{"id":61007,"date":"2024-10-11T14:04:08","date_gmt":"2024-10-11T06:04:08","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61007"},"modified":"2024-10-11T14:04:08","modified_gmt":"2024-10-11T06:04:08","slug":"%e6%8b%92%e7%bb%9d%e5%8d%83%e7%af%87%e4%b8%80%e5%be%8b%ef%bc%81r%e5%8c%85ragas%e5%b8%a6%e4%bd%a0%e6%89%93%e5%bc%80%e5%8d%95%e7%bb%86%e8%83%9e%e8%81%9a%e7%b1%bb%e5%88%86%e6%9e%90%e5%8f%8a%e5%8f%af","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61007","title":{"rendered":"\u62d2\u7edd\u5343\u7bc7\u4e00\u5f8b\uff01R\u5305Ragas\u5e26\u4f60\u6253\u5f00\u5355\u7ec6\u80de\u805a\u7c7b\u5206\u6790\u53ca\u53ef\u89c6\u5316\u7684\u65b0\u5927\u95e8"},"content":{"rendered":"\n

\u524d\u8a00<\/strong><\/strong><\/p>\n\n\n\n

\u4eca\u5929\u5c0f\u679c\u53c8\u548c\u5927\u5bb6\u89c1\u9762\u4e86\uff0c\u8ddf\u968f\u5c0f\u679c\u7684\u6b65\u4f10\u6765\u7ed9\u81ea\u5df1\u7a7a\u762a\u7684\u76ae\u56ca\u6ce8\u5165\u70b9\u77e5\u8bc6\u7684\u80fd\u91cf\u5427\uff01\u63a5\u89e6\u5355\u7ec6\u80de\u5206\u6790\u7684\u5c0f\u4f19\u4f34\u4eec\u90fd\u77e5\u9053\u4e9a\u7c07\u5206\u6790\u662f\u6539\u5584\u5355\u7ec6\u80deRNA-Seq\u6570\u636e\u805a\u7c7b\u548c\u8868\u5f81\u7684\u6709\u529b\u624b\u6bb5\u3002\u7136\u800c\uff0c\u76ee\u524d\u8fd8\u6ca1\u6709\u5de5\u5177\u53ef\u4ee5\u7cfb\u7edf\u5730\u6574\u5408\u6765\u81ea\u591a\u4e2a\u5b50\u96c6\u7fa4\u7684\u7ed3\u679c\uff0c\u8fd9\u4e3a\u4e0b\u6e38\u5206\u6790\u4e2d\u7684\u51c6\u786e\u6570\u636e\u91cf\u5316\u3001\u53ef\u89c6\u5316\u548c\u89e3\u91ca\u5e26\u6765\u4e86\u969c\u788d\u3002\u6240\u4ee5\u4eca\u5929\u5c0f\u679c\u7ed9\u4f60\u4ecb\u7ecd\u7684R\u5305Ragas\u4e00\u5b9a\u8981\u7741\u5927\u773c\u775b\u597d\u597d\u5b66\u4e00\u4e0b\u54e6\uff01\u5b83\u96c6\u6210\u4e86\u591a\u7ea7\u5b50\u805a\u7c7b\u5bf9\u8c61\uff0c\u7528\u4e8e\u7b80\u5316\u5206\u6790\u548c\u53ef\u89c6\u5316\u3002\u5b9e\u65bd\u4e86\u4e00\u79cd\u65b0\u7684\u6570\u636e\u7ed3\u6784\uff0c\u4ee5\u65e0\u7f1d\u8fde\u63a5\u548c\u7ec4\u88c5\u6765\u81ea\u4e0d\u540c\u5b50\u805a\u7c7b\u7ea7\u522b\u7684\u5404\u79cd\u5355\u7ec6\u80de\u5206\u6790\uff0c\u4ee5\u53ca\u51e0\u4e2a\u65b0\u7684\u6216\u589e\u5f3a\u7684\u53ef\u89c6\u5316\u529f\u80fd\u3002\u6b64\u5916\uff0c\u8fd8\u5f00\u53d1\u4e86\u4e00\u79cd\u91cd\u65b0\u6295\u5f71\u7b97\u6cd5\u6765\u6574\u5408\u6765\u81ea\u591a\u4e2a\u4e9a\u7c07\u7684\u6700\u8fd1\u90bb\u56fe\uff0c\u4ee5\u6700\u5927\u9650\u5ea6\u5730\u63d0\u9ad8\u5b83\u4eec\u5728\u7ec4\u5408\u7ec6\u80de\u5d4c\u5165\u4e0a\u7684\u53ef\u5206\u79bb\u6027\uff0c\u4ece\u800c\u663e\u7740\u6539\u5584\u4e86\u7a00\u6709\u548c\u5747\u8d28\u4e9a\u7fa4\u7684\u5448\u73b0\u3002\u6b64\u5916\uff0cRagas\u5305\u6709\u4e0eSeurat\u5bf9\u8c61\u517c\u5bb9\u7684\u7edf\u4e00\u6570\u636e\u7ed3\u6784\uff0c\u4ee5\u534f\u8c03\u5355\u4e2a\u5355\u5143\u6570\u636e\u7684\u5206\u6790\u548c\u53ef\u89c6\u5316\uff0c\u662f\u4e0d\u662f\u7528\u8d77\u6765\u66f4\u65b9\u4fbf\u4e86\u5462\uff1f\u7ec3\u4e86\u5341\u5e74\u751f\u4fe1\u5206\u6790\u7684\u5c0f\u679c\u5bf9\u4e8e\u5355\u7ec6\u80de\u7684\u77e5\u8bc6\u5df2\u7ecf\u5982\u9c7c\u5f97\u6c34\uff0c\u76f8\u4fe1\u6709\u5c0f\u679c\u7684\u52a0\u6301\u4e00\u5b9a\u80fd\u8ba9\u5927\u5bb6\u5b66\u4f1a\uff01\u6ca1\u6709\u65f6\u95f4\u5b66\u4e60\u4e14\u6709\u5355\u7ec6\u80de\u5206\u6790\u7684\u5c0f\u4f19\u4f34\u4eec\u5feb\u6765\u6ef4\u6ef4\u5c0f\u679c\u5662\uff01\u4ece\u5206\u6790\u5230\u53ef\u89c6\u5316\u76f4\u5230\u4f60\u6ee1\u610f\u4e3a\u6b62\uff01<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u5355\u7ec6\u80de\u7684\u6570\u636e\u5b9e\u5728\u662f\u592a\u5927\u4e86\uff0c\u4e00\u4e2a\u6837\u672c\u8981\u5403\u6389\u4f602-4G\u7684\u5185\u5b58\uff0c\u7535\u8111\u5403\u4e0d\u6d88\u7684\u5c0f\u4f19\u4f34\u4eec\u5efa\u8bae\u4f7f\u7528\u670d\u52a1\u5668\uff0c\u6b22\u8fce\u5927\u5bb6\u8054\u7cfb\u5c0f\u679c\uff0c\u4fdd\u8bc1\u670d\u52a1\u5668\u7684\u6027\u4ef7\u6bd4\u6700\u9ad8\u54e6\uff01<\/p>\n\n\n\n

\u4ee3\u7801\u5b9e\u73b0<\/strong><\/strong><\/p>\n\n\n\n

\u52a0\u8f7dR\u5305\u5e76\u6784\u5efaPi\u5bf9\u8c61<\/strong><\/strong><\/p>\n\n\n\n

##\u672c\u5b9e\u9a8c\u6240\u4f7f\u7528\u7684\u6570\u636e\u96c6\u5df2\u7ecf\u96c6\u6210\u5728R\u5305Ragas\u91cc\u9762\u4e86\uff0c\u5c0f\u4f19\u4f34\u4eec\u76f4\u63a5\u52a0\u8f7dR\u5305\u8bfb\u53d6\u6570\u636e\u5c31\u53ef\u4ee5\u5566\u3002<\/p>\n\n\n\n

##\u9996\u5148\uff0c\u52a0\u8f7d Ragas \u5305<\/p>\n\n\n\n

library(Ragas)<\/p>\n\n\n\n

csle.pbmc.small<\/p>\n\n\n\n

## An object of class Seurat<\/p>\n\n\n\n

## 1503 features across 3000 samples within 1 assay<\/p>\n\n\n\n

## Active assay: RNA (1503 features, 1501 variable features)<\/p>\n\n\n\n

##  3 dimensional reductions calculated: pca, harmony, umap<\/p>\n\n\n\n

\u4e0a\u9762\u7684 Seurat \u5bf9\u8c61\u662f Ragas \u4e2d\u7684\u793a\u4f8b\u5bf9\u8c61\u4e4b\u4e00 \u5305\uff0c\u8fd9\u662f scRNA-Seq \u6570\u636e\u5206\u6790\u7684\u4e00\u90e8\u5206\uff0cPBMC \u6765\u81ea \u5065\u5eb7 \uff08cHD\uff09 \u548c\u7cfb\u7edf\u6027\u7ea2\u6591\u72fc\u75ae \uff08cSLE\uff09 \u513f\u7ae5 \uff08https:\/\/www.nature.com\/articles\/s41590-020-0743-0\uff09\u3002\u6267\u884c Ragas \u5206\u6790\uff0c\u6211\u4eec\u9700\u8981\u521b\u5efa\u4e00\u4e2a\u57fa\u4e8eSeurat\u5bf9\u8c61\u3002\u96c6\u6210\u540e\u5bf9\u8c61\u662f\u5355\u7ec6\u80de\u6570\u636e\u5bf9\u8c61\u5df2\u89c4\u8303\u5316\u3001\u6279\u91cf\u96c6\u6210\u5e76\u6267\u884c UMAP \u548c \u805a\u7c7b\u5206\u6790\u3002\u6ce8\u610f\uff1a\u5728\u6211\u4eec\u7684\u5206\u6790\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528 Harmony \u8fdb\u884c\u6279\u91cf\u96c6\u6210\u3002<\/p>\n\n\n\n

\u51fa\u4e8e\u6f14\u793a\u76ee\u7684\uff0c\u6211\u4eec\u9884\u5148\u6784\u5efa\u4e86\u4e00\u4e2a\u540d\u4e3a Pi \u7684\u5bf9\u8c61 \u201ccsle.pbmc.pi\u201d\u57fa\u4e8e\u793a\u4f8b Seurat \u5bf9\u8c61 \uff08csle.pbmc.small\uff09\uff0c \u5b83\u4e0e Ragas \u5305\u4e00\u8d77\u53d1\u5e03\uff0c\u4f5c\u4e3a\u793a\u4f8b\u6570\u636e\u7684\u4e00\u90e8\u5206\u3002<\/p>\n\n\n\n

csle.pbmc.pi<\/p>\n\n\n\n

## An object of class Pi<\/p>\n\n\n\n

## 6 fields in the object: seurat.obj, exp.freq, markers, ds, cell.prop, parent.meta.data.<\/p>\n\n\n\n

## The following fields have been processed:<\/p>\n\n\n\n

##  seurat.obj: A Seurat object of 1503 features and 2901 cells.<\/p>\n\n\n\n

##      1 assay: RNA, and 4 reductions: pca, harmony, umap, rp<\/p>\n\n\n\n

##  exp.freq: A list of numeric matrices containing per gene expression frequencies<\/p>\n\n\n\n

##      1 analysis run:<\/p>\n\n\n\n

##        ExpFreq|subcluster_idents|cutoff=0<\/p>\n\n\n\n

##  markers: A list of data frames containing marker results.<\/p>\n\n\n\n

##      1 analysis run:<\/p>\n\n\n\n

##        Markers|subcluster_idents|AllMarkers|test.use=wilcox<\/p>\n\n\n\n

##  ds: A list of lists and data frames containing pseudobulk analysis results.<\/p>\n\n\n\n

##      1 analysis run:<\/p>\n\n\n\n

##        DS|subcluster_idents|edgeR|group=Groups;sample=Names;gp1=cSLE;gp2=cHD;contrast=cSLE-cHD<\/p>\n\n\n\n

##  cell.prop: A list of data frames containing cell proportion analysis results<\/p>\n\n\n\n

##      1 analysis run:<\/p>\n\n\n\n

##        CellProp|subcluster_idents|unpooled|unpool_by_Names|group=Groups<\/p>\n\n\n\n

## Metadata from the parent object provided? No<\/p>\n\n\n\n

## Subclusters integrated? Yes<\/p>\n\n\n\n

my.pbmc.pi <- CreatePostIntegrationObject(object = csle.pbmc.small)<\/p>\n\n\n\n

## Post-integration object created<\/p>\n\n\n\n

my.pbmc.pi<\/p>\n\n\n\n

## An object of class Pi<\/p>\n\n\n\n

## 6 fields in the object: seurat.obj, exp.freq, markers, ds, cell.prop, parent.meta.data.<\/p>\n\n\n\n

## The following field has been processed:<\/p>\n\n\n\n

##  seurat.obj: A Seurat object of 1503 features and 3000 cells.<\/p>\n\n\n\n

##      1 assay: RNA, and 3 reductions: pca, harmony, umap<\/p>\n\n\n\n

## Metadata from the parent object provided? No<\/p>\n\n\n\n

## Subclusters integrated? No<\/p>\n\n\n\n

\u4e0a\u9762\u7684\u4ee3\u7801\u521b\u5efa\u4e86\u4e00\u4e2a\u4ec5\u5305\u542b Seurat \u7684\u201c\u6700\u5c0f\u201dPi \u5bf9\u8c61 \u5bf9\u8c61\u3002\u5982\u679c\u7528\u6237\u5177\u6709\u5b50\u96c6\u7fa4\u4e2d\u7684\u5176\u4ed6\u5bf9\u8c61 \u5206\u6790\uff0c\u53ef\u4ee5\u9075\u5faa\u5b50\u805a\u7c7b\u5206\u6790 \u7528\u4e8e\u5c06\u4f18\u5316\u7684\u5b50\u96c6\u7fa4\u6807\u8bc6\u91cd\u65b0\u6295\u5f71\u5230 PBMC \u7684 Vignette\u3002\u521b\u5efa Pi \u5bf9\u8c61\u540e\uff0c\u53ef\u4ee5\u5148\u8fd0\u884c UMAP \u5e76\u68c0\u67e5 \u5bf9\u8c61\u4e2d\u6240\u6709\u5355\u5143\u683c\u7684\u7c07\u3002<\/p>\n\n\n\n

RunDimPlot(object = my.pbmc.pi)<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

RunDimPlot(object = my.pbmc.pi,<\/p>\n\n\n\n

           group.by = “cluster.annotation”)<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u5206\u6790\u6570\u636e<\/strong><\/strong><\/p>\n\n\n\n

Ragas \u63d0\u4f9b\u4e86\u4e00\u4e2a\u7528\u6237\u53cb\u597d\u7684\u754c\u9762\u6765\u96c6\u6210\u548c\u53ef\u89c6\u5316 \u591a\u79cd\u5355\u7ec6\u80de\u5206\u6790\u529f\u80fd\uff0c\u5305\u62ec Marker \u5206\u6790\uff0c\u4f7f\u7528\u4f2a\u6563\u4f53\u6cd5\u8fdb\u884c\u5dee\u5206\u72b6\u6001\u5206\u6790\uff0c\u4ee5\u53ca\u5dee\u5206\u7ec6\u80de\u6bd4\u4f8b\u5206\u6790\u3002<\/p>\n\n\n\n

\u6807\u8bb0\u5206\u6790\u548c\u77e9\u9635\u56fe\uff1a\u7528\u6237\u53ef\u4ee5\u901a\u8fc7\u8fd0\u884c \u5305\u88c5\u51fd\u6570 RunFindAllMarkers\uff0c\u7528\u4e8e\u5b58\u50a8\u6807\u8bb0\u7ed3\u679c \u5728 Pi \u5bf9\u8c61\u4e2d\uff0c\u4ee5\u4fbf\u4ee5\u540e\u53ef\u89c6\u5316\u3002<\/p>\n\n\n\n

my.pbmc.pi <- RunFindAllMarkers(my.pbmc.pi,<\/p>\n\n\n\n

                                ident = “cluster.annotation”)<\/p>\n\n\n\n

my.pbmc.pi<\/p>\n\n\n\n

\u8fd0\u884c RunFindAllMarkers \u540e\uff0c\u4e00\u4e2a\u540d\u4e3a\u201cmarkers\u201d\u7684\u65b0\u6570\u636e\u5b57\u6bb5\u5c06 \u88ab\u6dfb\u52a0\u5230\u5f53\u524d Pi \u5bf9\u8c61\u4e2d\uff0c\u8be5\u5bf9\u8c61\u5305\u542b\u5177\u6709\u957f \u552f\u4e00\u6807\u8bc6\u7b26\u79f0\u4e3a \u201c\u6807\u8bb0\u201ccluster.annotation|AllMarkers|test.use=wilcox\u201c\u3002\u8fd9\u4e2a\u72ec\u4e00\u65e0\u4e8c\u7684 \u6807\u8bc6\u7b26\u53ef\u7528\u4e8e\u68c0\u7d22\u6807\u8bb0\u7ed3\u679c\u3002<\/p>\n\n\n\n

my.pbmc.pi[[“markers”]]<\/p>\n\n\n\n

\u77e9\u9635\u56fe\uff1a\u6807\u8bb0\u5206\u6790\u5b8c\u6210\u540e\uff0c\u6bcf\u4e2a\u805a\u7c7b\u7684\u9876\u7ea7\u6807\u8bb0\u53ef\u4ee5\u4f7f\u7528\u77e9\u9635\u56fe\u7ed8\u5236<\/p>\n\n\n\n

RunMatrixPlot(my.pbmc.pi,<\/p>\n\n\n\n

              markers.key = “Markers|cluster.annotation|AllMarkers|test.use=wilcox”,<\/p>\n\n\n\n

              column.anno.name.rot = 45,<\/p>\n\n\n\n

              heatmap.height = 6)<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u4f2a\u4f53\u79ef\u5206\u6790<\/strong><\/strong><\/p>\n\n\n\n

\u5355\u7ec6\u80de\u7814\u7a76\u7684\u53e6\u4e00\u9879\u91cd\u8981\u4efb\u52a1\u662f\u8bc6\u522b \u5404\u7c07\u95f4\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\uff0c\u4e5f\u79f0\u4e3a\u5dee\u5206\u72b6\u6001 \uff08DS\uff09 \u5206\u6790\u3002\u6211\u4eec\u4f7f\u7528 \u51fd\u6570\u4ece Muscat \u5305\u5230\u5bf9\u591a\u6837\u672c\u591a\u7ec4scRNA-Seq\u8fdb\u884c\u4f2a\u6279\u91cf\u5206\u6790\u8bbe\u8ba1\u3002<\/p>\n\n\n\n

\u5728\u8fd0\u884c\u4f2a\u6279\u91cf\u5206\u6790\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u5148\u8fdb\u884c\u8868\u8fbe \u9891\u7387\u5206\u6790\u3002\u5bf9\u7ed9\u5b9a\u7c07\u7684\u4f4e\u8868\u8fbe\u57fa\u56e0 \u5c06\u5728\u6700\u7ec8\u7684\u4f2a\u6279\u91cf\u8f93\u51fa\u4e2d\u88ab\u8fc7\u6ee4\u6389\u3002<\/p>\n\n\n\n

my.pbmc.pi <- CalculateExpFreqs(my.pbmc.pi,<\/p>\n\n\n\n

                                ident = “cluster.annotation”,<\/p>\n\n\n\n

                                verbose = FALSE)<\/p>\n\n\n\n

my.pbmc.pi<\/p>\n\n\n\n

#\u6279\u91cf\u5206\u6790<\/p>\n\n\n\n

my.pbmc.pi <- RunPseudobulkAnalysis(object = my.pbmc.pi,<\/p>\n\n\n\n

                                    ident.var = “cluster.annotation”,<\/p>\n\n\n\n

                                    group.var = “Groups”,<\/p>\n\n\n\n

                                    sample.var = “Names”,<\/p>\n\n\n\n

                                    group.1 = “cSLE”,<\/p>\n\n\n\n

                                    group.2 = “cHD”)<\/p>\n\n\n\n

my.pbmc.pi<\/p>\n\n\n\n

#\u4f7f\u7528 DSDotPlot \u53ef\u89c6\u5316\u4f2a\u6279\u91cf\u7ed3\u679c<\/p>\n\n\n\n

RunDSDotPlot(object = my.pbmc.pi,<\/p>\n\n\n\n

             exp.freq.key = “ExpFreq|cluster.annotation|cutoff=0”,<\/p>\n\n\n\n

             ds.key = “DS|cluster.annotation|edgeR|group=Groups;sample=Names;gp1=cSLE;gp2=cHD;contrast=cSLE-cHD”,<\/p>\n\n\n\n

             p.filter = 0.05,<\/p>\n\n\n\n

             FC.filter = 4,<\/p>\n\n\n\n

             to.adjust = TRUE<\/p>\n\n\n\n

                    )<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u6bd4\u4f8b\u5206\u6790<\/strong><\/strong><\/p>\n\n\n\n

Ragas\u8fd8\u63d0\u4f9b\u4e86\u4e00\u7ec4\u4e30\u5bcc\u7684\u51fd\u6570\u6765\u53ef\u89c6\u5316\u5355\u5143\u683c\u6bd4\u4f8b\u6570\u636e\u3002\u6bd4\u4f8b\u5206\u6790\u6709\u4e24\u79cd\u65b9\u6cd5:\u6c60\u5316\u548c\u975e\u6c60\u5316\u3002\u6df7\u5408\u6bd4\u4f8b\u5206\u6790\u5728\u5355\u7ec6\u80de\u7814\u7a76\u4e2d\u5f97\u5230\u4e86\u5e7f\u6cdb\u7684\u5e94\u7528\uff0c\u4f46\u5b83\u7684\u4e3b\u8981\u7f3a\u70b9\u662f\u7531\u4e8e\u6df7\u5408\u4e86\u6240\u6709\u6837\u672c\u7684\u6240\u6709\u7ec6\u80de\u800c\u5931\u53bb\u4e86\u5bf9\u751f\u7269\u590d\u5236\u7684\u8ddf\u8e2a\uff0c\u8fd9\u4f7f\u5f97\u5f88\u96be\u8bc4\u4f30\u7ec6\u80de\u6bd4\u4f8b\u7684\u53ef\u53d8\u6027\u3002\u56e0\u6b64\uff0c\u5bf9\u4e8e\u591a\u6837\u672c\u591a\u7ec4\u8bbe\u8ba1\uff0c\u975e\u6c60\u5316\u662f\u9996\u9009\u3002<\/p>\n\n\n\n

#\u4f7f\u7528\u201c\u6c60\u5316\u201d\u56fe\u65f6\uff0c\u7528\u6237\u53ef\u4ee5\u8fdb\u4e00\u6b65\u9009\u62e9\u5176\u4e2d\u4e4b\u4e00 \u4ee5\u4e0b\u4e24\u79cd\u65b9\u6cd5\u8ba1\u7b97\u6bd4\u4f8b\uff1a\u6309\u201c\u805a\u7c7b\u201d\u6216\u6309 \u201c\u7ec4\u201d\u3002<\/p>\n\n\n\n

RunProportionPlot(csle.pbmc.small, ## input can be a Seurat object<\/p>\n\n\n\n

                  ident = “cluster.annotation”,<\/p>\n\n\n\n

                  group.by = “Groups”,<\/p>\n\n\n\n

                  method = “pooled”,<\/p>\n\n\n\n

                  pooled.prop.by = “cluster”, ## default<\/p>\n\n\n\n

                  axis.text.angle = 45,<\/p>\n\n\n\n

                  axis.text.size = 8,<\/p>\n\n\n\n

                  return.value = ‘ggplot’<\/p>\n\n\n\n

                  )<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u5982\u679c\u6211\u4eec\u4ea4\u6362\u53c2\u6570\u201cident\u201d\u548c\u201cgroup.by\u201d\u7684\u8f93\u5165\uff0c\u8fd9\u5c06 \u5bfc\u81f4\u663e\u793a\u6240\u6709\u805a\u7c7b\u7684\u5355\u5143\u683c\u6bd4\u4f8b\u7684\u4e0d\u540c\u6761\u5f62\u56fe \u7531\u6bcf\u7ec4\u4e2d\u7684\u7ec6\u80de\u603b\u6570\u5f52\u4e00\u5316\u3002\u8fd9\u662f \u672c\u8d28\u4e0a\u662f\u201c\u6309\u7ec4\u201d\u5408\u5e76\u5355\u5143\u683c\u6bd4\u4f8b\u7684\u5806\u53e0\u7248\u672c\uff0c\u4f46\u6761\u5f62\u56fe\u662f\u5806\u53e0\u7684\u3002<\/p>\n\n\n\n

my.pbmc.pi <- RunProportionPlot(my.pbmc.pi, ## if input is a Pi object, an updated Pi object will be the returned by default<\/p>\n\n\n\n

                                ident = “Groups”,<\/p>\n\n\n\n

                                group.by = “cluster.annotation”,<\/p>\n\n\n\n

                                method = “pooled”,<\/p>\n\n\n\n

                                pooled.prop.by = “cluster”, ## default<\/p>\n\n\n\n

                                axis.text.size = 8)<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u5728\u53e6\u4e00\u79cd\u60c5\u51b5\u4e0b\uff0c\u7528\u6237\u53ef\u4ee5\u6309\u7ec4\u7ed8\u5236\u5355\u5143\u683c\u6bd4\u4f8b\uff0c \u8fd9\u610f\u5473\u7740\u7ed9\u5b9a\u96c6\u7fa4\u4e2d\u7ed9\u5b9a\u7ec4\u7684\u7ec6\u80de\u8ba1\u6570\u4e3a\u7531\u6bcf\u7ec4\u7684\u7ec6\u80de\u603b\u6570\u5f52\u4e00\u5316\u3002<\/p>\n\n\n\n

my.pbmc.pi <- RunProportionPlot(my.pbmc.pi,<\/p>\n\n\n\n

                                ident = “cluster.annotation”,<\/p>\n\n\n\n

                                group.by = “Groups”,<\/p>\n\n\n\n

                                method = “pooled”,<\/p>\n\n\n\n

                                pooled.prop.by = “group”,<\/p>\n\n\n\n

                                axis.text.size = 8)<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u672a\u6c60\u5316\u6bd4\u4f8b\u56fe<\/strong><\/strong><\/p>\n\n\n\n

\u672a\u5408\u5e76\u6bd4\u4f8b\u56fe\u9002\u7528\u4e8e\u591a\u6837\u672c\u3001\u591a\u7ec4 \u7814\u7a76\u8bbe\u8ba1\uff0c\u5141\u8bb8\u6bd4\u8f83\u611f\u5174\u8da3\u7684\u7ec4\uff0c\u4ee5\u53ca\u8bc4\u4f30\u5b83\u4eec\u5728\u5185\u90e8\u7684\u53d8\u5f02\u6027\u6bcf\u4e2a\u7ec4\u3002<\/p>\n\n\n\n

my.pbmc.pi <- RunProportionPlot(my.pbmc.pi,<\/p>\n\n\n\n

                  ident = “cluster.annotation”,<\/p>\n\n\n\n

                  group.by = “Groups”,<\/p>\n\n\n\n

                  method = “unpooled”,<\/p>\n\n\n\n

                  unpool.by = “Names”,<\/p>\n\n\n\n

                  unpool.ncol = 3,<\/p>\n\n\n\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0title.text.size = 6)<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

#\u672a\u6c60\u5316\u6761\u5f62\u56fe<\/p>\n\n\n\n

my.pbmc.pi <- RunProportionPlot(my.pbmc.pi,<\/p>\n\n\n\n

                                ident = “cluster.annotation”,<\/p>\n\n\n\n

                                group.by = “Groups”,<\/p>\n\n\n\n

                                method = “unpooled”,<\/p>\n\n\n\n

                                unpool.by = “Names”,<\/p>\n\n\n\n

                                unpool.plot.type = “barplot”,<\/p>\n\n\n\n

                                unpool.ncol = 3,<\/p>\n\n\n\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0title.text.size = 6)<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u5176\u4ed6\u53ef\u89c6\u5316<\/strong><\/strong><\/p>\n\n\n\n

#\u5e26\u6ce8\u91ca\u7684\u70b9\u9635\u56fe<\/p>\n\n\n\n

my.list <- list(Monocytes = c(“CD14″,”FCGR3A”),<\/p>\n\n\n\n

                B = “MS4A1”,<\/p>\n\n\n\n

                T = c(“CCR7”, “CD8A”),<\/p>\n\n\n\n

                NK = “NKG7”)<\/p>\n\n\n\n

RunAnnotatedDotPlot(object = my.pbmc.pi,<\/p>\n\n\n\n

                    annotations = my.list,<\/p>\n\n\n\n

                    annotation.cols = c( ‘Monocytes’= ‘tomato’, ‘B’ = ‘seagreen’, ‘T’ = ‘steelblue’, ‘NK’ = ‘purple’),<\/p>\n\n\n\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0group.by = “cluster.annotation”)<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

#\u5806\u53e0\u5c0f\u63d0\u7434\u56fe<\/p>\n\n\n\n

RunStackedVlnPlot(my.pbmc.pi,<\/p>\n\n\n\n

                  ident = “cluster.annotation”,<\/p>\n\n\n\n

                  features = my.list,<\/p>\n\n\n\n

                  color.by = “mean.exp”,<\/p>\n\n\n\n

                  column.names.rotation = 90)<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

RunStackedVlnPlot(my.pbmc.pi,<\/p>\n\n\n\n

                  ident = “cluster.annotation”,<\/p>\n\n\n\n

                  features = my.list,<\/p>\n\n\n\n

                  split.by = “Groups”,<\/p>\n\n\n\n

                  color.by = “split.var”,<\/p>\n\n\n\n

                  column.names.rotation = 45)<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

##\u6c47\u603b\u70ed\u56fe<\/p>\n\n\n\n

features <- list(“T cell” = c(“CD8A”, “CD8B”),<\/p>\n\n\n\n

                ISG = c(“ISG15″,”ISG20″,”IFI44L”, “IFI6”, “MX1”),<\/p>\n\n\n\n

                “Housekeeping” = c(“ACTB”))<\/p>\n\n\n\n

RunSummarizedHeatmap(object = my.pbmc.pi,<\/p>\n\n\n\n

                     ident = “cluster.annotation”,<\/p>\n\n\n\n

                     features = features,<\/p>\n\n\n\n

                     split.by = “Groups”,<\/p>\n\n\n\n

                     column.names.rotation = 30,<\/p>\n\n\n\n

                     heatmap.width = 20)<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

##\u8868\u8fbe\u5f0f\u56fe<\/p>\n\n\n\n

RunExpressionPlot(object = my.pbmc.pi,<\/p>\n\n\n\n

                  feature = “ISG15”,<\/p>\n\n\n\n

                  ident = “cluster.annotation”,<\/p>\n\n\n\n

                  group.by = “Names”,<\/p>\n\n\n\n

                  split.by = “Groups”)<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u6570\u636e\u5bfc\u51fa<\/strong><\/strong><\/p>\n\n\n\n

\u5728 Ragas \u4e2d\uff0c\u6211\u4eec\u63d0\u4f9b\u4e86 ExportPiData \u51fd\u6570\u6765\u5bfc\u51fa\u5206\u6790\u7ed3\u679c\u3002<\/p>\n\n\n\n

ExportPiData(object = csle.pbmc.pi,<\/p>\n\n\n\n

             field = ‘ds’,<\/p>\n\n\n\n

             key = ‘DS|subcluster_idents|edgeR|group=Groups;sample=Names;gp1=cSLE;gp2=cHD;contrast=cSLE-cHD’,<\/p>\n\n\n\n

             file.prefix = ‘pbmc.ds’ ## A mandatory argument. User should provide an informative prefix (e.g., “csle.pbmc”) for exported data<\/p>\n\n\n\n

)<\/p>\n\n\n\n

\u5c0f\u7ed3<\/strong><\/strong><\/p>\n\n\n\n

\u5c0f\u679c\u5411\u5927\u5bb6\u4ecb\u7ecd\u4e86Ragas\uff0c\u8fd9\u662f\u4e00\u4e2a\u57fa\u4e8eR\u7684\u6846\u67b6\uff0c\u7528\u4e8e\u5355\u7ec6\u80de\u5b50\u96c6\u7fa4\u6570\u636e\u7684\u96c6\u6210\u5206\u6790\u548c\u53ef\u89c6\u5316\u3002Ragas\u63d0\u4f9b\u4e86\u4e09\u4e2a\u4e3b\u8981\u8d21\u732e\uff1a\uff08i\uff09\u5b9a\u4e49\u4e86\u4e00\u4e2a\u4e0eSeurat\u5bf9\u8c61\u517c\u5bb9\u7684\u7edf\u4e00\u6570\u636e\u7ed3\u6784\uff0c\u4ee5\u534f\u8c03\u5355\u4e2a\u5355\u5143\u6570\u636e\u7684\u5206\u6790\u548c\u53ef\u89c6\u5316\u3002\uff08ii\uff09\u5f00\u53d1\u4e86\u4e00\u79cd\u91cd\u65b0\u6295\u5f71\u7b97\u6cd5\uff0c\u5c06\u6765\u81ea\u591a\u4e2a\u4e9a\u7c07\u7684\u7ec6\u80de\u5305\u57cb\u7ec4\u5408\u5728\u4e00\u8d77\uff0c\u8be5\u7b97\u6cd5\u53ef\u4ee5\u663e\u793a\u8db3\u591f\u7684\u7ed3\u6784\u7c92\u5ea6\uff0c\u7528\u4e8e\u7a00\u6709\u6216\u540c\u8d28\u4e9a\u7fa4\u3002\uff08iii\uff09 \u4f5c\u4e3a\u5bf9\u73b0\u6709Seurat\u53ef\u89c6\u5316\u529f\u80fd\u7684\u8865\u5145\uff0cRagas \u63d0\u4f9b\u4e86\u4e00\u4e2a\u76f4\u89c2\u7684\u754c\u9762\u6765\u751f\u6210\u65b0\u7684\u6216\u589e\u5f3a\u7684\u5355\u7ec6\u80de\u56fe\u3002\u5230\u8fd9\u91cc\u5c0f\u679c\u8981\u548c\u5927\u5bb6\u8bf4\u518d\u89c1\u4e86\uff01\u6700\u540e\u5c0f\u679c\u7ed9\u5927\u5bb6\u4ecb\u7ecd\u4e00\u4e2a\u4e91\u5de5\u5177\uff01\u540c\u5b66\u4eec\u5982\u679c\u89c9\u5f97\u81ea\u5df1\u7684\u4ee3\u7801\u6c34\u5e73\u4e00\u822c\uff0c\u5bf9\u4e8e\u5f88\u591a\u7684\u53c2\u6570\u4e0d\u77e5\u9053\u600e\u4e48\u6539\uff0c\u53ef\u4ee5\u4f53\u9a8c\u4e00\u4e0b\u6211\u4eec\u7684\u4e91\u751f\u4fe1\u5c0f\u5de5\u5177\uff0c\u53ea\u9700\u8f93\u5165\u6570\u636e\uff0c\u5373\u53ef\u8f7b\u677e\u751f\u6210\u6240\u9700\u56fe\u8868\uff0c\u5b57\u4f53\u5927\u5c0f\u3001\u6807\u9898\u7b49\u4e5f\u53ef\u4e00\u952e\u66f4\u6539\u3002\u611f\u5174\u8da3\u7684\u5c0f\u4f19\u4f34\u53bb\u4e91\u751f\u4fe1\uff08http:\/\/www.biocloudservice.com\/home.html\uff09\u4f53\u9a8c\u4e00\u4e0b\u5427\uff01<\/p>\n","protected":false},"excerpt":{"rendered":"

\u524d\u8a00 \u4eca\u5929\u5c0f\u679c\u53c8\u548c\u5927\u5bb6\u89c1\u9762\u4e86\uff0c\u8ddf\u968f\u5c0f\u679c\u7684\u6b65\u4f10\u6765\u7ed9\u81ea\u5df1\u7a7a\u762a\u7684\u76ae\u56ca\u6ce8\u5165\u70b9\u77e5\u8bc6\u7684\u80fd\u91cf\u5427\uff01\u63a5\u89e6\u5355\u7ec6\u80de\u5206\u6790\u7684\u5c0f\u4f19\u4f34\u4eec\u90fd\u77e5 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"","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\/61007"}],"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=61007"}],"version-history":[{"count":1,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/61007\/revisions"}],"predecessor-version":[{"id":61027,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/61007\/revisions\/61027"}],"wp:attachment":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=61007"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=61007"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=61007"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}