{"id":422,"date":"2023-08-14T14:01:06","date_gmt":"2023-08-14T06:01:06","guid":{"rendered":"https:\/\/blog.ineuro.net\/?p=422"},"modified":"2023-09-08T13:26:46","modified_gmt":"2023-09-08T05:26:46","slug":"%e5%8d%95%e7%bb%86%e8%83%9e%e5%88%86%e6%9e%90%ef%bc%88%e4%b8%80%ef%bc%89%ef%bc%9a%e7%9f%a9%e9%98%b5%e6%95%b0%e6%8d%ae%e7%9a%84%e5%a4%84%e7%90%86","status":"publish","type":"post","link":"https:\/\/blog.ineuro.net\/index.php\/2023\/08\/14\/422\/","title":{"rendered":"\u5355\u7ec6\u80de\u5206\u6790\uff08\u4e00\uff09\uff1a\u77e9\u9635\u6570\u636e\u7684\u5904\u7406"},"content":{"rendered":"

\u4e00\u3001\u603b\u4f53\u4ee3\u7801<\/h1>\n

Seurat\u5305\u5b98\u65b9\u6587\u6863https:\/\/satijalab.org\/seurat\/articles\/pbmc3k_tutorial<\/a><\/p>\n

#\u52a0\u8f7d\u6240\u9700R\u5305\nlibrary(Seurat)\nlibrary(tidyverse)\nlibrary(dplyr)\nlibrary(patchwork)\nlibrary(ggplot2)\n\n#\u8bfb\u53d610X\u6570\u636e\nscRNA.counts=Read10X(\u201c~\/path)\n\n#\u521b\u5efaSeurat\u5bf9\u8c61\nscRNA <- CreateSeuratObject(scRNA.counts ,min.cells = 3,project="os", min.features = 300)\n\n#\u53bb\u9664\u7ebf\u7c92\u4f53\u548c\u7ea2\u7ec6\u80de\u76f8\u5173\u7684\u57fa\u56e0\n##\u53bb\u9664\u7ebf\u7c92\u4f53\nscRNA[["percent.mt"]] <- PercentageFeatureSet(scRNA, pattern = "^MT-")\n##\u7ea2\u7ec6\u80de\u57fa\u56e0\u6bd4\u4f8b\nHB.genes <- c("HBA1","HBA2","HBB","HBD","HBE1","HBG1","HBG2","HBM","HBQ1","HBZ")\nHB_matched <- match(HB.genes, rownames(scRNA@assays$RNA)) \nHB.genes <- rownames(scRNA@assays$RNA)[HB_matched] \nHB.genes <- HB.genes[!is.na(HB.genes)] \nscRNA[["percent.HB"]]<-PercentageFeatureSet(scRNA, features=HB.genes) \n\n#\u89c2\u5bdf\u6570\u636e\u7ed3\u6784\nlevels(scRNA@active.ident)\ncol.num <- length(levels(scRNA@active.ident))\nviolin <- VlnPlot(scRNA,\n                  features = c("nFeature_RNA", "nCount_RNA", "percent.mt","percent.HB"), \n                  cols =rainbow(col.num), \n                  pt.size = 0.01, \n                  ncol = 4) + \n  theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank()) \n\n#\u5747\u4e00\u5316\u6570\u636e\nscRNAa <- NormalizeData(scRNAa, normalization.method = "LogNormalize", scale.factor = 10000)\n\n#\u5bfb\u627e\u9ad8\u53d8\u57fa\u56e0\nscRNAa <- FindVariableFeatures(scRNAa, selection.method = "vst", nfeatures = 2000) \ntop10 <- head(VariableFeatures(scRNA1), 10) \n\nplot1 <- VariableFeaturePlot(scRNA1) \n\nplot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE, size=2.5) \nplot <- CombinePlots(plots = list(plot1, plot2),legend="bottom") \n\n#\u6570\u636e\u7f29\u653e\nscale.genes <-  rownames(scRNAa)\nscRNAb <- ScaleData(scRNAa, features = scale.genes)\n\n#\u53bb\u9664\u5468\u671f\u7684\u5f71\u54cd\nCaseMatch(c(cc.genes$s.genes,cc.genes$g2m.genes),VariableFeatures(scRNA1))\ng2m_genes = cc.genes$g2m.genes\ng2m_genes = CaseMatch(search = g2m_genes, match = rownames(scRNA1))\ns_genes = cc.genes$s.genes\ns_genes = CaseMatch(search = s_genes, match = rownames(scRNA1))\nscRNA11 <- CellCycleScoring(object=scRNA11,  g2m.features=g2m_genes,  s.features=s_genes)\nscRNA2 <- CellCycleScoring(object=scRNA2,  g2m.features=g2m_genes,  s.features=s_genes)\n\n#\u53bb\u9664\u5468\u671f\u7684\u5f71\u54cd\nlibrary(future)\nplan("multicore",workers = 128)\nplan("multisession", workers = 64)\noptions(future.globals.maxSize= 2147483648)\n\nscRNAb <- ScaleData(scRNA11, vars.to.regress = c("S.Score", "G2M.Score"), features = rownames(scRNA1))\n\n#\u6570\u636e\u964d\u7ef4\n\nscRNAb1 <- RunPCA(scRNAb, features = VariableFeatures(scRNAb)) \nplot1 <- DimPlot(scRNAb1, reduction = "pca", group.by="orig.ident") \nplot1\n\nElbowPlot(scRNAb1, ndims=20, reduction="pca") \npc.num=1:20\nscRNAb1 <- FindNeighbors(scRNAb1, dims = pc.num) \nscRNAb1 <- FindClusters(scRNAb1, resolution = 1.0)\n\nscRNAb1<-BuildClusterTree(scRNAb1)\ninstall.packages('ape')\nPlotClusterTree(scRNAb1)\n\nscRNAb1 = RunTSNE(scRNAb1, dims = pc.num)\nembed_tsne <- Embeddings(scRNAb1, 'tsne')\nwrite.csv(embed_tsne,'embed_tsne.csv')\nplot1 = DimPlot(scRNAb1, reduction = "tsne") \n\nplot1\n\nDimPlot(scRNAb1, reduction = "tsne",label = TRUE) \n\nggsave("tSNE.pdf", plot = plot1, width = 8, height = 7)\n\nscRNAb1 <- RunUMAP(scRNAb1, dims = pc.num)\nembed_umap <- Embeddings(scRNAb1, 'umap')\nwrite.csv(embed_umap,'embed_umap.csv') \nplot2 = DimPlot(scRNAb1, reduction = "umap") \nplot2<\/code><\/pre>\n
\u4e8c\u3001\u91cd\u70b9\u51fd\u6570\u89e3\u91ca<\/h1>\n
1.Read10X<\/h2>\n
Read10X() \u51fd\u6570\u4ece 10X \u8bfb\u53d6 cellranger\u6d41\u7a0b\u7684\u8f93\u51fa\uff0c\u8fd4\u56de\u552f\u4e00\u5206\u5b50\u8bc6\u522b\uff08UMI\uff09\u8ba1\u6570\u77e9\u9635\u3002\u8be5\u77e9\u9635\u4e2d\u7684\u503c\u4ee3\u8868\u5728\u6bcf\u4e2a\u7ec6\u80de\uff08\u5217\uff09\u4e2d\u68c0\u6d4b\u5230\u7684\u6bcf\u4e2a\u7279\u5f81\uff08\u5373\u57fa\u56e0\uff1b\u884c\uff09\u7684\u5206\u5b50\u6570\u3002<\/p>\n
2.CreateSeuratObject<\/h2>\n
\u4f7f\u7528\u8ba1\u6570\u77e9\u9635\u521b\u5efa\u4e00\u4e2a Seurat \u5bf9\u8c61\u3002\u8be5\u5bf9\u8c61\u53ef\u4f5c\u4e3a\u4e00\u4e2a\u5bb9\u5668\uff0c\u540c\u65f6\u5305\u542b\u5355\u7ec6\u80de\u6570\u636e\u96c6\u7684\u6570\u636e\uff08\u5982\u8ba1\u6570\u77e9\u9635\uff09\u548c\u5206\u6790\uff08\u5982 PCA \u6216\u805a\u7c7b\u7ed3\u679c\uff09\u3002<\/p>\n
3.PercentageFeatureSet()<\/h2>\n
PercentageFeatureSet()\u51fd\u6570\u8ba1\u7b97\u7ebf\u7c92\u4f53QC\u6307\u6807\uff0c\u8be5\u51fd\u6570\u8ba1\u7b97\u6e90\u81ea\u4e00\u7ec4\u7279\u5f81\u7684\u8ba1\u6570\u767e\u5206\u6bd4\u3002
\nnFeature_RNA: \u53ef\u4ee5\u7406\u89e3\u4e3a\u88ab\u68c0\u6d4b\u5230\u7684\u57fa\u56e0\u6570
\nnCount_RNA: \u53ef\u4ee5\u7406\u89e3\u4e3a\u88ab\u6d4b\u5e8f\u5230\u7684\u8f6c\u5f55\u672c\u7684\u6570\u91cf
\nR\u8bed\u8a00\u6b63\u5219\u8868\u8fbe\u5f0f\u5e38\u7528\u7b26\u53f7\uff1a
\n\"\u56fe\u7247[1]-\u5403\u4e86\u5403\u4e86\"
\n\"\u56fe\u7247[2]-\u5403\u4e86\u5403\u4e86\"<\/p>\n
4.NormalizeData()<\/h2>\n
\u4ece\u6570\u636e\u96c6\u4e2d\u79fb\u9664\u4e0d\u9700\u8981\u7684\u7ec6\u80de\u540e\uff0c\u4e0b\u4e00\u6b65\u5c31\u662f\u5bf9\u6570\u636e\u8fdb\u884c\u5f52\u4e00\u5316\u5904\u7406\u3002\u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u91c7\u7528\u5168\u5c40\u7f29\u653e\u5f52\u4e00\u5316\u65b9\u6cd5 "LogNormalize"\uff08\u5bf9\u6570\u5f52\u4e00\u5316\uff09\uff0c\u5c06\u6bcf\u4e2a\u7ec6\u80de\u7684\u7279\u5f81\u8868\u8fbe\u6d4b\u91cf\u503c\u6309\u603b\u8868\u8fbe\u91cf\u5f52\u4e00\u5316\uff0c\u518d\u4e58\u4ee5\u7f29\u653e\u56e0\u5b50\uff08\u9ed8\u8ba4\u4e3a 10,000\uff09\uff0c\u7136\u540e\u5bf9\u7ed3\u679c\u8fdb\u884c\u5bf9\u6570\u8f6c\u6362\u3002\u5f52\u4e00\u5316\u503c\u5b58\u50a8\u5728 [["RNA"]]@data\u3002<\/p>\n
5.FindVariableFeatures()<\/h2>\n
\u901a\u8fc7\u76f4\u63a5\u6a21\u62df\u5355\u7ec6\u80de\u6570\u636e\u56fa\u6709\u7684\u5747\u503c-\u65b9\u5dee\u5173\u7cfb\u6539\u8fdb\u4e86\u4e4b\u524d\u7684\u7248\u672c\uff0c\u5e76\u5728 FindVariableFeatures() \u51fd\u6570\u4e2d\u5b9e\u73b0\u3002\u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0c\u4f1a\u8fd4\u56de\u6bcf\u4e2a\u6570\u636e\u96c6\u7684 2000 \u4e2a\u7279\u5f81\u3002\u8fd9\u4e9b\u7279\u5f81\u5c06\u7528\u4e8e\u4e0b\u6e38\u5206\u6790\uff0c\u5982 PCA\u3002<\/p>\n
6.ScaleData()<\/h2>\n
\u5e94\u7528\u7ebf\u6027\u53d8\u6362\uff08"\u7f29\u653e"\uff09\uff0c\u8fd9\u662f PCA \u7b49\u964d\u7ef4\u6280\u672f\u4e4b\u524d\u7684\u6807\u51c6\u9884\u5904\u7406\u6b65\u9aa4\u3002ScaleData() \u51fd\u6570
\n\u00b7 \u79fb\u52a8\u6bcf\u4e2a\u57fa\u56e0\u7684\u8868\u8fbe\u91cf\uff0c\u4f7f\u7ec6\u80de\u95f4\u7684\u5e73\u5747\u8868\u8fbe\u91cf\u4e3a 0
\n\u00b7 \u8c03\u6574\u6bcf\u4e2a\u57fa\u56e0\u7684\u8868\u8fbe\u91cf\uff0c\u4f7f\u7ec6\u80de\u95f4\u7684\u65b9\u5dee\u4e3a 1
\n\u00b7 \u8fd9\u4e00\u6b65\u5728\u4e0b\u6e38\u5206\u6790\u4e2d\u7ed9\u4e88\u540c\u7b49\u6743\u91cd\uff0c\u56e0\u6b64\u9ad8\u8868\u8fbe\u57fa\u56e0\u4e0d\u4f1a\u5360\u4e3b\u5bfc\u5730\u4f4d
\n\u7ed3\u679c\u5b58\u50a8\u5728 [["RNA"]]@scale.data\u4e2d
\n\u5728 Seurat v2 \u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528 ScaleData() \u51fd\u6570\u79fb\u9664\u5355\u7ec6\u80de\u6570\u636e\u96c6\u4e2d\u4e0d\u9700\u8981\u7684\u53d8\u5f02\u6e90\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5 "\u56de\u5f52 "\u4e0e\u7ec6\u80de\u5468\u671f\u9636\u6bb5\u6216\u7ebf\u7c92\u4f53\u6c61\u67d3\u76f8\u5173\u7684\u5f02\u8d28\u6027\u3002\u5728 Seurat v3 \u4e2d\uff0cScaleData()\u4ecd\u7136\u652f\u6301\u8fd9\u4e9b\u7279\u5f81\uff0c\u5373<\/p>\n
pbmc <- ScaleData(pbmc, vars.to.regress = "percent.mt")\n<\/code><\/pre>\n
7.RunPCA()<\/h2>\n
\u51b3\u5b9a\u6570\u636e\u7684\u7ef4\u5ea6\uff1a
\nJackStrawPlot() \u51fd\u6570\u63d0\u4f9b\u4e86\u4e00\u79cd\u53ef\u89c6\u5316\u5de5\u5177\uff0c\u7528\u4e8e\u6bd4\u8f83\u6bcf\u4e2a PC \u7684 p \u503c\u5206\u5e03\u4e0e\u5747\u5300\u5206\u5e03\uff08\u865a\u7ebf\uff09\u3002\u663e\u8457 "\u7684 PC \u5c06\u663e\u793a\u51fa\u4f4e p \u503c\u7279\u5f81\u7684\u5f3a\u70c8\u5bcc\u96c6\uff08\u865a\u7ebf\u4e0a\u65b9\u7684\u5b9e\u5fc3\u66f2\u7ebf\uff09\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u5728\u524d 10-12 \u4e2a PC \u4e4b\u540e\uff0c\u663e\u8457\u6027\u4f1a\u6025\u5267\u4e0b\u964d\u3002
\n\u53e6\u4e00\u79cd\u542f\u53d1\u5f0f\u65b9\u6cd5\u662f\u751f\u6210 "\u8098\u56fe"\uff1a\u6839\u636e\u6bcf\u4e2a\u4e3b\u6210\u5206\u6240\u89e3\u91ca\u7684\u65b9\u5dee\u767e\u5206\u6bd4\u5bf9\u4e3b\u6210\u5206\u8fdb\u884c\u6392\u5e8f\uff08ElbowPlot() \u51fd\u6570\uff09\u3002\u53ef\u4ee5\u89c2\u5bdf\u5230 PC9-10 \u5468\u56f4\u51fa\u73b0\u4e86\u4e00\u4e2a "\u5f2f\u5934"\uff0c\u8868\u660e\u524d 10 \u4e2a PC \u4e2d\u6355\u6349\u5230\u4e86\u5927\u90e8\u5206\u771f\u5b9e\u4fe1\u53f7\u3002<\/p>\n
8.FindNeighbors\/FindClusters<\/h2>\n
\u8fd9\u4e9b\u65b9\u6cd5\u5c06\u7ec6\u80de\u5d4c\u5165\u4e00\u4e2a\u56fe\u7ed3\u6784–\u4f8b\u5982 K \u8fd1\u90bb\uff08KNN\uff09\u56fe\uff0c\u5728\u5177\u6709\u76f8\u4f3c\u7279\u5f81\u8868\u8fbe\u6a21\u5f0f\u7684\u7ec6\u80de\u4e4b\u95f4\u5212\u51fa\u8fb9\uff0c\u7136\u540e\u5c1d\u8bd5\u5c06\u8be5\u56fe\u5212\u5206\u4e3a\u9ad8\u5ea6\u76f8\u4e92\u5173\u8054\u7684 "\u51c6\u5c0f\u533a "\u6216 "\u793e\u533a"\u3002
\n\u4e0e PhenoGraph \u4e00\u6837\uff0c\u9996\u5148\u6839\u636e PCA \u7a7a\u95f4\u4e2d\u7684\u6b27\u6c0f\u8ddd\u79bb\u6784\u5efa\u4e00\u4e2a KNN \u56fe\uff0c\u7136\u540e\u6839\u636e\u4e24\u4e2a\u5355\u5143\u7684\u5c40\u90e8\u90bb\u57df\u4e2d\u7684\u5171\u4eab\u91cd\u53e0\uff08Jaccard \u76f8\u4f3c\u6027\uff09\u6765\u7ec6\u5316\u5b83\u4eec\u4e4b\u95f4\u7684\u8fb9\u6743\u91cd\u3002\u8fd9\u4e00\u6b65\u9aa4\u4f7f\u7528 FindNeighbors() \u51fd\u6570\u6267\u884c\uff0c\u5e76\u5c06\u4e4b\u524d\u5b9a\u4e49\u7684\u6570\u636e\u96c6\u7ef4\u5ea6\uff08\u524d 10 \u4e2a PCs\uff09\u4f5c\u4e3a\u8f93\u5165\u3002
\n\u4e3a\u4e86\u5bf9\u7ec6\u80de\u8fdb\u884c\u805a\u7c7b\uff0c\u6211\u4eec\u63a5\u4e0b\u6765\u4f1a\u5e94\u7528\u6a21\u5757\u5316\u4f18\u5316\u6280\u672f\uff0c\u5982\u5362\u4e07\u7b97\u6cd5\uff08\u9ed8\u8ba4\u503c\uff09\u6216 SLM\uff0c\u5bf9\u7ec6\u80de\u8fdb\u884c\u8fed\u4ee3\u805a\u7c7b\uff0c\u76ee\u7684\u662f\u4f18\u5316\u6807\u51c6\u6a21\u5757\u5316\u51fd\u6570\u3002FindClusters() \u51fd\u6570\u5b9e\u73b0\u4e86\u8fd9\u4e00\u8fc7\u7a0b\uff0c\u5e76\u5305\u542b\u4e00\u4e2a\u5206\u8fa8\u7387\u53c2\u6570\uff0c\u7528\u4e8e\u8bbe\u7f6e\u4e0b\u6e38\u805a\u7c7b\u7684 "\u7c92\u5ea6"\uff0c\u6570\u503c\u8d8a\u5927\uff0c\u805a\u7c7b\u6570\u91cf\u8d8a\u591a\u3002\u6211\u4eec\u53d1\u73b0\uff0c\u5c06\u8be5\u53c2\u6570\u8bbe\u7f6e\u5728 0.4-1.2 \u4e4b\u95f4\uff0c\u901a\u5e38\u80fd\u4e3a 3K \u5de6\u53f3\u7684\u5355\u7ec6\u80de\u6570\u636e\u96c6\u5e26\u6765\u826f\u597d\u7684\u7ed3\u679c\u3002\u5bf9\u4e8e\u66f4\u5927\u7684\u6570\u636e\u96c6\uff0c\u6700\u4f73\u5206\u8fa8\u7387\u901a\u5e38\u4f1a\u63d0\u9ad8\u3002\u53ef\u4ee5\u4f7f\u7528 Idents() \u51fd\u6570\u627e\u5230\u805a\u7c7b\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
\u4e00\u3001\u603b\u4f53\u4ee3\u7801 Seurat\u5305\u5b98\u65b9\u6587\u6863https:\/\/satijalab.org\/seurat\/articles […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1,24],"tags":[],"topics":[],"_links":{"self":[{"href":"https:\/\/blog.ineuro.net\/index.php\/wp-json\/wp\/v2\/posts\/422"}],"collection":[{"href":"https:\/\/blog.ineuro.net\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.ineuro.net\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.ineuro.net\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.ineuro.net\/index.php\/wp-json\/wp\/v2\/comments?post=422"}],"version-history":[{"count":9,"href":"https:\/\/blog.ineuro.net\/index.php\/wp-json\/wp\/v2\/posts\/422\/revisions"}],"predecessor-version":[{"id":431,"href":"https:\/\/blog.ineuro.net\/index.php\/wp-json\/wp\/v2\/posts\/422\/revisions\/431"}],"wp:attachment":[{"href":"https:\/\/blog.ineuro.net\/index.php\/wp-json\/wp\/v2\/media?parent=422"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.ineuro.net\/index.php\/wp-json\/wp\/v2\/categories?post=422"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.ineuro.net\/index.php\/wp-json\/wp\/v2\/tags?post=422"},{"taxonomy":"topics","embeddable":true,"href":"https:\/\/blog.ineuro.net\/index.php\/wp-json\/wp\/v2\/topics?post=422"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}