{"id":28251,"date":"2024-02-06T10:18:53","date_gmt":"2024-02-06T02:18:53","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=28251"},"modified":"2024-02-06T10:18:54","modified_gmt":"2024-02-06T02:18:54","slug":"%e6%83%b3%e6%8f%90%e9%ab%98%e5%8d%95%e7%bb%86%e8%83%9e%e6%b3%a8%e9%87%8a%e5%87%86%e7%a1%ae%e5%ba%a6%ef%bc%9f%e6%89%8b%e5%8a%a8%e6%a0%a1%e6%ad%a3%e9%aa%8c%e8%af%81%e5%88%86%e6%9e%90","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=28251","title":{"rendered":"\u60f3\u63d0\u9ad8\u5355\u7ec6\u80de\u6ce8\u91ca\u51c6\u786e\u5ea6\uff1f\u201c\u624b\u52a8\u6821\u6b63+\u9a8c\u8bc1\u5206\u6790\u201d\u5fc5\u4e0d\u53ef\u5c11\uff01"},"content":{"rendered":"

\u5728\u8fdb\u884c\u5355\u4e2a\u7ec6\u80de\u6c34\u5e73\u7684\u5206\u6790\u65f6\uff0c\u5355\u7ec6\u80de\u8f6c\u5f55\u7ec4\u6d4b\u5e8f\u6280\u672f\u662f\u975e\u5e38\u4fbf\u5229\u9ad8\u6548\u7684\u4e00\u79cd\u6280\u672f\uff0c\u5b83\u53ef\u4ee5\u901a\u8fc7\u6d4b\u91cf\u5355\u4e2a\u7ec6\u80de\u4e2d\u6240\u6709\u57fa\u56e0\u7684\u8868\u8fbe\u6c34\u5e73\uff0c\u4ece\u800c\u8bfb\u53d6\u7ec6\u80de\u7684\u201c\u65e5\u8bb0\u201d\uff0c\u8fd9\u6837\u6211\u4eec\u5c31\u53ef\u4ee5\u5f88\u5bb9\u6613\u7684\u77e5\u9053\u7ec6\u80de\u5728\u505a\u4ec0\u4e48\uff0c\u60f3\u4ec0\u4e48\uff0c\u611f\u53d7\u4ec0\u4e48\uff0c\u4ee5\u53ca\u5176\u751f\u547d\u6d3b\u52a8\u548c\u4ec0\u4e48\u6709\u8054\u7cfb\u3002<\/p>\n

\u800c\u5728\u8fdb\u884c\u5355\u7ec6\u80de\u8f6c\u5f55\u7ec4\u6d4b\u5e8f\u5206\u6790\u7684\u65f6\u5019\uff0c\u6211\u4eec\u5e38\u5e38\u7ed5\u4e0d\u5f00\u7ed9\u7ec6\u80de\u8fdb\u884c\u6ce8\u91ca\u8fd9\u4e00\u6b65\u9aa4\uff0c\u4e5f\u5c31\u662f\u6839\u636e\u5355\u7ec6\u80de\u8f6c\u5f55\u7ec4\u6570\u636e\uff0c\u5bf9\u4e0d\u540c\u7684\u7ec6\u80de\u805a\u7c7b\u8fdb\u884c\u7ec6\u80de\u7c7b\u578b\u7684\u6807\u8bc6\u548c\u547d\u540d\uff0c\u8fd9\u4e00\u6b65\u4e2d\u901a\u5e38\u4f7f\u7528\u7684\u662f\u81ea\u52a8\u6ce8\u91ca\uff0c \u5bf9\u81ea\u52a8\u6ce8\u91ca\u800c\u8a00\uff0c\u662f\u4e00\u79cd\u5229\u7528\u7b97\u6cd5\u548c\u9002\u5f53\u7684\u5148\u9a8c\u751f\u7269\u5b66\u77e5\u8bc6\u5bf9\u7ec6\u80de\u6216\u7ec6\u80de\u7c07\u8fdb\u884c\u6807\u8bb0\u7684\u65b9\u6cd5\uff0c\u8fd9\u79cd\u65b9\u6cd5\u867d\u7136\u9ad8\u901f\u6709\u6548\uff0c\u5374\u5b58\u5728\u9519\u8bef\u6ce8\u91ca\u7684\u53ef\u80fd\u6027\u3002<\/p>\n

\u57fa\u51c6\u7814\u7a76\u8868\u660e\uff0c\u81ea\u52a8\u6ce8\u91ca\u5de5\u5177\u7684\u6027\u80fd\u662f\u5904\u4e8e\u53d8\u5316\u4e2d\u7684\uff0c\u5b83\u7684\u7cbe\u51c6\u5ea6\u53d6\u51b3\u4e8e\u88ab\u6ce8\u91ca\u7ec6\u80de\u7c7b\u578b\u7684\u6570\u636e\u96c6\u548c\u57fa\u56e0\u8868\u8fbe\u8c31\u7684\u72ec\u7279\u6027\uff0c\u6bd4\u5982\u8bf4\uff0c\u4f7f\u7528\u81ea\u52a8\u6ce8\u91ca\u533a\u5206T\u7ec6\u80de\u548cB\u7ec6\u80de\u76f8\u5bf9\u7b80\u5355\uff0c\u4f46\u4f7f\u7528\u81ea\u52a8\u6ce8\u91ca\u5de5\u5177\u533a\u5206CD8+\u7ec6\u80de\u6bd2\u6027T\u7ec6\u80de\u548c\u81ea\u7136\u6740\u4f24\u7ec6\u80de\u5374\u6bd4\u8f83\u56f0\u96be\u3002\u56e0\u6b64\uff0c\u6211\u4eec\u9700\u8981\u5bf9\u81ea\u52a8\u6ce8\u91ca\u8fdb\u884c\u624b\u52a8\u6539\u8fdb\u4ee5\u53ca\u9a8c\u8bc1\u5206\u6790\uff0c\u4ece\u800c\u8ba9\u6211\u4eec\u7684\u6ce8\u91ca\u7ed3\u679c\u66f4\u52a0\u51c6\u786e\u53ef\u4fe1\u3002\u672c\u6b21\u4ee3\u7801\u7684\u6570\u636e\u91cf\u504f\u5927\uff0c\u6b22\u8fce\u5927\u5bb6\u6765\u79df\u8d41\u6211\u4eec\u7684\u670d\u52a1\u5668\u54e6\uff0c\u5173\u6ce8\u516c\u4f17\u53f7\u8fd8\u53ef\u4ee5\u83b7\u5f97\u72ec\u7279\u7684\u751f\u4fe1\u5206\u6790\u601d\u8def\u5b9a\u5236~<\/p>\n

\u63a5\u4e0b\u6765\u5c0f\u679c\u5c06\u4ece\u5934\u5f00\u59cb\uff0c\u5e26\u7740\u5c0f\u4f19\u4f34\u4eec\u4e00\u8d77\u5b8c\u6210\u5355\u7ec6\u80de\u6ce8\u91ca\u7684\u6574\u4e2a\u8fc7\u7a0b~\u672c\u6b21\u6559\u7a0b\u6211\u4eec\u4f7f\u7528\u7684\u662f\u7ec6\u80de\u662f\uff1aPeripheral blood mononuclear cells\uff08PBMC\uff09\u3002\u8fd9\u662f\u5916\u5468\u8840\u4e2d\u5177\u6709\u5355\u4e2a\u6838\u7684\u7ec6\u80de\u7684\u6df7\u5408\u7fa4\u4f53\uff0c\u662f\u514d\u75ab\u7ec6\u80de\u7684\u4e30\u5bcc\u6765\u6e90\uff0c\u53ef\u4ee5\u8fdb\u4e00\u6b65\u7eaf\u5316\u5206\u79bb\u51fa\u591a\u4e2a\u7ec6\u80de\u7c7b\u578b\u3002<\/p>\n

<\/p>\n

\u5982\u56fe\uff0c\u662f\u672c\u6b21\u6240\u9700\u5f97\u57fa\u56e0\u6587\u4ef6\u3002\u53e6\u5916\u672c\u6b21\u5206\u6790\u6240\u9700\u7684R\u7248\u672c\u4ee5\u53caR\u5305\u7248\u672c\u5c0f\u679c\u4e5f\u5e2e\u5927\u5bb6\u6574\u7406\u597d\u4e86~<\/p>\n

<\/p>\n

Step 1 \u5b89\u88c5R\u5305<\/p>\n

install.packages(c(“BiocManager”,”Seurat”,”SCINA”,”ggplot2″,”devtools”,”shiny”))<\/p>\n

BiocManager::install(c(“SingleCellExperiment”,”scater”,”dplyr”, “scmap”,<\/p>\n

“celldex”,”SingleR”,”GSVA”,”S4Vectors”, “SummarizedExperiment”))<\/p>\n

devtools::install_github(“immunogenomics\/harmony”)<\/p>\n

devtools::install_github(“romanhaa\/cerebroApp”)<\/p>\n

devtools::install_github(“mw201608\/msigdb”)<\/p>\n

Step 2 \u57fa\u4e8e\u53c2\u8003\u7684\u81ea\u52a8\u6ce8\u91ca<\/p>\n

#\u4eceGEO\u6570\u636e\u5e93\u4e0b\u8f7d\u6570\u636e\uff08\u7531\u4e8e\u5df2\u7ecf\u4e0b\u8f7d\u597d\u4e86\u6240\u4ee5\u76f4\u63a5\u5bfc\u5165\u6587\u4ef6\u5373\u53ef\uff09<\/p>\n

set.seed(9742)<\/p>\n

ref <- readRDS(“..\/data\/data_DICE.RData”)<\/p>\n

\uff03\u683c\u5f0f\u5316\u6570\u636e\uff0c\u786e\u4fdd\u4e0e\u6b63\u5728\u4f7f\u7528\u7684\u5de5\u5177\u517c\u5bb9\uff0c\u8fd9\u91cc\u4f7f\u7528\u7684\u662fscmap<\/p>\n

# \u5728\u540d\u4e3a\u201ccell_type1\u201d\u7684\u5217\u4e2d\u5206\u914d\u5355\u5143\u683c\u7c7b\u578b\u6807\u7b7e<\/p>\n

SummarizedExperiment::colData(ref)$cell_type1 <- SummarizedExperiment::colData(ref)$label.fine<\/p>\n

# \u5728\u540d\u4e3a\u201cfeature_symbol\u201d\u7684\u5217\u4e2d\u5206\u914d\u57fa\u56e0\u540d\u79f0<\/p>\n

SummarizedExperiment::rowData(ref)$feature_symbol <- rownames(ref)<\/p>\n

# \u5c06\u6570\u636e\u8f6c\u6362\u4e3aSingleCellExperiment\u5bf9\u8c61<\/p>\n

ref_sce <- SingleCellExperiment::SingleCellExperiment(assays=list(logcounts=Matrix::Matrix(SummarizedExperiment::assays(ref)$logcounts)),<\/p>\n

colData=SummarizedExperiment::colData(ref),<\/p>\n

rowData=SummarizedExperiment::rowData(ref))<\/p>\n

\u8fd9\u6837\u53c2\u8003\u6570\u636e\u5c31\u53ef\u4ee5\u4f7f\u7528\u4e86\uff0c\u7136\u540e\u6211\u4eec\u9700\u8981\u5904\u7406\u8fd9\u4e9b\u6570\u636e\u6765\u6784\u5efa\u5c06\u7528\u6765\u5c06\u672a\u6807\u8bb0\u6570\u636e\u6620\u5c04\u7684\u7d22\u5f15\u3002 \u9996\u5148\uff0c\u6211\u4eec\u9009\u62e9\u8981\u4f7f\u7528\u7684\u57fa\u56e0\uff0c\u8fd9\u4e9b\u57fa\u56e0\u5c06\u662f\u5728\u901a\u8fc7\u57fa\u56e0\u4e22\u5931\u5206\u5e03\u5c06\u7ebf\u6027\u6a21\u578b\u62df\u5408\u5230\u57fa\u56e0\u8868\u8fbe\u540e\u88ab scmap \u68c0\u7d22\u7684\u8ba4\u4e3a\u6700\u5177\u4fe1\u606f\u6027\u7684\u57fa\u56e0\uff0c\u4fe1\u606f\u6700\u4e30\u5bcc\u7684\u7ec6\u80de\u5177\u6709\u9ad8\u8868\u8fbe\u503c\u548c\u4f4e\u4e22\u5931\u7387\u3002<\/p>\n

#\u9996\u5148\u9009\u62e9\u4fe1\u606f\u6700\u4e30\u5bcc\u7684\u7279\u5f81\u6765\u521b\u5efa scmap-cluster \u53c2\u8003<\/p>\n

ref_sce <- scmap::selectFeatures(ref_sce, suppress_plot=FALSE)<\/p>\n

<\/p>\n

\u5982\u56fe\u662f\u901a\u8fc7\u53ef\u89c6\u5316\u6765\u5c55\u793a\u5355\u7ec6\u80de\u8f6c\u5f55\u7ec4\u6570\u636e\u7684\u8d28\u91cf\u63a7\u5236\uff0c\u7279\u5f81\u9009\u62e9\u7684\u7ed3\u679c\u3002log2(Expression)\u8868\u793a\u6bcf\u4e2a\u7279\u5f81\u7684\u5e73\u5747\u8868\u8fbe\u6c34\u5e73\uff0clog2(% of dropouts)\u8868\u793a\u6bcf\u4e2a\u7279\u5f81\u5728\u591a\u5c11\u6bd4\u4f8b\u7684\u7ec6\u80de\u4e2d\u6ca1\u6709\u88ab\u68c0\u6d4b\u5230\u3002\u56fe\u4e2d\u7684\u7ea2\u8272\u70b9\u5c31\u662f\u6839\u636e\u6807\u51c6\u7b97\u6cd5\u9009\u62e9\u51fa\u6765\u7684\u7279\u5f81\uff0c\u800c\u84dd\u8272\u70b9\u662f\u88ab\u6392\u9664\u7684\u7279\u5f81\u3002<\/p>\n

#\u68c0\u67e5 scmap \u9009\u62e9\u7684\u524d 50 \u4e2a\u57fa\u56e0<\/p>\n

rownames(ref_sce)[which(SummarizedExperiment::rowData(ref_sce)$scmap_features)][1:50]<\/p>\n

\u5982\u56fe\uff0c\u662f\u6211\u4eec\u7b5b\u9009\u7684\u524d 50\u4e2a\u57fa\u56e0<\/p>\n

#\u901a\u8fc7\u68c0\u67e5\u57fa\u56e0\u540d\u79f0\u5411\u91cf\u7684\u957f\u5ea6\u6765\u68c0\u67e5\u9009\u62e9\u4e86\u591a\u5c11\u4e2a\u57fa\u56e0<\/p>\n

length(rownames(ref_sce)[which(SummarizedExperiment::rowData(ref_sce)$scmap_features)])<\/p>\n

<\/p>\n

\u73b0\u5728\u6211\u4eec\u53ef\u4ee5\u770b\u5230 scmap \u9009\u62e9\u4f7f\u7528\u7684\u57fa\u56e0\u3002 \u5982\u679c\u7f3a\u5c11\u5173\u952e\u6807\u8bb0\u57fa\u56e0\uff0c\u6211\u4eec\u53ef\u4ee5\u786e\u4fdd\u5c06\u5b83\u4eec\u5305\u542b\u5728\u5185\uff0c\u5982\u4e0b\u6240\u793a\uff1a<\/p>\n

#\u521b\u5efa\u8981\u4f7f\u7528\u7684\u5173\u952e\u6807\u8bb0\u7684\u5217<\/p>\n

my_key_markers = c(“TRAC”, “TRBC1”, “TRBC2”, “TRDC”, “TRGC1”, “TRGC2”, “IGKC”)<\/p>\n

# \u786e\u4fdd\u6807\u8bb0\u4f4d\u4e8e scmap \u4f7f\u7528\u7684\u529f\u80fd\u5217\u8868\u4e2d<\/p>\n

SummarizedExperiment::rowData(ref_sce)$scmap_features[rownames(ref_sce) %in% my_key_markers] <- TRUE<\/p>\n

# \u901a\u8fc7\u518d\u6b21\u68c0\u67e5\u57fa\u56e0\u540d\u79f0\u5411\u91cf\u7684\u957f\u5ea6\u6765\u68c0\u67e5\u662f\u5426\u6dfb\u52a0\u4e86\u4efb\u4f55\u57fa\u56e0\u957f\u5ea6<\/p>\n

length(rownames(ref_sce)[which(SummarizedExperiment::rowData(ref_sce)$scmap_features)])<\/p>\n

<\/p>\n

\u6211\u4eec\u53ef\u4ee5\u5220\u9664\u53ef\u80fd\u662f\u6280\u672f\u4ea7\u7269\u7684\u57fa\u56e0\uff0c\u4f8b\u5982\u7ebf\u7c92\u4f53 RNA\uff0c\u5982\u4e0b\u6240\u793a\uff1a<\/p>\n

# \u4ece\u6570\u636e\u96c6\u4e2d\u521b\u5efa\u7ebf\u7c92\u4f53\u57fa\u56e0\u5217\u8868\uff08\u4ee5\u201cMT\u201d\u5f00\u5934\u7684\u57fa\u56e0\uff09<\/p>\n

mt_genes <- rownames(ref_sce)[grep(“^MT-“, rownames(ref_sce))]<\/p>\n

# \u4ece scmap \u4f7f\u7528\u7684\u7279\u5f81\u4e2d\u5220\u9664\u8fd9\u4e9b\u57fa\u56e0<\/p>\n

SummarizedExperiment::rowData(ref_sce)$scmap_features[rownames(ref_sce) %in% mt_genes] <- FALSE<\/p>\n

# \u68c0\u67e5\u6709\u591a\u5c11\u4e2a\u57fa\u56e0<\/p>\n

length(rownames(ref_sce)[which(SummarizedExperiment::rowData(ref_sce)$scmap_features)])<\/p>\n

<\/p>\n

#\u63d0\u53d6\u7279\u5f81\u5e76\u5c06\u5176\u5206\u914d\u7ed9\u65b0\u53d8\u91cf\u201cscmap_feature_genes\u201d<\/p>\n

scmap_feature_genes <- rownames(ref_sce)[which(SummarizedExperiment::rowData(ref_sce)$scmap_features)]<\/p>\n

#\u57fa\u56e0\/\u7279\u5f81\u7684\u6570\u91cf\u4e0e\u6211\u4eec\u521a\u521a\u68c0\u67e5\u7684\u76f8\u540c<\/p>\n

length(scmap_feature_genes)<\/p>\n

<\/p>\n

\u73b0\u5728\u6211\u4eec\u6784\u5efa scmap-cluster \u4e2d\u4f7f\u7528\u7684\u53c2\u8003\u914d\u7f6e\u6587\u4ef6\uff0c\u7528\u4e8e\u57fa\u4e8e\u7c07\u7684\u7ec6\u80de\u7c7b\u578b\u6ce8\u91ca\u3002\u6211\u4eec \u53ef\u4ee5\u4ece SingleCellExperiment \u5bf9\u8c61\u5185\u90e8\u8bbf\u95ee\u548c\u7ed8\u5236\u8fd9\u4e9b\u914d\u7f6e\u6587\u4ef6\uff0c\u5982\u4e0b\u6240\u793a\uff1a<\/p>\n

# \u521b\u5efa\u53c2\u8003\u8d44\u6599\uff1b<\/p>\n

# \u751f\u6210\u53c2\u8003\u914d\u7f6e\u6587\u4ef6\u540e\uff0cscmap-cluster \u4e0d\u9700\u8981\u539f\u59cb\u6570\u636e<\/p>\n

ref_sce <- scmap::indexCluster(ref_sce)<\/p>\n

# \u5c06\u529f\u80fd\u53ef\u89c6\u5316\u4e3a\u70ed\u56fe<\/p>\n

# \u91cd\u65b0\u683c\u5f0f\u5316\u6570\u636e\uff0c\u4ee5\u4fbf\u5b83\u4eec\u53ef\u4ee5\u7528\u4f5c ggplot2 \u7684\u8f93\u5165<\/p>\n

cormat <- reshape2::melt(as.matrix(S4Vectors::metadata(ref_sce)$scmap_cluster_index))<\/p>\n

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

ggplot2::ggplot(cormat, ggplot2::aes(x = Var2, y = Var1, fill = value)) +<\/p>\n

ggplot2::geom_tile() +<\/p>\n

ggplot2::scale_fill_gradient2(low = “blue”, high = “darkred”,<\/p>\n

name = “Expression value”) +<\/p>\n

ggplot2::theme_minimal() +<\/p>\n

ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 1,<\/p>\n

size = 18, hjust = 1),<\/p>\n

axis.text.y = ggplot2::element_text(size = 15),<\/p>\n

axis.title.x = ggplot2::element_blank(),<\/p>\n

axis.title.y = ggplot2::element_blank())<\/p>\n

<\/p>\n

\u7ed3\u679c\u5982\u56fe\u6240\u793a\uff0c\u8fd9\u4e2a\u56fe\u7247\u663e\u793a\u4e86\u4e0d\u540c\u7c7b\u578b\u7684\u7ec6\u80de\u4e0e\u7279\u5b9a\u57fa\u56e0\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u989c\u8272\u7684\u6df1\u6d45\u8868\u793a\u5173\u8054\u7a0b\u5ea6\u6216\u6d3b\u52a8\u6c34\u5e73\u7684\u9ad8\u4f4e\u3002\u5c0f\u679c\u8fd9\u91cc\u53ea\u622a\u53d6\u4e86\u4e00\u90e8\u5206\uff0c\u5b8c\u6574\u7684\u56fe\u7247\u5927\u5bb6\u53ef\u4ee5\u81ea\u5df1\u8bd5\u7740\u7ed8\u5236\u54e6~<\/p>\n

\u63a5\u4e0b\u6765\u6211\u4eec\u7528scmap-cell \u6765\u6ce8\u91ca\u6570\u636e\u96c6\u4e2d\u7684\u5355\u4e2a\u7ec6\u80de\u3002<\/p>\n

# \u5c06\u8868\u8fbe\u5f0f\u4fe1\u606f\u5b58\u50a8\u4e3a\u53d8\u91cf<\/p>\n

scmap_cluster_reference <- S4Vectors::metadata(ref_sce)$scmap_cluster_index<\/p>\n

# \u66f4\u65b0\u4e4b\u524d\u7684\u5f15\u7528\u4ee5\u4e5f\u5305\u542b scmap-cell \u5f15\u7528<\/p>\n

ref_sce <- scmap::indexCell(ref_sce)<\/p>\n

#\u4ece\u5f15\u7528\u4e2d\u63d0\u53d6 scmap \u7d22\u5f15\u5e76\u5c06\u5176\u5b58\u50a8\u4e3a\u53d8\u91cf<\/p>\n

scmap_cell_reference <- S4Vectors::metadata(ref_sce)$scmap_cell_index<\/p>\n

# \u4ece\u5f15\u7528\u4e2d\u63d0\u53d6\u5173\u8054\u7684\u7ec6\u80de ID \u5e76\u4fdd\u5b58\u4e3a\u53d8\u91cf<\/p>\n

scmap_cell_metadata <- SummarizedExperiment::colData(ref_sce)<\/p>\n

\u7136\u540e\u5c06\u67e5\u8be2\u6570\u636e\u96c6\u4e2d\u7684\u7ec6\u80de\u5206\u914d\u7ed9\u53c2\u8003\u6570\u636e\u96c6\uff0c\u5229\u7528\u4e00\u4e2a\u5df2\u6709\u7684\u53c2\u8003\u6570\u636e\u96c6\u6765\u5bf9\u4e00\u4e2a\u65b0\u7684\u67e5\u8be2\u6570\u636e\u96c6\u8fdb\u884c\u6ce8\u91ca\u6216\u6620\u5c04\u3002\u6211\u4eec\u5c06\u4f7f\u7528\u7684\u67e5\u8be2\u6570\u636e\u96c6\u7531 10X genomics \u63d0\u4f9b\uff0c\u5927\u5bb6\u53ef\u4ee5\u4ece\u5c0f\u679c\u7ed9\u5927\u5bb6\u63d0\u4f9b\u7684\u94fe\u63a5\u4e0b\u8f7d~<\/p>\n

data <- Seurat::Read10X(“..\/data\/hg19\/”)<\/p>\n

# \u4ece\u539f\u59cb\u77e9\u9635\u4e2d\u8fdb\u884c SingleCellExperiment<\/p>\n

query_sce <- SingleCellExperiment::SingleCellExperiment(assays=list(counts=data))<\/p>\n

# \u4ece Seurat \u5bf9\u8c61\u5236\u4f5c SingleCellExperiment<\/p>\n

query_seur <- Seurat::CreateSeuratObject(data)<\/p>\n

query_sce <- Seurat::as.SingleCellExperiment(query_seur)<\/p>\n

# \u4f7f\u7528 scatter \u5305\u6807\u51c6\u5316\u6570\u636e<\/p>\n

query_sce <- scater::logNormCounts(query_sce)<\/p>\n

# \u6dfb\u52a0feature_symbol\u5217\uff08\u5373\u57fa\u56e0\u7b26\u53f7\uff09<\/p>\n

SummarizedExperiment::rowData(query_sce)$feature_symbol <- rownames(query_sce)<\/p>\n

\u73b0\u5728\uff0c\u6211\u4eec\u5df2\u51c6\u5907\u597d\u4f7f\u7528 scmap-cluster \u5bf9\u6570\u636e\u8fdb\u884c\u6ce8\u91ca\u3002<\/p>\n

# \u8fd0\u884c scmapCluster<\/p>\n

scmap_cluster_res <- scmap::scmapCluster(projection=query_sce,<\/p>\n

index_list=list(immune1 = scmap_cluster_reference),<\/p>\n

threshold=0.1)<\/p>\n

# \u7ed8\u5236\u6ce8\u91ca\u7ed3\u679c<\/p>\n

par(mar=c(13, 4, 1, 0))<\/p>\n

barplot(table(scmap_cluster_res$combined_labs), las=2)<\/p>\n

<\/p>\n

\u4e0a\u56fe\u5c31\u662f\u6211\u4eec\u8fdb\u884c\u6ce8\u91ca\u7684\u7ed3\u679c\u5566\uff0c\u53ef\u4ee5\u770b\u5230Monocytes CD14+\u7ec6\u80de\u7684\u6570\u91cf\u662f\u6700\u591a\u7684\u3002<\/p>\n

# \u5c06\u6b64\u6ce8\u91ca\u4fe1\u606f\u5b58\u50a8\u5728\u67e5\u8be2\u5bf9\u8c61\u4e2d<\/p>\n

SummarizedExperiment::colData(query_sce)$scmap_cluster <- scmap_cluster_res$combined_labs<\/p>\n

# \u5236\u4f5c\u5355\u5143\u683c\u7684 UMAP\uff0c\u5e76\u4f7f\u7528 scmapCluster \u4e2d\u7684\u5355\u5143\u683c\u7c7b\u578b\u6ce8\u91ca\u8fdb\u884c\u6807\u8bb0<\/p>\n

query_sce <- scater::runUMAP(query_sce)<\/p>\n

scater::plotReducedDim(query_sce, dimred=”UMAP”, colour_by=”scmap_cluster”)<\/p>\n

<\/p>\n

\u7ed3\u679c\u5982\u56fe\uff0c\u5c55\u793a\u4e86\u4e0d\u540c\u7c7b\u578b\u7684\u7ec6\u80de\u5728\u4e8c\u7ef4\u7a7a\u95f4\u4e2d\u7684\u5206\u5e03\u548c\u805a\u7c7b\u3002\u6bcf\u79cd\u989c\u8272\u4ee3\u8868\u4e00\u79cd\u7279\u5b9a\u7c7b\u578b\u7684\u7ec6\u80de\uff0c\u4f8b\u5982B\u7ec6\u80de\u3001T\u7ec6\u80de\u7b49\u3002<\/p>\n

\u9664\u6b64\u4e4b\u5916\uff0c\u6211\u4eec\u8fd8\u53ef\u4ee5\u7528scmap_cell\uff0csingleR\uff0cHarmony_lab\u7b49\u65b9\u5f0f\u8fdb\u884c\u81ea\u52a8\u6ce8\u91ca~<\/p>\n

Step 3 \u7ec6\u5316\u6ce8\u91ca<\/p>\n

\u5728\u8fd9\u91cc\u6211\u4eec\u4f7f\u7528\u8de8\u5de5\u5177\u7684\u6700\u5e38\u89c1\u6807\u7b7e\u6765\u5206\u914d\u6700\u7ec8\u7684\u81ea\u52a8\u6ce8\u91ca\u6807\u7b7e\u3002<\/p>\n

annotation_columns <- c(“scmap_cluster”, “scmap_cell”, “singleR”, “<\/a>Harmony_lab”)<\/p>\n

get_consensus_label <- function(labels){<\/p>\n

labels <- labels[labels != “ambiguous”]<\/p>\n

if (length(labels) == 0) {return(“ambiguous”)}<\/p>\n

freq <- table(labels)<\/p>\n

label <- names(freq)[which(freq == max(freq))]<\/p>\n

if (length(label) > 1) {return(“ambiguous”)}<\/p>\n

return(label)<\/p>\n

}<\/p>\n

SummarizedExperiment::colData(query_sce)$consensus_lab <- apply(SummarizedExperiment::colData(query_sce)[,annotation_columns], 1, get_consensus_label)<\/p>\n

scater::plotReducedDim(query_sce, dimred=”UMAP”, colour_by=”consensus_lab”)<\/p>\n

<\/p>\n

\u7ec6\u5316\u7684\u7ed3\u679c\u5982\u56fe\uff0c\u53ef\u4ee5\u4ece\u4fa7\u8fb9\u7684\u6807\u6ce8\u770b\u51fa\u6765\u660e\u663e\u6bd4\u4e4b\u524d\u7684\u5206\u7c7b\u66f4\u52a0\u7ec6\u81f4\u4e86\u3002<\/p>\n

Step 4 \u624b\u52a8\u6ce8\u91ca<\/p>\n

\u9996\u5148\u6211\u4eec\u9700\u8981\u68c0\u7d22\u6807\u8bb0\u57fa\u56e0\uff0c\u5982\u679c\u6ca1\u6709\u6bcf\u79cd\u7ec6\u80de\u7c7b\u578b\u7684\u6807\u8bb0\u7269\u7684\u5217\u8868\uff0c\u4e5f\u6ca1\u6709\u9ad8\u8d28\u91cf\u7684\u53c2\u8003\u6570\u636e\u96c6\u7684\u8bdd\uff0c\u53ef\u4ee5\u4ece\u67e5\u8be2\u6570\u636e\u7684\u6bcf\u4e2a\u805a\u7c7b\u4e2d\u63d0\u53d6\u9876\u7ea7\u6807\u8bb0\u57fa\u56e0\u3002\u8fd9\u91cc\u6211\u4eec\u4f7f\u7528 Seurat\u6765\u5b8c\u6210\uff0c\u5c06\u6570\u636e\u683c\u5f0f\u5316\u4e3aSeurat \u5bf9\u8c61\u3002\u5148\u5bf9\u6570\u636e\u8fdb\u884c\u5f52\u4e00\u5316\u548c\u7f29\u653e\uff0c\u5e76\u4e14\u786e\u5b9a\u7ec6\u80de\u4e4b\u95f4\u7684\u53ef\u53d8\u57fa\u56e0\u3002<\/p>\n

# \u4f7f\u7528 Seurat \u7684 SCTransform \u51fd\u6570\u7f29\u653e\u548c\u6807\u51c6\u5316\u6570\u636e<\/p>\n

query_seur <- Seurat::SCTransform(query_seur)<\/p>\n

#\u63a5\u4e0b\u6765\uff0c\u5bf9\u6570\u636e\u6267\u884c\u4e0d\u540c\u7c7b\u578b\u7684\u964d\u7ef4\uff0c\u4ee5\u4fbf\u53ef\u4ee5\u5728\u4e8c\u7ef4\u7a7a\u95f4\u4e2d\u5c06\u5355\u5143\u683c\u7ec4\u5408\u5728\u4e00\u8d77\u3002<\/p>\n

query_seur <- Seurat::RunPCA(query_seur)<\/p>\n

query_seur <- Seurat::RunTSNE(query_seur)<\/p>\n

query_seur <- Seurat::RunUMAP(query_seur, dims = 1:50)<\/p>\n

#\u4ee5\u9009\u5b9a\u7684\u5206\u8fa8\u7387\u5bf9\u6570\u636e\u8fdb\u884c\u805a\u7c7b<\/p>\n

# \u786e\u5b9a\u6bcf\u4e2a\u5355\u5143\u683c\u7684\u201c\u6700\u8fd1\u90bb\u5c45\u201d<\/p>\n

query_seur <- Seurat::FindNeighbors(query_seur, dims = 1:50)<\/p>\n

# \u805a\u7c7b<\/p>\n

query_seur <- Seurat::FindClusters(query_seur, resolution = 0.5)<\/p>\n

#\u53ef\u89c6\u5316<\/p>\n

Seurat::DimPlot(query_seur, reduction = “UMAP”)<\/p>\n

<\/p>\n

\u5982\u56fe\uff0c\u662f\u8fd8\u6ca1\u6709\u63d0\u53d6\u6807\u8bb0\u57fa\u56e0\u7684\u521d\u6b65\u7ed8\u5236\u7684\u70ed\u56fe\uff0c\u63a5\u4e0b\u6765\u6211\u4eec\u63d0\u53d6\u9876\u90e8\u57fa\u56e0\u3002<\/p>\n

#\u4f7f\u7528 Seurat \u4e2d\u7684 FindAllMarkers \u51fd\u6570\u6765\u5b8c\u6210<\/p>\n

markers_seur <- Seurat::FindAllMarkers(query_seur, only.pos = TRUE)<\/p>\n

# \u67e5\u770b\u6807\u8bb0\uff0c\u5b83\u4eec\u6309\u7167\u6bcf\u4e2a\u7c07\u7684 p \u503c\u4ece\u6700\u4f4e\u5230\u6700\u9ad8\u7684\u987a\u5e8f\u6392\u5217<\/p>\n

markers_seur<\/p>\n

<\/p>\n

\u7ed3\u679c\u5982\u56fe\uff0c\u662f\u6211\u4eec\u6311\u9009\u7684\u6807\u8bb0\u57fa\u56e0\u3002\u4e0d\u540c\u7ec6\u80de\u7c07\u4e4b\u95f4\u7684\u6807\u8bb0\u57fa\u56e0\u7684\u8868\u8fbe\u901a\u5e38\u7528\u70b9\u56fe\u6216\u70ed\u56fe\u6765\u5c55\u793a\u3002\u70b9\u56fe\u663e\u793a\u4e86\u6bcf\u4e2a\u7ec6\u80de\u7c07\u4e2d\u8868\u8fbe\u67d0\u4e2a\u6807\u8bb0\u57fa\u56e0\u7684\u7ec6\u80de\u7684\u767e\u5206\u6bd4\uff08\u70b9\u7684\u5927\u5c0f\uff09\u548c\u8be5\u7ec6\u80de\u7c07\u7684\u5e73\u5747\u68c0\u6d4b\u5230\u7684\u57fa\u56e0\u8868\u8fbe\u6c34\u5e73\u3002\u70ed\u56fe\u663e\u793a\u4e86\u4e0d\u540c\u7ec6\u80de\u7c07\u7684\u5e73\u5747\u6807\u8bb0\u57fa\u56e0\u8868\u8fbe\u6c34\u5e73\u3002\u8fd9\u4e24\u79cd\u56fe\u90fd\u662f\u7528Seurat\u521b\u5efa\u7684\u3002<\/p>\n

require(dplyr)<\/p>\n

# \u68c0\u7d22\u6bcf\u4e2a\u7c07\u7684\u524d 5 \u4e2a\u6807\u8bb0\u57fa\u56e0<\/p>\n

# \u4f7f\u7528 AVG_LOG \u5217\u4e0b\u5177\u6709\u6700\u9ad8\u503c\u7684\u57fa\u56e0<\/p>\n

top5 <- markers_seur %>% group_by(cluster) %>%<\/p>\n

dplyr::slice_max(get(grep(“^avg_log”, colnames(markers_seur), value = TRUE)),<\/p>\n

n = 5)<\/p>\n

# \u7ed8\u5236\u70b9\u56fe<\/p>\n

Seurat::DotPlot(query_seur, features = unique(top5$gene)) +<\/p>\n

ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 1,<\/p>\n

size = 8, hjust = 1)) +<\/p>\n

Seurat::NoLegend()<\/p>\n

<\/p>\n

\u5982\u56fe\u6240\u793a\uff0c\u6e05\u6670\u7684\u663e\u793a\u4e86\u6bcf\u4e2a\u7ec6\u80de\u7c07\u4e2d\u8868\u8fbe\u67d0\u4e2a\u6807\u8bb0\u57fa\u56e0\u7684\u7ec6\u80de\u7684\u767e\u5206\u6bd4\uff08\u70b9\u7684\u5927\u5c0f\uff09\u548c\u8be5\u7ec6\u80de\u7c07\u7684\u5e73\u5747\u68c0\u6d4b\u5230\u7684\u57fa\u56e0\u8868\u8fbe\u6c34\u5e73\u3002<\/p>\n

# \u7ed8\u5236\u70ed\u56fe<\/p>\n

Seurat::DoHeatmap(query_seur, features = unique(top5$gene)) +<\/p>\n

Seurat::NoLegend() +<\/p>\n

ggplot2::theme(axis.text.y = ggplot2::element_text(size = 8))<\/p>\n

<\/p>\n

\u5982\u56fe\u6240\u793a\uff0c\u663e\u793a\u4e86\u4e0d\u540c\u7ec6\u80de\u7c07\u7684\u5e73\u5747\u6807\u8bb0\u57fa\u56e0\u8868\u8fbe\u6c34\u5e73\u3002<\/p>\n

\u901a\u8fc7\u4e0a\u8ff0\u7684\u5de5\u5177\u548c\u65b9\u6cd5\u53ef\u4ee5\u4e3ascRNA-seq\u6570\u636e\u63d0\u4f9b\u53ef\u9760\u7684\u7ec6\u80de\u7c7b\u578b\u6807\u8bb0\u3002\u7136\u800c\uff0c\u7531\u4e8e\u7b97\u6cd5\u7684\u6d6e\u52a8\u4ee5\u53ca\u6807\u8bb0\u7684\u6a21\u7cca\u6027\uff0c\u4f7f\u7528\u5176\u4ed6\u7684\u65b9\u6cd5(\u4f8b\u5982\u7edf\u8ba1\u65b9\u6cd5)\u6216\u8fdb\u884c\u5b9e\u9a8c\u6765\u786e\u8ba4\u5355\u5143\u683c\u6ce8\u91ca\u6807\u7b7e\u662f\u975e\u5e38\u91cd\u8981\u7684\u3002\u6b64\u5916\uff0c\u7531\u4e8emRNA\u6d4b\u91cf\u53ea\u80fd\u90e8\u5206\u5b9a\u4e49\u7ec6\u80de\u7c7b\u578b\u548c\u529f\u80fd\uff0c\u56e0\u6b64\u5173\u4e8e\u65b0\u7ec6\u80de\u7c7b\u578b\u7684\u91cd\u8981\u7ed3\u8bba\u5fc5\u987b\u7ecf\u8fc7\u5b9e\u9a8c\u9a8c\u8bc1\u3002<\/p>\n

\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528T\u7ec6\u80de\u53d7\u4f53(TCR)\u548cB\u7ec6\u80de\u53d7\u4f53\u514b\u9686\u5206\u578b\u6765\u6539\u8fdb\u7ec4\u7ec7\u9a7b\u7559\u514d\u75ab\u7ec6\u80de\u7684\u7ec6\u80de\u7c7b\u578b\u6807\u8bb0\uff0c\u4ee5\u68c0\u67e5T\u7ec6\u80de\u548cB\u7ec6\u80de\u7684\u8f6c\u5f55\u7279\u5f81\uff0c\u5206\u522b\u7531\u5b83\u4eec\u8868\u8fbe\u7684TCR\u548cB\u7ec6\u80de\u53d7\u4f53\u5206\u5c42\u3002<\/p>\n

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