{"id":67108,"date":"2026-11-14T19:47:54","date_gmt":"2026-11-14T11:47:54","guid":{"rendered":"https:\/\/www.biocloudservice.com\/wordpress\/?p=67108"},"modified":"2026-11-14T19:53:16","modified_gmt":"2026-11-14T11:53:16","slug":"reproduce-early-onset-crc-myeloid-fig1","status":"publish","type":"post","link":"https:\/\/www.biocloudservice.com\/wordpress\/reproduce-early-onset-crc-myeloid-fig1\/","title":{"rendered":"55 \u4e07\u7ec6\u80de\uff0c\u5e74\u9f84\u8d8a\u5927\u9ad3\u7cfb\u7ec6\u80de\u8d8a\u591a\u5417\uff1f\u7528 168 \u4f4d\u7ed3\u76f4\u80a0\u764c\u60a3\u8005\u590d\u73b0\u4e00\u5f20\u56fe"},"content":{"rendered":"
\n

\u590d\u73b0\u8fd9\u7bc7\u8bba\u6587\uff0c\u6700\u5bb9\u6613\u72af\u7684\u9519\u53ea\u6709\u4e00\u884c\u4ee3\u7801\u3002\u628a\u7ec6\u80de\u7c7b\u578b\u7b49\u4e8e Myeloid<\/code> \u7684\u884c\u6570\u51fa\u6765\uff0c\u60a3\u8005\u5e74\u9f84\u4e0e\u9ad3\u7cfb\u7ec6\u80de\u6bd4\u4f8b\u7684\u76f8\u5173\u7cfb\u6570\u662f 0.139<\/strong>\u3002\u628a\u53e6\u4e00\u6279\u6570\u636e\u4e2d\u4f7f\u7528\u7684 McDC<\/code> \u6807\u7b7e\u4e5f\u8ba1\u5165\uff0c\u76f8\u5173\u7cfb\u6570\u53d8\u6210 0.261<\/strong>\u3002\u60a3\u8005\u8fd8\u662f\u90a3 168 \u4f4d\uff0c\u6539\u53d8\u7684\u662f\u6211\u4eec\u600e\u6837\u8bc6\u522b\u4e0d\u540c\u7814\u7a76\u91cc\u7684\u540c\u4e00\u7c7b\u7ec6\u80de\u3002\u4e0b\u9762\u4ece\u539f\u59cb\u516c\u5f00\u5bf9\u8c61\u5f00\u59cb\uff0c\u9010\u6b65\u89e3\u91ca\u8fd9\u4e24\u4e2a\u6570\u5b57\uff0c\u4e5f\u4ea4\u4ee3\u6211\u4eec\u548c\u8bba\u6587\u7a76\u7adf\u5bf9\u4e0a\u4e86\u54ea\u4e00\u6b65\u3002Peng \u4e0e Zhan\uff0cnpj Precision Oncology\uff0c2025\uff0c\u539f\u6587\u56fe 1<\/a><\/p>\n

\"\u6309\u60a3\u8005\u8ba1\u7b97\u5e74\u9f84\u4e0e\u9ad3\u7cfb\u7ec6\u80de\u6bd4\u4f8b\uff0c\u4e24\u79cd\u539f\u59cb\u6807\u7b7e\u53e3\u5f84\u7684\u6563\u70b9\u56fe\u53ca\u7ed3\u679c\"<\/a><\/p>\n

\u7814\u7a76\u95ee\u9898\u548c\u65b9\u6cd5\u4eae\u70b9<\/h2>\n

\u7814\u7a76\u8005\u60f3\u77e5\u9053\uff0c\u8f83\u5e74\u8f7b\u7684\u7ed3\u76f4\u80a0\u764c\u60a3\u8005\uff0c\u80bf\u7624\u4e2d\u7684\u514d\u75ab\u7ec6\u80de\u7ec4\u6210\u662f\u5426\u4e0d\u540c\u3002\u4ed6\u4eec\u6574\u5408\u516d\u9879\u516c\u5f00\u5355\u7ec6\u80de\u7814\u7a76\uff0c\u8986\u76d6 22 \u5230 91 \u5c81\u7684 168 \u4f4d\u60a3\u8005\u3002\u8bba\u6587\u7684\u56fe 1 \u5177\u4f53\u95ee\u7684\u662f\uff0c\u6bcf\u4f4d\u60a3\u8005\u6355\u83b7\u7684\u80bf\u7624\u7ec4\u7ec7\u7ec6\u80de\u4e2d\uff0c\u9ad3\u7cfb\u7ec6\u80de\u5360\u591a\u5c11\u3002\u56fe 1C \u6570\u5404\u5e74\u9f84\u7ec4\u6709\u51e0\u4f4d\u60a3\u8005\uff0c\u56fe 1G \u628a\u5e74\u9f84\u5f53\u4f5c\u8fde\u7eed\u53d8\u91cf\uff0c\u6bcf\u4f4d\u60a3\u8005\u753b\u4e00\u4e2a\u70b9\u3002\u8bba\u6587\u968f\u540e\u5728 TCGA \u7ed3\u80a0\u817a\u764c\u7684 bulk RNA-seq \u4e2d\u7528 BayesPrism \u4f30\u8ba1\u7ec6\u80de\u6bd4\u4f8b\uff0c\u89c2\u5bdf\u540c\u65b9\u5411\u7684\u5173\u7cfb\u3002\u540e\u8005\u63d0\u4f9b\u4e86\u53e6\u4e00\u4e2a\u961f\u5217\u7684\u8bc1\u636e\uff0c\u6d4b\u91cf\u65b9\u5f0f\u4e5f\u968f\u4e4b\u6539\u53d8\u3002\u8bba\u6587\u56fe 1 \u4e0e\u65b9\u6cd5<\/a><\/p>\n

\u8fd9\u5957\u8bbe\u8ba1\u503c\u5f97\u501f\u9274\u7684\u5730\u65b9\uff0c\u662f\u628a\u5355\u7ec6\u80de\u7684\u5206\u8fa8\u7387\u548c\u60a3\u8005\u5c42\u9762\u7684\u6bd4\u8f83\u63a5\u4e86\u8d77\u6765\u3002\u5047\u5982\u76f4\u63a5\u628a\u51e0\u4e07\u4e2a\u7ec6\u80de\u5f53\u6210\u72ec\u7acb\u60a3\u8005\uff0c\u5efa\u5e93\u65f6\u591a\u6355\u83b7\u4e86\u7ec6\u80de\u7684\u6837\u672c\u5c31\u4f1a\u83b7\u5f97\u66f4\u5927\u6743\u91cd\u3002\u4e0b\u9762\u6240\u6709\u6bd4\u4f8b\u90fd\u4ee5\u5355\u4e2a\u60a3\u8005\u4e3a\u5355\u4f4d\u3002\u5206\u6bcd\u5305\u62ec\u8be5\u60a3\u8005\u901a\u8fc7\u8d28\u63a7\u7684\u4e0a\u76ae\u3001\u514d\u75ab\u3001\u57fa\u8d28\u7b49\u6240\u6709\u7ec6\u80de\u3002\u5982\u679c\u5148\u53d6\u514d\u75ab\u7ec6\u80de\u5b50\u96c6\u518d\u7b97\uff0c\u56de\u7b54\u7684\u5c31\u53d8\u6210\u201c\u9ad3\u7cfb\u7ec6\u80de\u5728\u514d\u75ab\u7ec6\u80de\u4e2d\u5360\u591a\u5c11\u201d\u3002<\/p>\n

\u7b2c\u4e00\u6b65\u3000\u9009\u4e00\u4e2a\u9002\u5408\u81ea\u5df1\u7684\u8d77\u70b9<\/h2>\n

\u60f3\u5148\u770b\u65b9\u6cd5\uff0c\u4e0b\u8f7d\u672c\u6587\u811a\u672c\u5305\uff0c\u89e3\u538b\u540e\u5728\u76ee\u5f55\u91cc\u6267\u884c Rscript --vanilla quickcheck.R age_moments.tsv<\/code> \u5373\u53ef\u3002\u8fd9\u4e2a\u5c0f\u6587\u4ef6\u4fdd\u5b58\u6bcf\u4e2a\u5e74\u9f84\u4e0b\u7684\u4eba\u6570\u3001\u60a3\u8005\u6bd4\u4f8b\u4e4b\u548c\u53ca\u5e73\u65b9\u548c\uff0c\u8db3\u4ee5\u91cd\u7b97\u60a3\u8005\u7ea7 Pearson \u76f8\u5173\u548c\u53cc\u4fa7 P \u503c\u3002\u82e5\u53ea\u62ff\u5e74\u9f84\u7ec4\u5747\u503c\u505a\u76f8\u5173\uff0c\u6837\u672c\u91cf\u548c\u6807\u51c6\u8bef\u90fd\u4f1a\u53d8\u3002\u60f3\u68c0\u67e5\u8d28\u63a7\u3001\u91cd\u65b0\u5b9a\u4e49\u7ec6\u80de\u7c7b\u578b\uff0c\u5219\u6309\u4e0b\u9762\u6b65\u9aa4\u4f7f\u7528\u4f5c\u8005\u5bf9\u8c61\u3002\u4e0b\u8f7d\u672c\u6587 R \u811a\u672c\u3001\u8f7b\u91cf\u6570\u636e\u4e0e\u6838\u5bf9\u7ed3\u679c<\/a><\/p>\n

\u51c6\u5907 R\u3001readxl<\/code> \u5305\u4ee5\u53ca\u4e24\u4e2a\u539f\u59cb\u6587\u4ef6\u3002Zenodo \u8bb0\u5f55\u91cc\u53ea\u6709\u4e00\u4e2a\u7ea6 3.87 GB \u7684 crc_seu_raw.rds<\/code>\u3002\u8865\u5145\u6750\u6599\u538b\u7f29\u5305\u4e2d\uff0c\u6587\u4ef6\u540d\u4ee5 MOESM2_ESM.xlsx<\/code> \u7ed3\u5c3e\u7684\u5de5\u4f5c\u7c3f\u5b58\u6709\u7814\u7a76\u6765\u6e90\u548c\u60a3\u8005\u4e34\u5e8a\u8868\u3002\u4e0b\u4f8b\u5728 Linux \u6216 macOS \u7684\u7ec8\u7aef\u91cc\u8fd0\u884c\uff0c\u5df2\u89e3\u538b\u672c\u6587\u811a\u672c\u5305\u7684\u76ee\u5f55\u4f5c\u4e3a\u5de5\u4f5c\u76ee\u5f55\u3002\u4f5c\u8005\u7684 Zenodo \u5bf9\u8c61<\/a> \u00b7 \u8bba\u6587\u8865\u5145\u6750\u6599\u4e0b\u8f7d<\/a><\/p>\n

\n
Shell<\/span><\/div>\n
curl -fL --retry 3 -C - -o crc_seu_raw.rds \\\r\n  'https:\/\/zenodo.org\/records\/15267716\/files\/crc_seu_raw.rds?download=1'\r\ncurl -fL --retry 3 -o supp.zip \\\r\n  'https:\/\/www.ebi.ac.uk\/europepmc\/webservices\/rest\/PMC12627461\/supplementaryFiles'\r\nunzip -p supp.zip '*MOESM2_ESM.xlsx' > supp.xlsx\r\nRscript -e 'stopifnot(unname(tools::md5sum(\"crc_seu_raw.rds\")) == \"5730f7ff6b48be474a4846d278fbfaac\")'\r\nRscript -e 'if (!requireNamespace(\"readxl\", quietly=TRUE)) install.packages(\"readxl\", repos=\"https:\/\/cloud.r-project.org\")'\r\nRscript --vanilla reproduce.R crc_seu_raw.rds supp.xlsx out\r\n<\/code><\/pre>\n<\/div>\n
\u672c\u6b21\u7528 R 4.3.3 \u4ece\u5b8c\u6574 RDS \u5b9e\u8dd1\uff0c\u8fdb\u7a0b\u5cf0\u503c\u7ea6 18.8 GiB<\/strong>\u3002\u6587\u4ef6\u4e0b\u8f7d\u5927\u5c0f\u548c\u8f7d\u5165\u5185\u5b58\u5dee\u522b\u5f88\u5927\uff0c\u56e0\u4e3a\u8bfb\u5165\u6574\u4e2a\u5bf9\u8c61\u65f6\u8fd8\u4f1a\u5c55\u5f00\u8868\u8fbe\u77e9\u9635\u3002\u811a\u672c\u4e00\u53d6\u51fa\u5143\u6570\u636e\u5c31\u91ca\u653e\u5927\u5bf9\u8c61\uff0c\u540e\u9762\u7684\u5e74\u9f84\u7edf\u8ba1\u53ea\u5904\u7406\u4e00\u5f20\u8868\u3002\u5185\u5b58\u7d27\u5f20\u65f6\uff0c\u53ef\u4ee5\u5148\u8d70\u8f7b\u91cf\u8def\u7ebf\u5b66\u4e60\u8ba1\u7b97\u8fc7\u7a0b\u3002<\/p>\n
\u7b2c\u4e8c\u6b65\u3000\u5148\u5bf9\u9f50\u60a3\u8005\uff0c\u518d\u590d\u73b0\u56fe 1C<\/h2>\n
\u811a\u672c\u4f7f\u7528 Supplementary Data 2<\/code> \u7684 patientID<\/code> \u5bf9\u9f50\u4e34\u5e8a\u8868\u548c\u7ec6\u80de\u8868\uff0c\u68c0\u67e5\u60a3\u8005\u7f16\u53f7\u552f\u4e00\u3001\u4e24\u4fa7\u7f16\u53f7\u96c6\u5408\u76f8\u540c\uff0c\u5e76\u6838\u5bf9\u6bcf\u4e2a\u7ec6\u80de\u8bb0\u5f55\u7684\u5e74\u9f84\u3002\u6309\u8865\u8868 Group<\/code> \u8ba1\u6570\uff0c\u56db\u7ec4\u5206\u522b\u6709 17\u300157\u300179\u300115 \u4f4d\u60a3\u8005<\/strong>\uff0c\u4e0e\u56fe 1C \u7684\u56db\u6839\u67f1\u5b50\u4e00\u81f4\u3002\u8bba\u6587\u4ee5\u5c0f\u4e8e 40 \u5c81\u4f5c\u4e3a G1\uff0c\u8bfb\u8005\u82e5\u7814\u7a76\u901a\u5e38\u5b9a\u4e49\u7684\u5c0f\u4e8e 50 \u5c81\u65e9\u53d1\u75c5\u4f8b\uff0c\u5e94\u91cd\u65b0\u5b9a\u4e49\u5206\u7ec4\u3001\u91cd\u65b0\u8ba1\u6570\u3002\u4f5c\u8005\u56fe 1C \u548c\u5206\u7ec4\u8bf4\u660e<\/a><\/p>\n
\n
R<\/span><\/div>\n
clinical <- as.data.frame(readxl::read_excel(\"supp.xlsx\", sheet = \"Supplementary Data 2\"))\r\nstopifnot(!anyDuplicated(clinical$patientID))\r\ntable(clinical$Group)\r\nwith(subset(clinical, Age %in% c(60, 80)), table(Age, Group))\r\n<\/code><\/pre>\n<\/div>\n
\u6700\u540e\u4e00\u884c\u4e13\u67e5\u8fb9\u754c\u3002\u8865\u8868\u628a\u5168\u90e8 8 \u4f4d 60 \u5c81\u60a3\u8005\u5217\u5165 G3\uff0c2 \u4f4d 80 \u5c81\u60a3\u8005\u5217\u5165 G4<\/strong>\u3002\u8fd9\u662f\u503c\u5f97\u5355\u72ec\u8fd0\u884c\u7684\u68c0\u67e5\u3002\u4ec5\u6309\u6b63\u6587\u4e2d\u7684\u201c40\u201360\u201d\u201c60\u201380\u201d\u201c>80\u201d\u91cd\u65b0\u5199\u6761\u4ef6\uff0c\u53ef\u80fd\u628a\u540c\u4e00\u4f4d\u60a3\u8005\u91cd\u590d\u5206\u7ec4\u3001\u653e\u5230\u76f8\u90bb\u7ec4\u6216\u6f0f\u6389\u3002\u8981\u590d\u73b0\u8bba\u6587\uff0c\u76f4\u63a5\u7528\u8865\u8868\u6807\u7b7e\u3002\u8981\u6539\u6210\u81ea\u5df1\u7684\u4e34\u5e8a\u95ee\u9898\uff0c\u628a\u65b0\u533a\u95f4\u7684\u7aef\u70b9\u548c\u4eba\u6570\u4e00\u5e76\u8bb0\u4e0b\u6765\u3002<\/p>\n
\u7b2c\u4e09\u6b65\u3000\u9010\u4e2a\u6838\u5bf9\u8d28\u63a7\u6307\u6807\uff0c\u627e\u51fa\u7b2c\u4e00\u5904\u5206\u6b67<\/h2>\n
\u539f\u59cb\u5bf9\u8c61\u6709 560,238 \u4e2a\u7ec6\u80de<\/strong>\u3002\u8bba\u6587\u65b9\u6cd5\u5199\u660e\u5254\u9664\u68c0\u6d4b\u57fa\u56e0\u6570\u5c0f\u4e8e 400\u3001UMI \u5c0f\u4e8e 1,000 \u6216\u7ebf\u7c92\u4f53\u6bd4\u4f8b\u5927\u4e8e 30% \u7684\u7ec6\u80de\u3002\u5bf9\u8c61\u4e2d\u7684 percent_mt<\/code> \u5df2\u7ecf\u662f\u767e\u5206\u6570\u3002\u4e0b\u9762\u7528\u539f\u6709\u5143\u6570\u636e\u5217\u6267\u884c\u4e00\u6b21\u4ea4\u96c6\u7b5b\u9009\u3002\u8bba\u6587\u5355\u7ec6\u80de\u65b9\u6cd5\u548c\u6570\u636e\u6765\u6e90<\/a> \u00b7 Zenodo \u5bf9\u8c61\u7248\u672c<\/a><\/p>\n
\n
R<\/span><\/div>\n
object <- readRDS(\"crc_seu_raw.rds\")\r\nm <- attr(object, \"meta.data\", exact = TRUE)\r\nrm(object)\r\ninvisible(gc())\r\nkeep <- with(m, nFeature_RNA >= 400 & nCount_RNA >= 1000 & percent_mt <= 30)\r\ntable(keep)\r\nwith(m[keep, ], table(dataset, celltype))\r\n<\/code><\/pre>\n<\/div>\n
\u5b9e\u8dd1\u4e2d\uff0c26,214 \u4e2a\u7ec6\u80de\u7684\u57fa\u56e0\u6570\u4f4e\u4e8e\u95e8\u69db\uff0c50,842 \u4e2a\u7684 UMI \u4f4e\u4e8e\u95e8\u69db\uff0c25,942 \u4e2a\u7684\u7ebf\u7c92\u4f53\u6bd4\u4f8b\u8d85\u8fc7\u95e8\u69db\u3002\u540c\u4e00\u7ec6\u80de\u53ef\u4ee5\u89e6\u53d1\u591a\u9879\uff0c\u4e09\u7ec4\u5254\u9664\u6570\u4e0d\u80fd\u76f4\u63a5\u76f8\u52a0\u3002\u4e09\u9879\u540c\u65f6\u901a\u8fc7\u7684\u6709 479,270 \u4e2a<\/strong>\uff0c\u6bd4\u8bba\u6587\u56fe 1D \u62a5\u544a\u7684 554,930 \u4e2a\u5c11 75,660 \u4e2a<\/strong>\u3002\u4ec5\u51ed\u73b0\u6709\u5143\u6570\u636e\u548c\u6587\u5b57\u65b9\u6cd5\uff0c\u65e0\u6cd5\u5b9a\u4f4d\u5dee\u5f02\u51fa\u5728\u54ea\u4e2a\u5904\u7406\u73af\u8282\u3002\u6211\u4eec\u4fdd\u7559\u8fd9\u4e24\u4e2a\u6570\uff0c\u5e76\u5728\u540e\u7eed\u91cd\u7b97\u4e2d\u4f7f\u7528\u5b9e\u9645\u901a\u8fc7\u7684 479,270 \u4e2a\u3002<\/p>\n
\u7b2c\u56db\u6b65\u3000\u5ba1\u67e5\u6807\u7b7e\uff0c\u91cd\u7b97\u56fe 1G \u7684\u5e74\u9f84\u76f8\u5173<\/h2>\n
\u8bba\u6587\u7528 Seurat 4.1.1 \u5f52\u4e00\u5316\uff0c\u9009 2,000 \u4e2a\u9ad8\u53d8\u57fa\u56e0\uff0c\u4ee5\u524d 30 \u4e2a\u4e3b\u6210\u5206\u505a Harmony \u6837\u672c\u6574\u5408\uff0c\u7528\u5206\u8fa8\u7387 0.3 \u805a\u7c7b\uff0c\u518d\u6839\u636e\u6807\u8bb0\u57fa\u56e0\u7b49\u4fe1\u606f\u6807\u51fa\u4e5d\u4e2a\u5927\u7c7b\u3002\u4f5c\u8005\u4e3e\u51fa\u7684\u6807\u8bb0\u5305\u62ec\u9ad3\u7cfb\u7684 CD14 \u548c\u6d46\u7ec6\u80de\u7684 JCHAIN\u3002Zenodo \u5bf9\u8c61\u7684 celltype<\/code> \u5217\u5b9e\u9645\u6709 20 \u4e2a\u6807\u7b7e\uff0c\u90e8\u5206\u6765\u6e90\u4f7f\u7528 Myeloid<\/code>\uff0c\u53e6\u4e00\u4e9b\u4f7f\u7528 McDC<\/code>\uff0c\u8fd8\u5355\u5217 pDC<\/code>\u3001Neutrophils<\/code>\u3001Mast<\/code> \u7b49\u3002\u6211\u4eec\u6ca1\u6709\u83b7\u5f97\u6bcf\u4e2a\u7ec6\u80de\u5bf9\u5e94\u56fe 1D \u6700\u7ec8\u4e5d\u7c7b\u7684\u6620\u5c04\uff0c\u672c\u6587\u4ee3\u7801\u5c5e\u4e8e\u6839\u636e\u516c\u5f00\u8d44\u6599\u7f16\u5199\u7684\u91cd\u7b97\u811a\u672c\u3002\u8bba\u6587\u65b9\u6cd5\u3001\u6570\u636e\u53ef\u7528\u6027\u8bf4\u660e<\/a> \u00b7 \u672c\u6587\u811a\u672c<\/a><\/p>\n
\u8fd0\u884c\u811a\u672c\u4f1a\u5f97\u5230 out\/labels_by_dataset.tsv<\/code>\u3002\u6253\u5f00\u8fd9\u5f20\u6765\u6e90\u4e0e\u6807\u7b7e\u4ea4\u53c9\u8868\uff0c\u4f60\u4f1a\u770b\u5230\u90e8\u5206\u6765\u6e90\u6ca1\u6709\u4e00\u4e2a\u7ec6\u80de\u53eb Myeloid<\/code>\uff0c\u5374\u6709\u6570\u5343\u4e2a\u53eb McDC<\/code>\u3002\u6309\u5b57\u9762\u7b5b\u9009\u5c31\u4f1a\u628a\u8fd9\u4e9b\u6765\u6e90\u7684\u9ad3\u7cfb\u7ec6\u80de\u6570\u8bb0\u6210\u96f6\u3002\u6211\u4eec\u4fdd\u7559\u5b57\u9762\u53e3\u5f84\u4f5c\u5bf9\u7167\uff0c\u518d\u8bd5\u4e00\u4e2a\u660e\u786e\u53ef\u590d\u67e5\u7684\u5408\u5e76\u53e3\u5f84\uff0c\u53ea\u5408\u5e76\u8fd9\u4e24\u4e2a\u6807\u7b7e\u3002\u8fd9\u91cc\u7684\u5408\u5e76\u662f\u4e00\u9879\u654f\u611f\u6027\u5206\u6790\uff0cpDC<\/code>\u3001\u4e2d\u6027\u7c92\u7ec6\u80de\u548c\u80a5\u5927\u7ec6\u80de\u7b49\u5355\u5217\u6807\u7b7e\u5747\u672a\u5e76\u5165\u3002\u8981\u5efa\u7acb\u81ea\u5df1\u7684\u9ad3\u7cfb\u5b9a\u4e49\uff0c\u8fd8\u9700\u8981\u6838\u67e5\u6765\u6e90\u7814\u7a76\u7684\u6ce8\u91ca\u8bf4\u660e\u548c\u6807\u8bb0\u8868\u8fbe\u3002<\/p>\n
\n
R<\/span><\/div>\n
id <- factor(m$patientID[keep], levels = clinical$patientID)\r\nlabels <- m$celltype[keep]\r\ntotal <- as.integer(table(id))\r\nstopifnot(all(total > 0))\r\nmyeloid <- as.integer(table(id[labels %in% c(\"Myeloid\", \"McDC\")]))\r\nfraction <- myeloid \/ total\r\ncor.test(clinical$Age, fraction, method = \"pearson\", alternative = \"two.sided\")\r\n<\/code><\/pre>\n<\/div>\n
factor<\/code> \u7684 levels<\/code> \u56fa\u5b9a\u4e86 168 \u4f4d\u60a3\u8005\u7684\u987a\u5e8f\u3002\u67d0\u4f4d\u60a3\u8005\u901a\u8fc7\u8d28\u63a7\u3001\u4f46\u8be5\u53e3\u5f84\u4e0b\u6ca1\u6709\u9ad3\u7cfb\u7ec6\u80de\u65f6\uff0c\u5206\u5b50\u4f1a\u6b63\u786e\u8bb0\u4e3a 0\u3002\u4fdd\u7559\u8fd9\u4e9b\u96f6\u624d\u80fd\u907f\u514d\u53ea\u5bf9\u201c\u68c0\u51fa\u8fc7\u9ad3\u7cfb\u7ec6\u80de\u201d\u7684\u4eba\u4f5c\u76f8\u5173\u3002\u4e0b\u8868\u7684\u7ec4\u5747\u503c\u4e5f\u662f\u5148\u7b97\u6bcf\u4f4d\u60a3\u8005\u7684\u6bd4\u4f8b\uff0c\u518d\u5728\u7ec4\u5185\u53d6\u5e73\u5747\u3002<\/p>\n
\n\n\n\n\n\n\n\n
\u6570\u636e\u4e0e\u53e3\u5f84<\/th>\nG1<\/th>\nG2<\/th>\nG3<\/th>\nG4<\/th>\n\u5e74\u9f84\u76f8\u5173 r<\/th>\n\u53cc\u4fa7 P<\/th>\n<\/tr>\n<\/thead>\n
\u672c\u6587\uff0c\u4ec5\u8ba1 Myeloid<\/td>\n6.21%<\/td>\n8.29%<\/td>\n8.59%<\/td>\n15.97%<\/td>\n0.139<\/td>\n0.0731<\/td>\n<\/tr>\n
\u672c\u6587\uff0c\u5408\u5e76 Myeloid \u4e0e McDC<\/td>\n8.68%<\/td>\n11.22%<\/td>\n14.86%<\/td>\n21.25%<\/td>\n0.261<\/td>\n0.000625<\/td>\n<\/tr>\n
\u8bba\u6587\u56fe 1G \u7684\u6700\u7ec8\u6ce8\u91ca<\/td>\n\u2014<\/td>\n\u2014<\/td>\n\u2014<\/td>\n\u2014<\/td>\n0.25<\/td>\n0.0011<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
\u5408\u5e76\u53e3\u5f84\u4e0b\u7684\u65b9\u5411\u548c\u91cf\u7ea7\u4e0e\u539f\u56fe\u63a5\u8fd1\uff0c95% \u7f6e\u4fe1\u533a\u95f4\u4e3a 0.114 \u81f3 0.397<\/strong>\u3002\u8d28\u63a7\u540e\u7684\u7ec6\u80de\u96c6\u5408\u548c\u6ce8\u91ca\u53e3\u5f84\u90fd\u4e0e\u8bba\u6587\u6709\u5c1a\u672a\u89e3\u51b3\u7684\u5dee\u5f02\uff0c\u56e0\u6b64\u4e0d\u80fd\u628a\u76f8\u8fd1\u7684\u7cfb\u6570\u53eb\u4f5c\u7cbe\u786e\u590d\u73b0\u3002\u811a\u672c\u53e6\u5b58 per_patient.tsv<\/code> \u548c age_myeloid.pdf<\/code>\uff0c\u4fbf\u4e8e\u68c0\u67e5\u6563\u70b9\u548c\u79bb\u7fa4\u60a3\u8005\u3002\u539f\u56fe 1H \u7684 TCGA \u53cd\u5377\u79ef\u5c5e\u4e8e\u53e6\u4e00\u9879\u5206\u6790\uff0c\u8fd9\u91cc\u7684\u5bf9\u7167\u503c\u53d6\u81ea\u56fe 1G\u3002\u8bba\u6587\u56fe 1G\u3001H<\/a><\/p>\n
\u628a\u8fd9\u4e2a\u65b9\u6cd5\u7528\u5230\u81ea\u5df1\u7684\u961f\u5217<\/h2>\n
\u5148\u505a\u4e24\u5f20\u5c0f\u8868\u3002\u7b2c\u4e00\u5f20\u6570\u201c\u7814\u7a76\u6765\u6e90 \u00d7 \u5e74\u9f84\u6bb5\u201d\u7684\u60a3\u8005\u6570\uff0c\u627e\u51fa\u54ea\u4e9b\u961f\u5217\u540c\u65f6\u6709\u5e74\u8f7b\u548c\u5e74\u957f\u60a3\u8005\u3002\u7b2c\u4e8c\u5f20\u6570\u201c\u6765\u6e90 \u00d7 \u539f\u59cb\u7ec6\u80de\u7c7b\u578b\u201d\u7684\u7ec6\u80de\u6570\uff0c\u627e\u51fa\u54ea\u51e0\u79cd\u540d\u79f0\u8981\u6838\u5bf9\u3002\u63a5\u7740\u6309\u6765\u6e90\u5206\u522b\u753b\u5e74\u9f84\u548c\u6bd4\u4f8b\u7684\u6563\u70b9\uff0c\u518d\u9010\u4e2a\u53bb\u6389\u4e00\u9879\u7814\u7a76\u91cd\u7b97\u3002\u5982\u679c\u53bb\u6389\u67d0\u4e2a\u6765\u6e90\u540e\u8d8b\u52bf\u6d88\u5931\uff0c\u5c31\u8981\u67e5\u770b\u8be5\u7814\u7a76\u7684\u53d6\u6750\u90e8\u4f4d\u3001\u6837\u672c\u5236\u5907\u548c\u5e74\u9f84\u6784\u6210\u3002Harmony \u53ef\u4ee5\u6539\u5584\u8868\u8fbe\u7a7a\u95f4\u7684\u6279\u6b21\u6df7\u5408\uff0c\u4e0d\u80fd\u8ba9\u4e0d\u540c\u961f\u5217\u7684\u6355\u83b7\u6548\u7387\u81ea\u52a8\u4e00\u81f4\u3002<\/p>\n
\u6700\u540e\u628a\u7ed3\u8bba\u5199\u56de\u5b83\u7684\u5206\u6bcd\u3002\u8fd9\u91cc\u89c2\u5bdf\u5230\u7684\u662f\u6355\u83b7\u7ec6\u80de\u4e2d\u7684\u76f8\u5bf9\u6bd4\u4f8b<\/strong>\u3002\u5373\u4f7f\u9ad3\u7cfb\u7ec6\u80de\u7edd\u5bf9\u6570\u91cf\u4e0d\u53d8\uff0c\u5176\u4ed6\u8c31\u7cfb\u6355\u83b7\u51cf\u5c11\uff0c\u4e5f\u53ef\u80fd\u8ba9\u8fd9\u4e2a\u6bd4\u4f8b\u5347\u9ad8\u3002\u6700\u5e74\u8f7b\u7ec4\u53ea\u6709 17 \u4f4d\u60a3\u8005\uff0c\u4e14\u5404\u4eba\u5e74\u9f84\u3001\u90e8\u4f4d\u548c\u4e34\u5e8a\u7279\u5f81\u4e0d\u80fd\u968f\u610f\u4e92\u6362\u3002\u8fd9\u5f20\u56fe\u80fd\u63d0\u51fa\u201c\u5e74\u9f84\u4e0e\u80bf\u7624\u514d\u75ab\u7ec4\u6210\u76f8\u5173\u201d\u7684\u95ee\u9898\uff0c\u5355\u51ed\u5b83\u65e0\u6cd5\u5224\u65ad\u67d0\u4f4d\u60a3\u8005\u7684\u514d\u75ab\u6cbb\u7597\u53cd\u5e94\u3002<\/p>\n
\u5982\u679c\u4f60\u7684\u961f\u5217\u4e5f\u51fa\u73b0\u201c\u6362\u4e00\u4e2a\u6ce8\u91ca\u53e3\u5f84\uff0c\u7ed3\u8bba\u5c31\u53d8\u4e86\u201d\uff0c\u53ef\u4ee5\u5148\u6574\u7406\u8131\u654f\u7684\u5206\u7ec4\u4eba\u6570\u3001\u5404\u6765\u6e90\u6807\u7b7e\u548c\u4e00\u5f20\u60a3\u8005\u7ea7\u6563\u70b9\u56fe\u3002\u5e26\u8fd9\u4e09\u6837\u6750\u6599\u4e0e\u4e91\u751f\u4fe1\u8ba8\u8bba\uff0c\u624d\u597d\u5224\u65ad\u63a5\u4e0b\u6765\u8be5\u7edf\u4e00\u6ce8\u91ca\u3001\u8c03\u6574\u6bd4\u8f83\u8bbe\u8ba1\uff0c\u8fd8\u662f\u8865\u4e00\u6b21\u5206\u961f\u5217\u9a8c\u8bc1\u3002\u4e91\u751f\u4fe1\u8bfe\u9898\u5206\u6790\u4e0e\u7814\u7a76\u8def\u7ebf\u8bc4\u4f30<\/a><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"
\u4ece\u4f5c\u8005\u516c\u5f00\u7684 560,238 \u4e2a\u7ec6\u80de\u548c 168 \u4f4d\u60a3\u8005\u5f00\u59cb\uff0c\u590d\u73b0\u56fe 1C\uff0c\u6309\u4e24\u79cd\u6ce8\u91ca\u53e3\u5f84\u91cd\u7b97\u5e74\u9f84\u76f8\u5173\uff0c\u5e76\u8ffd\u8e2a\u8d28\u63a7\u7ed3\u679c\u4e0e\u56fe 1G \u7684\u5dee\u5f02\u3002\u542b R \u811a\u672c\u548c\u8f7b\u91cf\u6570\u636e\u3002<\/p>\n","protected":false},"author":1,"featured_media":67109,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[1,71],"tags":[115,619,618,607,620],"class_list":["post-67108","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-part","category-single-cell","tag-seurat","tag-619","tag-618","tag-607","tag-620"],"jetpack_sharing_enabled":true,"jetpack_featured_media_url":"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2026\/11\/fig1-reproduction.png?fit=1080%2C800&ssl=1","_links":{"self":[{"href":"https:\/\/www.biocloudservice.com\/wordpress\/wp-json\/wp\/v2\/posts\/67108","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.biocloudservice.com\/wordpress\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.biocloudservice.com\/wordpress\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.biocloudservice.com\/wordpress\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.biocloudservice.com\/wordpress\/wp-json\/wp\/v2\/comments?post=67108"}],"version-history":[{"count":2,"href":"https:\/\/www.biocloudservice.com\/wordpress\/wp-json\/wp\/v2\/posts\/67108\/revisions"}],"predecessor-version":[{"id":67210,"href":"https:\/\/www.biocloudservice.com\/wordpress\/wp-json\/wp\/v2\/posts\/67108\/revisions\/67210"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.biocloudservice.com\/wordpress\/wp-json\/wp\/v2\/media\/67109"}],"wp:attachment":[{"href":"https:\/\/www.biocloudservice.com\/wordpress\/wp-json\/wp\/v2\/media?parent=67108"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.biocloudservice.com\/wordpress\/wp-json\/wp\/v2\/categories?post=67108"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.biocloudservice.com\/wordpress\/wp-json\/wp\/v2\/tags?post=67108"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}