSVU9GBQ.png?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/mmbiz_png\/zcicibWZessAxec5nH6tybm0EXxCaibxbtN261llpmfF6btPBTUKkibEdnRz5Y0WFLKBMicbm6iawtkrNrMxpSVU9GBQ\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"568\" data-width=\"100%\" style=\"vertical-align: inherit;width: 136.266px;display: block;border-radius: 7px;\" title=\"\u5c0f\u4e91-\u5fae\u4fe1\u4e8c\u7ef4\u7801.png\" data-recalc-dims=\"1\" \/><\/section>\n<\/section>\n<\/section>\n<section data-width=\"62%\" style=\"width: 358.359px;\">\n<section style=\"display: inline-block;\">\n<section style=\"padding: 7px 15px;border-radius: 20px;box-shadow: rgba(169, 187, 223, 0.45) 6px 6px 8px inset, rgb(255, 255, 255) -2.5px -2.5px 5px inset;\">\n<section data-brushtype=\"text\" style=\"font-size: 14px;letter-spacing: 1.5px;color: rgb(111, 133, 172);\">\n<p style=\"text-indent: 0em;line-height: 1.75em;\"><span style=\"letter-spacing: 0.5px;\">\u5fae\u4fe1\u53f7 | 18502195490<\/span><\/p>\n<\/section>\n<\/section>\n<section style=\"margin-top: 12px;padding: 7px 15px;border-radius: 20px;box-shadow: rgba(169, 187, 223, 0.45) 6px 6px 8px inset, rgb(255, 255, 255) -2.5px -2.5px 5px inset;\">\n<section data-brushtype=\"text\" style=\"font-size: 14px;letter-spacing: 1.5px;color: rgb(111, 133, 172);\">\n<p style=\"text-indent: 0em;line-height: 1.75em;\"><span style=\"letter-spacing: 0.5px;\">\u77e5\u4e4e | \u751f\u4fe1\u679c<\/span><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<section style=\"margin-bottom: 0px;letter-spacing: 0.578px;caret-color: rgba(0, 0, 0, 0);font-size: 16px;\">\n<section>\n<section style=\"margin-top: 0.5em;\">\n<section style=\"padding: 0.5em;border-width: 1px;border-style: solid;box-shadow: rgb(226, 226, 226) 0px 16px 1px -13px;border-color: rgb(0, 150, 136);\">\n<section style=\"text-align: left;\">\n<section 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