{"id":26799,"date":"2024-01-29T17:16:10","date_gmt":"2024-01-29T09:16:10","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=26799"},"modified":"2024-01-29T17:16:12","modified_gmt":"2024-01-29T09:16:12","slug":"%e5%8d%95%e7%bb%86%e8%83%9e%e6%b5%8b%e5%ba%8f%e5%88%86%e6%9e%90%ef%bc%88%e5%85%ad%ef%bc%89seurat%e5%af%bb%e6%89%bemarker%e5%9f%ba%e5%9b%a0%e4%bb%a5%e5%8f%8a%e7%bb%86%e8%83%9e%e9%89%b4%e5%ae%9a","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=26799","title":{"rendered":"\u5355\u7ec6\u80de\u6d4b\u5e8f\u5206\u6790\uff08\u516d\uff09Seurat\u5bfb\u627emarker\u57fa\u56e0\u4ee5\u53ca\u7ec6\u80de\u9274\u5b9a"},"content":{"rendered":"

\u4e0a\u4e00\u671f\u5c0f\u679c\u4f7f\u7528\u4e86\u4e09\u79cd\u964d\u7ef4\u7b97\u6cd5\u5bf9\u5355\u7ec6\u80de\u6570\u636e\u8fdb\u884c\u4e86\u964d\u7ef4\u548c\u805a\u7c7b\uff0c\u5c062700\u4e2a\u7ec6\u80de\u5206\u6210\u4e869\u4e2a\u7ec6\u80de\u4e9a\u7fa4\u3002\u5e76\u6bd4\u8f83\u4e86\u4e09\u79cd\u7b97\u6cd5\u7684\u5206\u7c7b\u6548\u679c\uff0c\u8fd9\u671f\u6211\u4eec\u5c06\u5728\u6b64\u57fa\u7840\u4e0a\u7ee7\u7eed\u6765\u63a2\u7d22\u8fd9\u4e9b\u7ec6\u80de\u7684\u5965\u79d8\u3002\u5bfb\u627e\u6bcf\u4e2a\u7ec6\u80de\u7fa4\u7684\u6807\u5fd7\u57fa\u56e0\uff0c\u6700\u540e\u6839\u636e\u6807\u5fd7\u57fa\u56e0\u8bc6\u522b\u7ec6\u80de\u7684\u7c7b\u578b\u3002<\/p>\n

\u4e0b\u9762\u5c31\u8ddf\u5c0f\u679c\u4e00\u8d77\u6765\u505a\u5427~<\/p>\n

\u4e00\u3001\u5bfb\u627e\u6807\u8bb0\u57fa\u56e0<\/h2>\n

#\u5bfb\u627e\u7ec6\u80de\u7fa42\u7684\u6807\u8bb0\u57fa\u56e0<\/p>\n

cluster2.markers <- FindMarkers(pbmc, ident.1 = 2, min.pct = 0.25)<\/p>\n

<\/p>\n

\u8fd9\u91cc\u8f93\u51fa\u5f97\u884c\u540d\u662f\u57fa\u56e0\u540d\uff0c\u6bcf\u4e00\u5217\u5206\u522b\u662fp\u503c\uff0c\u7ec6\u80de\u7fa42\u4e0e\u5176\u4ed6\u7ec6\u80de\u7fa4\u76f8\u6bd4\u5e73\u5747\u5f97\u5dee\u5f02\u8868\u8fbe\u500d\u6570\u3002\u6700\u540e\u4e00\u5217\u662f\u6821\u6b63\u540e\u5f97p\u503c\u3002<\/p>\n

\u4f7f\u7528FindMarkers\u51fd\u6570\u5373\u53ef\u5bfb\u627e\u6307\u5b9a\u7ec6\u80de\u7fa4\u7684\u6807\u8bb0\u57fa\u56e0\u3002\u539f\u7406\u662f\u8ba1\u7b97\u548c\u5176\u4ed6\u7ec6\u80de\u7fa4\u7684\u8868\u8fbe\u5dee\u5f02\u500d\u7387\u6700\u5927\u7684\u57fa\u56e0\uff0c\u5373\u4e3a\u8be5\u7ec6\u80de\u7fa4\u7684\u6807\u8bb0\u57fa\u56e0\u3002<\/p>\n

#\u67e5\u627e\u533a\u5206\u7ec6\u80de\u7fa45\u4e0e\u7ec6\u80de\u7fa40\u548c3\u7684\u6240\u6709\u6807\u8bb0<\/p>\n

cluster5.markers <- FindMarkers(pbmc, ident.1 = 5, ident.2 = c(0, 3), min.pct = 0.25)<\/p>\n

head(cluster5.markers, n = 5)<\/p>\n

<\/p>\n

#\u4e00\u6b21\u5bfb\u627e\u6bcf\u4e2a\u7ec6\u80de\u7fa4\u6807\u8bb0\u57fa\u56e0<\/p>\n

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

<\/p>\n

\u4f7f\u7528FindAllMarkers\u51fd\u6570\u5c31\u80fd\u4e00\u6b21\u627e\u51fa\u6bcf\u4e00\u7ec6\u80de\u7fa4\u7684\u6807\u8bb0\u57fa\u56e0\u3002\u51fd\u6570\u540d\u975e\u5e38\u76f4\u767d\uff0c\u662f\u4e0d\u662f\u975e\u5e38\u597d\u8bb0\u5462\u3002~<\/p>\n

\u89c2\u5bdf\u6570\u636e\u53ef\u4ee5\u770b\u5230\uff0c\u540e\u9762\u589e\u52a0\u4e86\u57fa\u56e0\u6765\u81ea\u54ea\u4e2a\u7ec6\u80de\u7fa4\uff0c\u4ee5\u53ca\u589e\u52a0\u4e86\u4e00\u5217gene\u7684\u540d\u79f0\uff0c\u4fbf\u4e8e\u6570\u636e\u64cd\u4f5c\u3002<\/p>\n

top3 <- pbmc.markers %>% group_by(cluster) %>% top_n(n=3,wt=avg_log2FC)<\/p>\n

<\/p>\n

\u4f7f\u7528\u8fd9\u6837\u4e00\u884c\u547d\u4ee4\uff0c\u901a\u8fc7\u7ba1\u9053\u64cd\u4f5c\uff0cgroup_by\u5c06\u57fa\u56e0\u6309\u7ec6\u80de\u7fa4\u5206\u7c7b\uff0c\u7136\u540e\u5217\u51fa\u5dee\u5f02\u8868\u8fbe\u524d\u4e09\u7684\u57fa\u56e0\u3002\u5373\u4e3a\u6807\u8bb0\u57fa\u56e0\u3002<\/p>\n

Seurat\u6709\u51e0\u4e2a\u68c0\u9a8c\u7b97\u6cd5\uff0c\u53ef\u4ee5\u4f7f\u7528test.use\u53c2\u6570\u8fdb\u884c\u8bbe\u7f6e\u3002\u4f8b\u5982\uff0c\u8fd9\u91cc\u4f7f\u7528\u7684ROC\u6d4b\u8bd5\u8fd4\u56de\u4efb\u4f55\u5355\u4e2a\u6807\u8bb0\u7684\u201c\u5206\u7c7b\u80fd\u529b\u201d\uff08\u8303\u56f4\u4ece0-1\uff09\u3002\u8fd8\u6709wilcox\u3001bimod\u3001t\u3001LR\u7b49\u68c0\u9a8c\u7b97\u6cd5\uff0c\u8be6\u60c5\u8bf7\u4f7f\u7528\uff1fFindMarkers\u67e5\u770b\u3002<\/p>\n

cluster0.markers <- FindMarkers(pbmc, ident.1 = 0, logfc.threshold = 0.25, test.use = “roc”, only.pos = TRUE)<\/p>\n

\u4e8c\u3001\u6807\u8bb0\u57fa\u56e0\u53ef\u89c6\u5316<\/h2>\n

Seurat\u63d0\u4f9b\u4e86\u51e0\u4e2a\u5e38\u7528\u7684\u53ef\u89c6\u5316\u7ec6\u80de\u7fa4\u6807\u8bb0\u57fa\u56e0\u7684\u5de5\u5177\uff0c\u975e\u5e38\u9002\u5408\u662f\u6570\u636e\u63a2\u7d22\u4e0e\u6316\u6398\u3002\u4ee5\u53ca\u8bba\u6587\u63d2\u56fe\u7ed8\u5236\u3002<\/p>\n

\u5c0f\u63d0\u7434\u56fe<\/h3>\n

VlnPlot<\/a>(pbmc, features = c<\/a>(“MS4A1”, “CD79A”))<\/p>\n

<\/p>\n

\u901a\u8fc7\u5c0f\u63d0\u7434\u56fe\u53ef\u4ee5\u660e\u663e\u770b\u51fa”MS4A1″, “CD79A”\u662f\u7ec6\u80de\u7fa43\u7684\u6807\u8bb0\u57fa\u56e0\u3002<\/p>\n

\u6807\u8bb0\u57fa\u56e0\u56fe<\/h3>\n

FeaturePlot(pbmc, features = c(“MS4A1”, “GNLY”, “CD3E”, “CD14”, “FCER1A”, “FCGR3A”, “LYZ”, “PPBP”,”CD8A”))<\/p>\n

<\/p>\n

\u8fd9\u4e2a\u56fe\u662f\u6700\u5e38\u7528\u7684\u56fe\u4e4b\u4e00\u3002 features\u662f\u63d0\u524d\u6316\u6398\u7684\u6bcf\u4e2a\u7ec6\u80de\u7fa4\u7684\u6807\u8bb0\u57fa\u56e0\u3002\u6e05\u695a\u663e\u793a\u4e86\u6bcf\u4e2a\u7ec6\u80de\u7fa4\u7684\u6807\u8bb0\u57fa\u56e0\u3002<\/p>\n

\u70ed\u56fe<\/h3>\n

pbmc.markers %>%<\/p>\n

group_by(cluster) %>%<\/p>\n

top_n(n = 10, wt = avg_log2FC) -> top10<\/p>\n

DoHeatmap(pbmc, features = top10$gene) + NoLegend()<\/p>\n

<\/p>\n

\u53ef\u4ee5\u6e05\u6670\u7684\u770b\u51fa\u533a\u5206\u7ec6\u80de\u7fa4\u7684\u6807\u8bb0\u57fa\u56e0\uff08\u9ec4\u8272\u90e8\u5206\uff09\u3002<\/p>\n

\u4e09\u3001\u7ec6\u80de\u9274\u5b9a<\/h2>\n

\u6211\u4eec\u53ef\u4ee5\u6839\u636e\u7ec6\u80de\u7684\u6807\u5fd7\u57fa\u56e0\u67e5\u9605\u5176\u786e\u5207\u7684\u7ec6\u80de\u540d\u79f0\u3002\u5728\u8fd9\u4e2a\u6570\u636e\u96c6\u7684\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u89c4\u8303\u540d\u79f0\u6765\u8f7b\u677e\u5730\u5c06\u65e0\u504f\u805a\u7c7b\u7684\u7ec6\u80de\u7fa4\u4e0e\u5df2\u77e5\u7ec6\u80de\u7c7b\u578b\u76f8\u5339\u914d\uff1a<\/p>\n

<\/p>\n

new.cluster.ids <- c(“Naive CD4 T”, “CD14+ Mono”, “Memory CD4 T”, “B”, “CD8 T”, “FCGR3A+ Mono”,”NK”, “DC”, “Platelet”)<\/p>\n

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

#\u7ec6\u80de\u7fa4\u7c7b\u91cd\u547d\u540d<\/p>\n

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

#\u7ed8\u5236UMAP\u56fe<\/p>\n

DimPlot(pbmc, reduction = “umap”, label = TRUE, pt.size = 0.5) + NoLegend()<\/p>\n

<\/p>\n

\u8fd9\u6837\u5c31\u8bc6\u522b\u51fa\u6bcf\u4e2a\u7ec6\u80de\u7fa4\u7684\u7c7b\u578b\u4e86\u3002<\/p>\n

p1 <- DimPlot(pbmc, reduction = “umap”, label = TRUE, pt.size = 1.5) + NoLegend()<\/p>\n

p2 <- DimPlot(pbmc, reduction = “tsne”, label = TRUE, pt.size = 1.5) + NoLegend()<\/p>\n

p3 <- DimPlot(pbmc, reduction = “pca”, label = TRUE, pt.size = 1.5)<\/p>\n

p1+p2+p3<\/p>\n

<\/p>\n

\u6211\u4eec\u518d\u5bf9\u4e09\u79cd\u964d\u7ef4\u7b97\u6cd5\u5206\u7c7b\u7684\u7ec6\u80de\u4e9a\u7fa4\u8fdb\u884c\u6bd4\u8f83\uff0c\u91cd\u65b0\u8f93\u51fa\u7ed3\u679c\u56fe\u3002<\/p>\n

\u56db\u3001\u8f93\u51fa\u8f6f\u4ef6\u7248\u672c\u4fe1\u606f<\/h2>\n

sessionInfo()<\/p>\n

<\/p>\n

\u6700\u540e\u53ef\u4ee5\u7528sessionInfo()\u6765\u8f93\u51fa\u8f6f\u4ef6\u7248\u672c\u4fe1\u606f\uff0c\u7528\u4e8e\u8bba\u6587\u7684\u8865\u5145\u6750\u6599\u5f53\u4e2d\u3002<\/p>\n

\u5c0f\u679c\u7684Seurat\u5355\u7ec6\u80de\u5206\u6790\u5c31\u5230\u8fd9\u91cc\u4e86\u3002\u5982\u679c\u60a8\u8ddf\u7740\u505a\u4e86\u4e00\u6b21\u7684\u8bdd\u4e00\u5b9a\u80fd\u5bf9\u5355\u7ec6\u80de\u5206\u6790\u7406\u89e3\u5f97\u66f4\u52a0\u6df1\u5165\u3002\u5c0f\u679c\u8fd8\u4f1a\u4e3a\u5927\u5bb6\u5e26\u6765\u66f4\u591a\u66f4\u4f18\u8d28\u7684\u5b66\u4e60\u8d44\u6e90\u3002\u6211\u4eec\u4e0b\u6b21\u89c1\u5566~<\/p>\n","protected":false},"excerpt":{"rendered":"

\u4e0a\u4e00\u671f\u5c0f\u679c\u4f7f\u7528\u4e86\u4e09\u79cd\u964d\u7ef4\u7b97\u6cd5\u5bf9\u5355\u7ec6\u80de\u6570\u636e\u8fdb\u884c\u4e86\u964d\u7ef4\u548c\u805a\u7c7b\uff0c\u5c062700\u4e2a\u7ec6\u80de\u5206\u6210\u4e869\u4e2a\u7ec6\u80de\u4e9a\u7fa4\u3002\u5e76\u6bd4\u8f83\u4e86\u4e09\u79cd\u7b97\u6cd5 […]<\/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\/26799"}],"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=26799"}],"version-history":[{"count":1,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/26799\/revisions"}],"predecessor-version":[{"id":26813,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/26799\/revisions\/26813"}],"wp:attachment":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=26799"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=26799"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=26799"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}