{"id":67119,"date":"2026-11-14T19:48:22","date_gmt":"2026-11-14T11:48:22","guid":{"rendered":"https:\/\/www.biocloudservice.com\/wordpress\/?p=67119"},"modified":"2026-11-15T00:06:56","modified_gmt":"2026-11-14T16:06:56","slug":"seurat-sketch-pbmc-project-cell-proportions","status":"publish","type":"post","link":"https:\/\/www.biocloudservice.com\/wordpress\/seurat-sketch-pbmc-project-cell-proportions\/","title":{"rendered":"Seurat \u62bd\u6837\u805a\u7c7b\u540e\uff0c\u600e\u6837\u56de\u5230\u5168\u91cf\u7ec6\u80de\u8ba1\u7b97\u5404\u7fa4\u5360\u6bd4\uff1f"},"content":{"rendered":"
\n

\u5728\u540c\u4e00\u4efd\u5916\u5468\u8840\u5355\u7ec6\u80de\u6570\u636e\u91cc\uff0c\u4e00\u4e2a\u7ec6\u80de\u7fa4\u5728\u62bd\u6837\u56fe\u4e0a\u5360 11.75%\uff0c\u56de\u5230\u5168\u91cf\u6570\u636e\u540e\u5360 7.77%\u3002\u4e24\u6b21\u8ba1\u7b97\u7528\u7684\u662f\u540c\u4e00\u6279\u53d7\u8bd5\u6750\u6599\uff0c\u4e5f\u6cbf\u7528\u540c\u4e00\u5957\u805a\u7c7b\u6807\u7b7e\u3002\u5dee\u522b\u4ece\u54ea\u513f\u6765\uff1f\u524d\u4e00\u4e2a\u6bd4\u4f8b\u7684\u5206\u6bcd\u662f\u5165\u9009\u5efa\u56fe\u7684 800 \u4e2a\u7ec6\u80de\uff0c\u540e\u4e00\u4e2a\u662f\u8d28\u63a7\u540e\u53ef\u4ee5\u53c2\u4e0e\u5206\u6790\u7684 2,638 \u4e2a\u7ec6\u80de\u3002\u505a\u56fe\u3001\u7ed9\u7ec6\u80de\u8d34\u6807\u7b7e\u3001\u7edf\u8ba1\u7ec6\u80de\u7ec4\u6210\uff0c\u4e09\u6b65\u5404\u81ea\u7528\u4e86\u54ea\u4e9b\u7ec6\u80de\uff0c\u51b3\u5b9a\u4e86\u6700\u540e\u4e00\u5217\u767e\u5206\u6570\u80fd\u591f\u56de\u7b54\u4ec0\u4e48\u3002<\/p>\n

\u4e0b\u9762\u7528 10x Genomics \u7684\u516c\u5f00 PBMC 3k \u6570\u636e\u4ece\u5934\u505a\u4e00\u904d\u3002\u5b83\u8db3\u591f\u5c0f\uff0c\u53ef\u4ee5\u540c\u65f6\u67e5\u770b\u62bd\u6837\u548c\u5168\u91cf\u7ed3\u679c\u3002\u5148\u5728\u4ee3\u8868\u6027\u7ec6\u80de\u4e0a\u53d1\u73b0\u7fa4\u4f53\uff0c\u518d\u628a\u6807\u7b7e\u8f6c\u56de\u539f\u59cb\u7ec6\u80de\uff0c\u6700\u540e\u7528\u6807\u5fd7\u57fa\u56e0\u6838\u5bf9\u3002\u5b66\u4f1a\u8fd9\u5957\u505a\u6cd5\u540e\uff0c\u540c\u6837\u7684\u68c0\u67e5\u4e5f\u9002\u7528\u4e8e\u66f4\u5927\u7684\u56fe\u8c31\u3002Seurat \u7684 PBMC \u6570\u636e\u6559\u7a0b<\/a><\/p>\n

\u7528\u4e00\u4e2a\u5c0f\u6570\u636e\u96c6\u628a\u4e24\u4efd\u7ec6\u80de\u540d\u5355\u5206\u6e05<\/h2>\n

\u4e0b\u8f7d\u8fc7\u6ee4\u540e\u7684\u57fa\u56e0\u4e0e\u6761\u7801\u77e9\u9635\uff0c\u89e3\u538b\u540e\u5e94\u5f97\u5230\u5305\u542b matrix.mtx<\/code>\u3001genes.tsv<\/code> \u548c barcodes.tsv<\/code> \u7684 hg19<\/code> \u5b50\u76ee\u5f55\u3002\u8fd9\u4efd\u77e9\u9635\u6709 2,700 \u5217\uff0c\u6bcf\u4e00\u5217\u5bf9\u5e94\u4e00\u4e2a\u6761\u7801\u3002\u4ee5\u4e0b\u4e09\u6bb5\u4ee3\u7801\u5728\u540c\u4e00\u4e2a R \u4f1a\u8bdd\u987a\u5e8f\u8fd0\u884c\u3002\u4e0b\u6587\u56fe\u8868\u8bb0\u5f55\u9996\u6b21\u590d\u7b97\uff0c\u4f7f\u7528 R 4.5.2 \u548c Seurat 5.4.0\u3002\u4e0b\u8f7d\u533a\u53e6\u5217 Linux \u4ece\u5934\u8fd0\u884c\u6b64\u811a\u672c\u7684\u7ed3\u679c\u3002\u4e0b\u8f7d\u672c\u6587\u6240\u7528\u7684 10x \u539f\u59cb\u8ba1\u6570\u77e9\u9635<\/a><\/p>\n

library(Seurat)\nset.seed(41)\ncounts <- Read10X(\"filtered_gene_bc_matrices\/hg19\")\nobj <- CreateSeuratObject(counts = counts, min.features = 200)\nobj[[\"percent.mt\"]] <- PercentageFeatureSet(obj, pattern = \"^MT-\")\nobj <- subset(obj, subset = nFeature_RNA < 2500 & percent.mt < 5)\nobj <- NormalizeData(obj, verbose = FALSE)\nobj <- FindVariableFeatures(obj, nfeatures = 1500, verbose = FALSE)\nc(input = ncol(counts), after_qc = ncol(obj))\n<\/code><\/pre>\n
\u672c\u4f8b\u4fdd\u7559\u4e86 2,638 \u4e2a\u7ec6\u80de\u3002\u8fd9\u91cc\u7684\u57fa\u56e0\u6570\u548c\u7ebf\u7c92\u4f53\u6bd4\u4f8b\u9608\u503c\u662f\u4e3a\u4e86\u590d\u73b0\u8fd9\u4efd\u4eba\u5916\u5468\u8840\u793a\u4f8b\u3002\u6362\u7528\u81ea\u5df1\u7684\u7ec4\u7ec7\u65f6\uff0c\u5148\u6309\u6587\u5e93\u67e5\u770b\u68c0\u6d4b\u57fa\u56e0\u6570\u548c\u7ebf\u7c92\u4f53\u6bd4\u4f8b\u7684\u5206\u5e03\uff0c\u518d\u51b3\u5b9a\u9608\u503c\u3002\u4f8b\u5982\u5728\u4e00\u4e2a\u7ec6\u80de\u672c\u6765\u5c31\u5bcc\u542b\u7ebf\u7c92\u4f53\u8f6c\u5f55\u672c\u7684\u7ec4\u7ec7\u91cc\uff0c\u76f4\u63a5\u7167\u642c\u5916\u5468\u8840\u9608\u503c\uff0c\u53ef\u80fd\u5728\u62bd\u6837\u4e4b\u524d\u5c31\u5220\u6389\u4e86\u611f\u5174\u8da3\u7684\u7fa4\u3002\u4f7f\u7528\u57fa\u56e0\u7f16\u53f7\u6216\u5c0f\u9f20\u57fa\u56e0\u7b26\u53f7\u65f6\uff0c\u8fd8\u8981\u68c0\u67e5\u7ebf\u7c92\u4f53\u57fa\u56e0\u7684\u5339\u914d\u6a21\u5f0f\u662f\u5426\u771f\u7684\u547d\u4e2d\u4e86\u57fa\u56e0\u3002<\/p>\n
\u5728\u62bd\u6837\u7ec6\u80de\u4e0a\u5b66\u6807\u7b7e\uff0c\u8ba9\u6a21\u578b\u80fd\u6295\u56de\u5168\u91cf<\/h2>\n
SketchData()<\/code> \u7684 LeverageScore<\/code> \u65b9\u6cd5\u503e\u5411\u4e8e\u9009\u5165\u80fd\u4ee3\u8868\u4e0d\u540c\u8868\u8fbe\u7ed3\u6784\u7684\u7ec6\u80de\u3002\u4e00\u4e9b\u6570\u91cf\u5c11\u3001\u8868\u8fbe\u7279\u5f81\u9c9c\u660e\u7684\u7ec6\u80de\u56e0\u6b64\u6709\u673a\u4f1a\u5728\u56fe\u4e0a\u88ab\u770b\u89c1\u3002\u8fd9\u4e2a\u7279\u70b9\u6709\u52a9\u4e8e\u53d1\u73b0\u5c0f\u7fa4\u4f53\uff0c\u4e5f\u610f\u5473\u7740\u4e0d\u80fd\u5047\u5b9a\u6bcf\u79cd\u7ec6\u80de\u8fdb\u5165\u62bd\u6837\u96c6\u5408\u7684\u6982\u7387\u76f8\u540c\u3002\u6570\u636e\u91cc\u6ca1\u6709\u63d0\u4f9b\u7ed9\u6d41\u7a0b\u7684\u7ec6\u80de\u7c7b\u578b\u6807\u7b7e\uff0c\u62bd\u6837\u4e5f\u4e0d\u4f1a\u66ff\u4f60\u5224\u65ad\u67d0\u4e00\u7fa4\u7a76\u7adf\u662f\u4ec0\u4e48\u7ec6\u80de\u3002\u4f5c\u8005\u5bf9 sketch \u62bd\u6837\u4e0e\u6295\u5f71\u7684\u8bf4\u660e<\/a><\/p>\n
obj <- SketchData(obj, ncells = 800, method = \"LeverageScore\",\n                  sketched.assay = \"sketch\", seed = 41)\nDefaultAssay(obj) <- \"sketch\"\nobj <- FindVariableFeatures(obj, nfeatures = 1500, verbose = FALSE)\nobj <- ScaleData(obj, verbose = FALSE)\nobj <- RunPCA(obj, npcs = 20, verbose = FALSE)\nobj <- FindNeighbors(obj, dims = 1:15, verbose = FALSE)\nobj <- FindClusters(obj, resolution = 0.6, random.seed = 41, verbose = FALSE)\nobj <- RunUMAP(obj, dims = 1:15, return.model = TRUE,\n               seed.use = 41, verbose = FALSE)\n\nobj <- ProjectData(obj, assay = \"RNA\", sketched.assay = \"sketch\",\n                   sketched.reduction = \"pca\", full.reduction = \"pca.full\",\n                   umap.model = \"umap\", dims = 1:15,\n                   refdata = list(cluster_full = \"seurat_clusters\"))\nDefaultAssay(obj) <- \"RNA\"\n<\/code><\/pre>\n
\u8fd9\u91cc\u6709\u4e24\u5904\u503c\u5f97\u4e13\u95e8\u6838\u5bf9\u3002\u7b2c\u4e00\uff0c\u805a\u7c7b\u65f6\u9ed8\u8ba4\u4f7f\u7528 sketch<\/code>\uff0c\u6240\u4ee5\u5f97\u5230\u7684\u662f\u62bd\u6837\u7ec6\u80de\u4e0a\u7684\u805a\u7c7b\u7ed3\u679c\u3002\u7b2c\u4e8c\uff0c\u62df\u5408 UMAP \u65f6\u8981\u4fdd\u5b58\u6a21\u578b\uff0c\u540e\u9762\u624d\u80fd\u628a\u5176\u4f59\u7ec6\u80de\u653e\u5230\u540c\u4e00\u4e2a\u5750\u6807\u7cfb\u91cc\u3002refdata<\/code> \u6307\u660e\u5c06\u62bd\u6837\u7fa4\u6807\u7b7e\u8f6c\u79fb\u5230\u5168\u91cf\u5143\u6570\u636e\u7684\u65b0\u5217 cluster_full<\/code>\u3002\u4e00\u5f20\u753b\u5f97\u5f88\u50cf\u539f\u56fe\u7684 UMAP \u53ea\u80fd\u8bf4\u660e\u5750\u6807\u53ef\u89c6\u5316\u6210\u529f\uff0c\u7edf\u8ba1\u524d\u8fd8\u8981\u68c0\u67e5\u8fd9\u5217\u662f\u5426\u8986\u76d6\u4e86\u6240\u6709\u5f85\u5206\u6790\u7ec6\u80de\u3002ProjectData \u51fd\u6570\u8bf4\u660e<\/a><\/p>\n
\u7528\u6761\u7801\u5bf9\u9f50\u540e\u518d\u505a\u4e24\u5f20\u9891\u6570\u8868<\/h2>\n
\u540c\u4e00\u4e2a Seurat \u5bf9\u8c61\u53ef\u4ee5\u5305\u542b\u4e0d\u540c\u7ec6\u80de\u96c6\u5408\u7684 assay\u3002\u5199 ncol(obj)<\/code> \u65f6\u5f88\u5bb9\u6613\u5fd8\u8bb0\u5f53\u524d\u770b\u7684\u662f\u54ea\u4e00\u5c42\uff0c\u76f4\u63a5\u4ece\u4e24\u5f20\u8868\u8fbe\u77e9\u9635\u53d6\u5217\u540d\u66f4\u6e05\u695a\u3002\u4e0b\u9762\u628a\u4e24\u5957\u6761\u7801\u5206\u522b\u7528\u4e8e\u7d22\u5f15\u540c\u4e00\u5f20\u5143\u6570\u636e\u8868\uff0c\u4e5f\u8981\u6c42\u6bcf\u4e2a\u6761\u7801\u90fd\u6709\u5bf9\u5e94\u6807\u7b7e\u3002\u5982\u679c\u68c0\u67e5\u62a5\u9519\uff0c\u5148\u627e\u51fa\u7f3a\u5931\u7684\u6761\u7801\u548c\u6807\u7b7e\u6765\u6e90\uff0c\u907f\u514d\u8ba9 table()<\/code> \u9ed8\u8ba4\u5ffd\u7565\u7f3a\u5931\u503c\u540e\u7ed9\u51fa\u770b\u4f3c\u5b8c\u6574\u7684\u767e\u5206\u6570\u3002<\/p>\n
meta <- obj[[]]\nfull_id <- colnames(obj[[\"RNA\"]])\nsketch_id <- colnames(obj[[\"sketch\"]])\nfull_lab <- as.character(meta[full_id, \"cluster_full\"])\nfit_lab <- as.character(meta[sketch_id, \"seurat_clusters\"])\nstopifnot(!anyNA(full_lab), !anyNA(fit_lab))\n\nlev <- sort(unique(c(full_lab, fit_lab)))\nn_fit <- table(factor(fit_lab, levels = lev))\nn_full <- table(factor(full_lab, levels = lev))\ndata.frame(cluster = lev, sketch_n = as.integer(n_fit),\n           full_n = as.integer(n_full),\n           sketch_pct = round(100 * as.numeric(n_fit) \/ length(fit_lab), 2),\n           full_pct = round(100 * as.numeric(n_full) \/ length(full_lab), 2))\n<\/code><\/pre>\n
\n\n\n\n\n\n\n\n\n\n\n
\u672c\u6b21\u805a\u7c7b\u7f16\u53f7<\/th>\n\u62bd\u6837\u5efa\u56fe\u7ec6\u80de\u6570 \/ 800<\/th>\n\u5168\u91cf\u6295\u5f71\u7ec6\u80de\u6570 \/ 2,638<\/th>\n\u62bd\u6837\u5360\u6bd4 \u2192 \u5168\u91cf\u5360\u6bd4<\/th>\n<\/tr>\n<\/thead>\n
0<\/td>\n319<\/td>\n1,267<\/td>\n39.88% \u2192 48.03%<\/td>\n<\/tr>\n
1<\/td>\n181<\/td>\n584<\/td>\n22.62% \u2192 22.14%<\/td>\n<\/tr>\n
2<\/td>\n110<\/td>\n346<\/td>\n13.75% \u2192 13.12%<\/td>\n<\/tr>\n
3<\/td>\n94<\/td>\n205<\/td>\n11.75% \u2192 7.77%<\/td>\n<\/tr>\n
4<\/td>\n55<\/td>\n136<\/td>\n6.88% \u2192 5.16%<\/td>\n<\/tr>\n
5<\/td>\n41<\/td>\n100<\/td>\n5.12% \u2192 3.79%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
\"PBMC
\u540c\u4e00\u4efd PBMC \u6570\u636e\u7684\u4e24\u5957\u5206\u6bcd\u3002\u6761\u5f62\u4f9d\u6b21\u8868\u793a\u62bd\u6837\u5efa\u56fe\u548c\u5168\u91cf\u6295\u5f71\u540e\u7684\u7fa4\u5185\u8ba1\u6570\u5360\u5404\u81ea\u603b\u6570\u7684\u6bd4\u4f8b\u3002<\/figcaption><\/figure>\n
\u770b\u7fa4 3\uff0c\u5982\u679c\u628a\u62bd\u6837\u6bd4\u4f8b\u76f4\u63a5\u5199\u8fdb\u7ed3\u679c\uff0c\u4f1a\u591a\u51fa\u7ea6\u56db\u4e2a\u767e\u5206\u70b9\u3002\u770b\u7fa4 0\uff0c\u53d8\u5316\u65b9\u5411\u5374\u76f8\u53cd\u3002\u8868\u7684\u5de6\u4fa7\u53d6\u5efa\u56fe\u6807\u7b7e\uff0c\u53f3\u4fa7\u53d6\u6295\u5f71\u6807\u7b7e\uff0c\u56e0\u6b64\u4e24\u5217\u5dee\u5f02\u8fd8\u53ef\u80fd\u5305\u542b\u8fb9\u754c\u7ec6\u80de\u88ab\u91cd\u65b0\u8d4b\u503c\u7684\u5f71\u54cd\u3002\u60f3\u62c6\u5f00\u8fd9\u4e24\u4e2a\u56e0\u7d20\uff0c\u53ef\u4ee5\u53ea\u5bf9\u5165\u9009\u7684 800 \u4e2a\u6761\u7801\u6bd4\u8f83 table(meta[sketch_id, \"seurat_clusters\"], meta[sketch_id, \"cluster_full\"])<\/code>\u3002\u96c6\u4e2d\u5728\u5bf9\u89d2\u7ebf\u5916\u7684\u6807\u7b7e\u5bf9\u503c\u5f97\u4f18\u5148\u56de\u770b\u3002\u540c\u4e00\u6b21\u5206\u6790\u91cc\u7684\u6570\u5b57\u7f16\u53f7\u53ea\u662f\u7d22\u5f15\uff0c\u6362\u968f\u673a\u79cd\u5b50\u6216\u805a\u7c7b\u53c2\u6570\u540e\uff0c\u8981\u6309\u6807\u5fd7\u57fa\u56e0\u91cd\u65b0\u8bc6\u522b\u5bf9\u5e94\u7fa4\u3002<\/p>\n
\u4ec0\u4e48\u65f6\u5019\u53ef\u4ee5\u628a\u201c\u7fa4 2\u201d\u5199\u6210\u4e00\u79cd\u7ec6\u80de\uff1f<\/h2>\n
\u672c\u4f8b\u5728\u5168\u91cf RNA \u7684\u5f52\u4e00\u5316\u8868\u8fbe\u4e2d\u6309\u6295\u5f71\u6807\u7b7e\u53d6\u5e73\u5747\uff0cMS4A1<\/code> \u548c CD79A<\/code> \u5747\u5728\u7fa4 2 \u6700\u9ad8\u3002\u4e24\u4e2a\u6807\u5fd7\u7269\u7ed9\u51fa\u4e86\u628a\u8be5\u7fa4\u4f5c\u4e3a B \u7ec6\u80de\u5019\u9009\u7684\u4f9d\u636e\u3002\u53ef\u4ee5\u518d\u8fd0\u884c DotPlot(obj, features = c(\"MS4A1\", \"CD79A\", \"CD3D\", \"NKG7\"), group.by = \"cluster_full\")<\/code>\uff0c\u540c\u65f6\u67e5\u770b\u8868\u8fbe\u7ec6\u80de\u6bd4\u4f8b\u4e0e\u7fa4\u5185\u5e73\u5747\u8868\u8fbe\u3002\u5982\u679c\u53ea\u6709\u6781\u5c11\u6570\u7ec6\u80de\u9ad8\u8868\u8fbe\u4e00\u4e2a\u6807\u5fd7\u7269\uff0c\u6216\u8005\u4e24\u4e2a\u901a\u5e38\u4e92\u65a5\u7684\u8c31\u7cfb\u6807\u5fd7\u5728\u540c\u4e00\u6279\u7ec6\u80de\u91cc\u540c\u65f6\u5f88\u5f3a\uff0c\u5c31\u8981\u56de\u770b\u5355\u7ec6\u80de\u8868\u8fbe\u3001\u53cc\u7ec6\u80de\u548c\u7fa4\u8fb9\u754c\uff0c\u518d\u7ed9\u7fa4\u4f53\u547d\u540d\u3002<\/p>\n
\u7528\u4e8e\u8bba\u6587\u6bd4\u8f83\u65f6\uff0c\u8fd8\u8981\u628a\u5206\u6bcd\u63a8\u8fdb\u4e00\u6b65\u3002\u672c\u4f8b\u7684 7.77% \u8868\u793a\u8be5\u6587\u5e93\u901a\u8fc7\u8d28\u63a7\u3001\u7eb3\u5165\u6295\u5f71\u7684\u7ec6\u80de\u4e2d\uff0c7.77% \u88ab\u5206\u5230\u4e86\u7fa4 3\u3002\u7ec4\u7ec7\u89e3\u79bb\u3001\u6355\u83b7\u548c\u8d28\u63a7\u90fd\u4f1a\u5f71\u54cd\u8fd9\u4e2a\u6bd4\u4f8b\u3002\u5bf9\u4e8e\u6709\u591a\u4f4d\u4f9b\u4f53\u7684\u7814\u7a76\uff0c\u5e94\u5148\u5728\u6bcf\u4f4d\u4f9b\u4f53\u5185\u90e8\u7edf\u8ba1\u5404\u7fa4\u6570\u91cf\u548c\u5360\u6bd4\uff0c\u540c\u65f6\u4fdd\u7559\u6bcf\u4f4d\u4f9b\u4f53\u7684\u603b\u7ec6\u80de\u6570\u3002\u8fd9\u6837\u80fd\u770b\u89c1\u53d8\u5316\u662f\u5426\u5728\u591a\u4f4d\u4f9b\u4f53\u4e2d\u540c\u5411\uff0c\u4e5f\u80fd\u53d1\u73b0\u67d0\u4e2a\u5927\u6587\u5e93\u5bf9\u5408\u5e76\u56fe\u7684\u5f71\u54cd\u3002\u6761\u4ef6\u95f4\u7684\u63a8\u65ad\u9700\u8981\u4ee5\u72ec\u7acb\u751f\u7269\u5b66\u6837\u672c\u4e3a\u5355\u4f4d\u3002<\/p>\n
\u4e00\u4e2a\u6587\u5e93\u6355\u83b7\u7684\u7ec6\u80de\u591a\uff0c\u4f1a\u600e\u6837\u6539\u53d8\u5408\u5e76\u6bd4\u4f8b\uff1f<\/h2>\n
\u628a\u8fd9\u4e2a\u4f8b\u5b50\u6362\u6210\u4e24\u4e2a\u4f9b\u4f53\uff0c\u95ee\u9898\u4f1a\u66f4\u5bb9\u6613\u770b\u6e05\u3002\u5047\u8bbe\u7532\u63d0\u4f9b\u4e86\u4e00\u5343\u4e2a\u901a\u8fc7\u8d28\u63a7\u7684\u7ec6\u80de\uff0c\u5176\u4e2d\u4e00\u767e\u4e2a\u5c5e\u4e8e\u76ee\u6807\u7fa4\u3002\u4e59\u53ea\u63d0\u4f9b\u4e00\u767e\u4e2a\uff0c\u5176\u4e2d\u56db\u5341\u4e2a\u5c5e\u4e8e\u76ee\u6807\u7fa4\u3002\u628a\u7ec6\u80de\u5148\u5408\u5728\u4e00\u8d77\uff0c\u4f1a\u7b97\u51fa\u4e00\u5343\u4e00\u767e\u4e2a\u7ec6\u80de\u4e2d\u6709\u4e00\u767e\u56db\u5341\u4e2a\u5c5e\u4e8e\u76ee\u6807\u7fa4\uff0c\u7ea6\u5360\u767e\u5206\u4e4b\u5341\u4e8c\u70b9\u4e03\u3002\u5148\u5728\u6bcf\u4e2a\u4f9b\u4f53\u5185\u8ba1\u7b97\u518d\u53d6\u5e73\u5747\uff0c\u5f97\u5230\u7684\u5374\u662f\u767e\u5206\u4e4b\u4e8c\u5341\u4e94\u3002\u8fd9\u4e24\u4e2a\u6570\u5b57\u7528\u4e86\u76f8\u540c\u7684\u6807\u7b7e\uff0c\u533a\u522b\u5b8c\u5168\u6765\u81ea\u7ed9\u4f9b\u4f53\u5206\u914d\u7684\u6743\u91cd\u3002\u524d\u8005\u63cf\u8ff0\u8fd9\u6279\u6355\u83b7\u7ec6\u80de\u7684\u7ec4\u6210\uff0c\u540e\u8005\u63cf\u8ff0\u8fd9\u4e24\u4f4d\u4f9b\u4f53\u5404\u5360\u540c\u7b49\u6743\u91cd\u65f6\u7684\u5e73\u5747\u7ec4\u6210\u3002\u7ed9\u56fe\u6807\u6ce8\u201c\u5e73\u5747\u5360\u6bd4\u201d\u4e4b\u524d\uff0c\u5e94\u80fd\u8bf4\u51fa\u53d6\u7684\u662f\u54ea\u4e00\u79cd\u5e73\u5747\u3002<\/p>\n
ex <- data.frame(donor = c(\"\u7532\", \"\u4e59\"), target = c(100, 40), total = c(1000, 100))\ntransform(ex, pct = round(100 * target \/ total, 1))\nround(100 * c(pooled = sum(ex$target) \/ sum(ex$total),\n              donor_mean = mean(ex$target \/ ex$total)), 1)\n<\/code><\/pre>\n
\u5728\u81ea\u5df1\u7684\u5143\u6570\u636e\u91cc\uff0c\u5e94\u5f53\u4fdd\u7559\u201c\u4f9b\u4f53\u3001\u7ec4\u7ec7\u6765\u6e90\u3001\u91c7\u6837\u65f6\u95f4\u3001\u6587\u5e93\u201d\u56db\u8005\u4e2d\u5b9e\u9645\u5b58\u5728\u7684\u5c42\u7ea7\u3002\u5047\u5982\u540c\u4e00\u4f9b\u4f53\u7684\u4e00\u4efd\u6837\u672c\u5206\u5728\u4e24\u4e2a\u4e0a\u673a\u5b54\uff0c\u5148\u5728\u6837\u672c\u5185\u5408\u5e76\u6280\u672f\u91cd\u590d\u7684\u7ec6\u80de\u8ba1\u6570\u3002\u628a\u4e24\u4e2a\u5b54\u5f53\u6210\u4e24\u4e2a\u53d7\u8bd5\u8005\u4f1a\u51ed\u7a7a\u589e\u52a0\u6837\u672c\u91cf\u3002\u76f8\u53cd\uff0c\u540c\u4e00\u4f9b\u4f53\u6cbb\u7597\u524d\u540e\u7684\u4e24\u6b21\u53d6\u6837\u5e94\u4fdd\u7559\u65f6\u95f4\u70b9\uff0c\u53ef\u4ee5\u753b\u4e00\u6761\u7ebf\u8fde\u63a5\u540c\u4e00\u4eba\u7684\u4e24\u4e2a\u70b9\u3002\u8fd9\u6837\u80fd\u770b\u51fa\u662f\u5927\u591a\u6570\u4eba\u90fd\u5411\u540c\u4e00\u65b9\u5411\u79fb\u52a8\uff0c\u8fd8\u662f\u51e0\u6761\u7ebf\u5f88\u9661\u3001\u5176\u4ed6\u4eba\u51e0\u4e4e\u6ca1\u6709\u53d8\u5316\u3002\u5bf9\u4e8e\u53ea\u91c7\u4e86\u4e00\u4efd\u6587\u5e93\u7684\u5916\u5468\u8840\u793a\u4f8b\uff0c\u53ea\u62a5\u544a\u7ec4\u6210\u63cf\u8ff0\u5373\u53ef\u3002<\/p>\n
\u8fd8\u8981\u533a\u5206\u96f6\u4e0e\u7f3a\u5931\u3002\u5982\u679c\u67d0\u4f4d\u4f9b\u4f53\u901a\u8fc7\u8d28\u63a7\u7684\u7ec6\u80de\u4e2d\u6ca1\u6709\u4e00\u4e2a\u88ab\u6807\u4e3a\u76ee\u6807\u7fa4\uff0c\u5b83\u5e94\u51fa\u73b0\u5728\u5360\u6bd4\u8868\u91cc\uff0c\u76ee\u6807\u6570\u586b\u96f6\u3002\u5982\u679c\u8be5\u4f9b\u4f53\u6839\u672c\u6ca1\u6709\u53ef\u7528\u7684\u7ec6\u80de\uff0c\u603b\u7ec6\u80de\u6570\u4e3a\u96f6\uff0c\u5360\u6bd4\u6ca1\u6709\u5b9a\u4e49\u3002\u628a\u540e\u4e00\u79cd\u60c5\u51b5\u4e5f\u5199\u6210\u96f6\uff0c\u4f1a\u4f7f\u6355\u83b7\u5931\u8d25\u7684\u6587\u5e93\u770b\u8d77\u6765\u50cf\u662f\u5b8c\u5168\u6ca1\u6709\u8fd9\u7c7b\u7ec6\u80de\u3002\u6574\u7406\u9891\u6570\u8868\u65f6\u5148\u4fdd\u7559\u6240\u6709\u5df2\u77e5\u6837\u672c\uff0c\u518d\u6807\u51fa\u54ea\u4e00\u884c\u80fd\u8ba1\u7b97\u6bd4\u4f8b\u3002\u51b3\u5b9a\u662f\u5426\u5254\u9664\u6709\u6548\u7ec6\u80de\u6781\u5c11\u7684\u6837\u672c\u4e4b\u524d\uff0c\u5148\u770b\u6bcf\u4e00\u7ec4\u5c06\u635f\u5931\u591a\u5c11\u4f4d\u72ec\u7acb\u4f9b\u4f53\u3002<\/p>\n
\u7ed9\u5c11\u6570\u7fa4\u4f53\u505a\u4e00\u6b21\u9488\u5bf9\u6027\u7684\u6295\u5f71\u590d\u6838<\/h2>\n
\u53ef\u4ee5\u628a\u62bd\u6837\u6761\u7801\u4e0e\u5176\u4f59\u6761\u7801\u5206\u6210\u4e24\u7ec4\uff0c\u5728\u540c\u4e00\u4e2a\u5019\u9009\u7ec6\u80de\u7fa4\u5185\u6bd4\u8f83\u6807\u5fd7\u57fa\u56e0\u68c0\u51fa\u7387\u3002\u5206\u5b50\u662f\u81f3\u5c11\u68c0\u51fa\u4e00\u6b21\u8be5\u6807\u5fd7\u7684\u7ec6\u80de\u6570\uff0c\u5206\u6bcd\u662f\u8be5\u90e8\u5206\u88ab\u5206\u8fdb\u5019\u9009\u7fa4\u7684\u7ec6\u80de\u6570\u3002\u82e5\u62bd\u6837\u4e2d\u4e24\u4e2a\u76f8\u4e92\u8865\u5145\u7684\u6807\u5fd7\u90fd\u5f88\u6e05\u695a\uff0c\u6295\u5f71\u540e\u65b0\u589e\u7684\u4e00\u6279\u7ec6\u80de\u5374\u51e0\u4e4e\u90fd\u53ea\u6709\u7ba1\u5bb6\u57fa\u56e0\uff0c\u5148\u6838\u5bf9\u8fd9\u4e9b\u7ec6\u80de\u7684\u6587\u5e93\u3001\u68c0\u6d4b\u57fa\u56e0\u6570\u548c\u6d4b\u5e8f\u6df1\u5ea6\u3002\u4e5f\u53ef\u4ee5\u9009\u53d6\u76f8\u90bb\u7fa4\u7684\u53cd\u5411\u6807\u5fd7\uff0c\u4e00\u8d77\u753b\u5728\u539f\u59cb\u8868\u8fbe\u5c42\u4e0a\u3002\u53ea\u5728\u7528\u4e8e\u5efa\u56fe\u7684\u4f4e\u7ef4\u5750\u6807\u4e2d\u770b\u70b9\u4e0e\u70b9\u7684\u8fdc\u8fd1\uff0c\u65e0\u6cd5\u66ff\u4ee3\u8fd9\u4e00\u6b65\u8868\u8fbe\u590d\u6838\u3002<\/p>\n
\u53e6\u4e00\u4e2a\u6709\u7528\u7684\u5bf9\u7167\u662f\u4fdd\u6301\u8d28\u91cf\u95e8\u69db\u3001\u57fa\u56e0\u96c6\u5408\u3001\u7ef4\u6570\u53ca\u5176\u4ed6\u53c2\u6570\u4e00\u81f4\uff0c\u4ec5\u6539\u53d8\u62bd\u6837\u79cd\u5b50\u91cd\u8dd1\u3002\u7ed9\u4e24\u6b21\u7ed3\u679c\u6309\u6807\u5fd7\u7ec4\u5408\u547d\u540d\uff0c\u518d\u6309\u6761\u7801\u8ba1\u7b97\u4e24\u6b21\u90fd\u88ab\u5224\u5230\u5019\u9009\u7fa4\u7684\u7ec6\u80de\u6570\uff0c\u4ee5\u53ca\u53ea\u6709\u5176\u4e2d\u4e00\u6b21\u5165\u7fa4\u7684\u7ec6\u80de\u6570\u3002\u5982\u679c\u4e24\u6b21\u90fd\u80fd\u8bc6\u522b\u540c\u4e00\u6838\u5fc3\u7fa4\uff0c\u4e00\u5708\u4f4e\u68c0\u6d4b\u6df1\u5ea6\u7684\u7ec6\u80de\u53cd\u590d\u8de8\u754c\uff0c\u62a5\u544a\u65f6\u53ef\u4ee5\u8bf4\u660e\u7fa4\u6838\u5fc3\u7684\u8bc1\u636e\u4e0e\u8fb9\u7f18\u6807\u7b7e\u7684\u654f\u611f\u6027\u3002\u82e5\u6574\u4e2a\u5c0f\u7fa4\u53ea\u51fa\u73b0\u5728\u4e00\u4e2a\u79cd\u5b50\u91cc\uff0c\u4e0b\u4e00\u6b65\u5e94\u56de\u5230\u539f\u59cb\u8868\u8fbe\u548c\u6837\u672c\u7ec4\u6210\uff0c\u5728\u7ed9\u5b83\u8d77\u751f\u7269\u5b66\u540d\u5b57\u4e4b\u524d\u5148\u5398\u6e05\u5b83\u51fa\u73b0\u7684\u6761\u4ef6\u3002<\/p>\n
\u4e0b\u8f7d\u4ee3\u7801\u548c\u793a\u4f8b\u6570\u636e\uff0c\u5728 Linux \u4e0a\u590d\u73b0<\/h2>\n
\u4e0b\u8f7d\u672c\u7bc7 R \u4ee3\u7801\uff08ZIP\uff0c4.8 KiB\uff09<\/a> \u00b7 \u4e0b\u8f7d\u914d\u5957\u793a\u4f8b\u6570\u636e\uff08ZIP\uff0c7.3 MiB\uff09<\/a><\/p>\n
\u5c06\u4e24\u4e2a ZIP \u89e3\u538b\u5230\u540c\u4e00\u4f4d\u7f6e\uff0c\u8fdb\u5165 001-seurat<\/code> \u76ee\u5f55\uff0c\u8fd0\u884c Rscript --vanilla run.R<\/code>\u3002\u6253\u5f00 results\/metrics.tsv<\/code> \u67e5\u770b\u7ed3\u679c\uff0c\u4ee3\u7801\u5305\u4e2d\u7684 expected\/metrics.tsv<\/code> \u53ef\u4f9b\u9010\u884c\u6838\u5bf9\u3002<\/p>\n
\u672c\u6b21\u4ece\u4e0b\u8f7d\u5305\u590d\u73b0\u7684\u6b65\u9aa4\u662f\u201c\u8d28\u63a7\u3001\u62bd\u6837\u3001\u805a\u7c7b\u53ca\u5168\u91cf\u6807\u7b7e\u6295\u5f71\u201d\u3002Linux x86_64\u3001R 4.5.2\u3001Seurat 5.4.0 \u7684\u8f93\u51fa\u4e3a\u539f\u59cb\u6761\u7801 2700\uff0c\u62bd\u6837\u7ec6\u80de 800\uff0c\u8d28\u63a7\u540e\u5168\u91cf\u7ec6\u80de 2638\u3002<\/p>\n
\u4e0b\u8f7d\u7248\u53e6\u5b58 cluster-counts.csv\u3002\u5148\u5206\u522b\u628a sketch\u3001full \u4e24\u5217\u6c42\u548c\uff0c\u786e\u8ba4\u4e24\u5f20\u540d\u5355\u5404\u81ea\u95ed\u5408\uff0c\u518d\u7528\u6bcf\u7fa4\u4eba\u6570\u9664\u4ee5\u5404\u81ea\u7684\u5217\u548c\u3002\u539f\u6587\u9996\u6b21\u8fd0\u884c\u4e2d\uff0c\u7f16\u53f7 3 \u4e3a 94\/800 \u548c 205\/2638\u3002\u6b64\u6b21 Linux \u8fd0\u884c\u4e2d\uff0c\u540c\u7f16\u53f7\u4e3a 97\/800 \u548c 208\/2638\uff0c\u5206\u522b\u4e3a 12.125% \u548c 7.88%\u3002\u8de8\u8fd0\u884c\u6838\u5bf9\u7ec6\u80de\u7fa4\u8981\u6309\u6761\u7801\u4e0e\u6807\u5fd7\u8868\u8fbe\u5339\u914d\uff0c\u7fa4\u53f7\u53ea\u5728\u4e00\u6b21\u805a\u7c7b\u5185\u90e8\u6709\u610f\u4e49\u3002<\/p>\n
\u5b9e\u8dd1\u540e\u7684\u7ecf\u9a8c\u603b\u7ed3<\/h2>\n
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