{"id":25831,"date":"2024-01-23T17:30:06","date_gmt":"2024-01-23T09:30:06","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=25831"},"modified":"2024-01-23T17:30:07","modified_gmt":"2024-01-23T09:30:07","slug":"%e5%af%b9%e5%8d%95%e7%bb%86%e8%83%9e%e5%88%86%e6%9e%90%e6%af%ab%e6%97%a0%e5%a4%b4%e7%bb%aa%ef%bc%9f%e8%ae%a9popsicler%e9%a2%86%e4%bd%a0%e5%85%a5%e9%97%a8","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=25831","title":{"rendered":"\u5bf9\u5355\u7ec6\u80de\u5206\u6790\u6beb\u65e0\u5934\u7eea\uff1f\u8ba9popsicleR\u9886\u4f60\u5165\u95e8"},"content":{"rendered":"

\u4e0d\u77e5\u9053\u6709\u591a\u5c11\u521a\u5f00\u59cb\u63a5\u89e6\u5355\u7ec6\u80de\u6d4b\u5e8f\u5206\u6790\u7684\u5c0f\u4f19\u4f34\u5728\u9762\u5bf9\u65e0\u7a77\u65e0\u5c3d\u7684R\u5305\u548c\u4ee3\u7801\u65f6\u611f\u5230\u754f\u96be\uff0c\u4eff\u4f5b\u4e00\u5806\u77e5\u8bc6\u8981\u5f3a\u884c\u6d8c\u5165\u6211\u4eec\u7684\u8111\u6d77\u3002\u4eca\u5929\u5c0f\u679c\u60f3\u5411\u5927\u5bb6\u4ecb\u7ecd\u4e00\u4e2aR\u5305\u2014\u2014popsicleR\u3002\u8be5R\u5305\u7684\u4f5c\u8005\u6574\u5408\u4e86\u5355\u7ec6\u80de\u8f6c\u5f55\u7ec4\u5206\u6790\u6240\u9700\u7684R\u5305\uff0c\u7528\u4e03\u884c\u4ee3\u7801\u5c31\u53ef\u4ee5\u5b8c\u6210\u4ece\u8d28\u63a7\u5230\u7ec6\u80de\u6ce8\u91ca\u7684\u6574\u5957\u6d41\u7a0b\u3002\u5b66\u4e60\u4f7f\u7528\u8be5R\u5305\u53ef\u4ee5\u4e3a\u521d\u5b66\u8005\u6784\u5efa\u601d\u7ef4\u6846\u67b6\uff0c\u4ece\u800c\u4f7f\u5b66\u4e60\u8fc7\u7a0b\u66f4\u52a0\u7b80\u5355\u3002<\/p>\n

\u4e0b\u9762\uff0c\u5c0f\u679c\u5c06\u4e3a\u5927\u5bb6\u8be6\u7ec6\u4ecb\u7ecd\u4e00\u4e0bpopsicle\u3002\u5b83\u7ed3\u5408\u4e86\u5e7f\u6cdb\u4f7f\u7528\u7684\u6d41\u7a0b\u4e2d\u5b9e\u73b0\u7684\u65b9\u6cd5\uff0c\u4ee5\u4ea4\u4e92\u65b9\u5f0f\u6267\u884c scRNA-seq \u6570\u636e\u5206\u6790\u7684\u6240\u6709\u4e3b\u8981\u9884\u5904\u7406\u548c QC \u6b65\u9aa4\u3002\u8be5\u8f6f\u4ef6\u5305\u7531\u4e03\u4e2a\u529f\u80fd\u7ec4\u4ef6\u7ec4\u6210\uff1a<\/p>\n

Step 1\uff1a\u6267\u884c\u8d28\u91cf\u63a7\u5236\u6307\u6807\u7684\u63a2\u7d22<\/strong><\/p>\n

Step 2\uff1a\u7ec6\u80de\u7b5b\u9009<\/strong><\/p>\n

Step 3\uff1a\u53cc\u7ec6\u80de\u68c0\u6d4b<\/strong><\/p>\n

Step 4\uff1a\u6570\u636e\u6807\u51c6\u5316<\/strong><\/p>\n

Step 5\uff1a\u7f29\u653e\u53ca\u56de\u5f52<\/strong><\/p>\n

Step 6\uff1a\u7ec6\u80de\u805a\u7c7b<\/strong><\/p>\n

Step 7\uff1a\u7ec6\u80de\u6ce8\u91ca<\/strong><\/p>\n

<\/p>\n

\u5728\u5206\u6790\u7684\u6bcf\u4e2a\u6b65\u9aa4\u4e2d\uff0cpopsicleR \u901a\u8fc7\u5f69\u8272\u6587\u672c\u6d88\u606f\u4ea4\u4e92\u5f0f\u5730\u6307\u5bfc\u7528\u6237\uff0c\u5e76\u5728\u4e13\u7528\u6587\u4ef6\u5939\u4e2d\u4fdd\u5b58\u5404\u79cd\u56fe\uff0c\u4ee5\u7814\u7a76\u591a\u4e2a QC \u6307\u6807\u5e76\u8bc4\u4f30\u8fc7\u6ee4\u548c\u56de\u5f52\u53c2\u6570\u5bf9\u7ec6\u80de\u7fa4\u8bc6\u522b\u548c\u5206\u7c7b\u7684\u5f71\u54cd\u3002\u8dd1\u5b8c\u6d41\u7a0b\u540e\u53ef\u4e3a\u5c0f\u4f19\u4f34\u4eec\u4ea7\u51fa20\u591a\u5f20\u5404\u5f0f\u5404\u6837\u4e14\u7f8e\u89c2\u7684\u56fe\u8868\u3002<\/p>\n

\u5c0f\u679c\u8fd9\u8fb9\u5df2\u7ecf\u4e3a\u5c0f\u4f19\u4f34\u4eec\u51c6\u5907\u597d\u8f93\u5165\u6587\u4ef6\u4e86\uff0c\u83b7\u53d6\u94fe\u63a5\u653e\u5728\u6587\u7ae0\u7684\u6700\u540e\u54e6~\u5c31\u8ba9\u6211\u4eec\u6b63\u5f0f\u5f00\u59cb\u4eca\u5929\u7684\u5b66\u4e60\u5427\uff01<\/p>\n

# Step 0 \u5b89\u88c5popsicleR\u5305\u53ca\u5176\u4f9d\u8d56<\/p>\n

\u56e0\u4e3apopsicleR\u6574\u5408\u5927\u91cf\u7684R\u5305\uff0c\u56e0\u6b64\u5728\u5b89\u88c5\u4f9d\u8d56\u7684\u8fc7\u7a0b\u4e2d\u53ef\u80fd\u6709\u4e00\u4e9b\u7e41\u7410\u54e6\uff0c\u4e0b\u9762\u7684\u4ee3\u7801\u867d\u7136\u5f88\u591a\uff0c\u4f46\u53ea\u9700\u8981\u5c0f\u4f19\u4f34\u4eec\u76f4\u63a5\u8fd0\u884c\u5c31\u53ef\u4ee5\u5566\u3002<\/p>\n

options(timeout = 999) # \u65b9\u4fbf\u5728GitHub\u4e0a\u4e0b\u8f7d\u5bf9\u5e94\u7684R\u5305<\/p>\n

CRANdep <- c(“Seurat”,”reticulate”,”R.utils”,”dplyr”,”ggplot2″,”clustree”,”ape”,”gtools”,<\/p>\n

“future”,”grid”,”gridExtra”,”magrittr”,”limma”,”patchwork”,<\/p>\n

“crayon”,”ggExtra”,”RColorBrewer”,”ggplotify”,”RANN”,”umap”,<\/p>\n

“celldex”,”curl”,”httr”,”lattice”,”shinythemes”,”usethis”,”rcmdcheck”,<\/p>\n

“roxygen2″,”rversions”,”devtools”,”pheatmap”,”BiocManager”,”corrplot”)<\/p>\n

newPackages <- CRANdep[!(CRANdep %in% installed.packages()[,”Package”])]<\/p>\n

if(length(newPackages)){install.packages(newPackages)}<\/p>\n

CRANarcdep <- c(“Matrix”,”optimbase”,”optimsimplex”,”neldermead”,”session”)<\/p>\n

newPackages <- CRANarcdep[!(CRANarcdep %in% installed.packages()[,”Package”])]<\/p>\n

if(length(newPackages)){<\/p>\n

packagesurl <- c(“https:\/\/cran.r-project.org\/src\/contrib\/Archive\/Matrix\/Matrix_1.3-2.tar.gz”,<\/p>\n

“https:\/\/cran.r-project.org\/src\/contrib\/Archive\/optimbase\/optimbase_1.0-9.tar.gz”,<\/p>\n

“https:\/\/cran.r-project.org\/src\/contrib\/Archive\/optimsimplex\/optimsimplex_1.0-7.tar.gz”,<\/p>\n

“https:\/\/cran.r-project.org\/src\/contrib\/Archive\/neldermead\/neldermead_1.0-11.tar.gz”,<\/p>\n

“https:\/\/cran.r-project.org\/src\/contrib\/Archive\/session\/session_1.0.3.tar.gz”)<\/p>\n

for (i in 1:length(newPackages)){<\/p>\n

source_repo <- packagesurl[grep(newPackages[i], packagesurl)]<\/p>\n

install.packages(source_repo, repos=NULL, type=”source”)<\/p>\n

}<\/p>\n

}<\/p>\n

BioCdep <- c(“SingleR”,”limma”,”BiocFileCache”,”AnnotationHub”,<\/p>\n

“ExperimentHub”,”celldex”,”scDblFinder”)<\/p>\n

newPackages <- BioCdep[!(BioCdep %in% installed.packages()[,”Package”])]<\/p>\n

if(length(newPackages)){BiocManager::install(newPackages)}<\/p>\n

if(!”scMCA”%in% installed.packages()[,”Package”]){devtools::install_github(“ggjlab\/scMCA”)}<\/p>\n

devtools::install_github(“bicciatolab\/popsicleR”)<\/p>\n

library(popsicleR)<\/p>\n

\u8f7d\u5165R\u5305\u540e\u51fa\u73b0<\/p>\n

<\/p>\n

\u5373\u4e3a\u5b89\u88c5\u6210\u529f\uff0c\u6211\u4eec\u4fbf\u53ef\u4ee5\u8fdb\u884c\u4e0b\u4e00\u6b65\u5566<\/p>\n

# step 1 \u4ece\u539f\u59cb\u6570\u636e\u521b\u5efa Seurat \u5bf9\u8c61\u5e76\u53ef\u89c6\u5316 QC \u6307\u6807<\/p>\n

seurat_obj <- PrePlots(sample = ‘CRR073027’,<\/p>\n

input_data = ‘filtered_feature_bc_matrix\/’,<\/p>\n

percentage = 0.1, gene_filter = 200, cellranger = TRUE, organism = “human”)<\/p>\n

@para sample: \u7814\u7a76\u9879\u76ee\u7684name\uff1b<\/p>\n

@para input_data: \u8f93\u5165\u6587\u4ef6\u7684\u8def\u5f84\uff0c\u5728\u8fd9\u9762\u5c0f\u4f19\u4f34\u4eec\u65e2\u53ef\u4ee5\u4f7f\u7528\u5c0f\u679c\u4e3a\u5927\u5bb6\u51c6\u5907\u597d\u7684cellranger\u7684\u77e9\u9635\u7ed3\u679c\uff0c\u4e5f\u53ef\u4ee5\u662f\u975ecellranger\u7684\u666e\u901a\u8ba1\u6570\u77e9\u9635\uff0c\u90fd\u662f\u517c\u5bb9\u7684\u54e6<\/p>\n

@para genelist: \u5728\u8fd9\u91cc\u5982\u679c\u5c0f\u4f19\u4f34\u4eec\u6709\u5c5e\u4e8e\u81ea\u5df1\u7684\u4e00\u5957\u4e0e\u7ec6\u80de\u5bf9\u5e94\u7684marker\u5217\u8868\u4e5f\u53ef\u4ee5\u4f7f\u7528\u5411\u91cf\u7684\u5f62\u5f0f\u8fdb\u884c\u6dfb\u52a0\u54e6\uff0c\u5c0f\u767d\u6ca1\u6709\u7684\u8bdd\uff0c\u4e5f\u6ca1\u6709\u5173\u7cfb\uff0c\u4f5c\u8005\u4e3a\u5927\u5bb6\u51c6\u5907\u597d\u4e86\u4e00\u5957marker\u3002<\/p>\n

@para percentage: \u8fd9\u91cc\u9650\u5236\u4e86\u6240\u6709\u7684\u57fa\u56e0\u8d77\u7801\u57281%\u7684\u7ec6\u80de\u4e2d\u8fdb\u884c\u8868\u8fbe\uff0c\u9ed8\u8ba4\u503c\u4e3a0.1<\/p>\n

@para gene_filter: \u9650\u5236\u6240\u6709\u7684\u7ec6\u80de\u6700\u8d77\u7801\u68c0\u6d4b\u51fa200\u4e2a\u57fa\u56e0<\/p>\n

@cellranger: \u82e5\u8f93\u5165\u6587\u4ef6\u4e3acellranger\u7684\u8f93\u51fa\u6587\u4ef6\u5219\u8bbe\u5b9a\u4e3aTRUE<\/p>\n

@organism: \u9009\u62e9\u7814\u7a76\u7684\u7269\u79cd<\/p>\n

\u4e0b\u9762\u5c0f\u679c\u4e3a\u5927\u5bb6\u7b5b\u9009\u4e86\u8fd0\u884c\u5b8c\u8d28\u63a7\u7ed3\u679c\u540e\u7684\u6570\u5f20\u7ed3\u679c\u56fe\u7ed9\u5404\u4f4d\u5c0f\u4f19\u4f34\u9884\u89c8\u4e00\u4e0b<\/p>\n

<\/p>\n

<\/p>\n

# step 2 \u53cc\u91cd\u7ec6\u80de\u68c0\u6d4b<\/p>\n

\u8be5\u5904\u4ee3\u7801\u7684\u8fd0\u884c\u65f6\u95f4\u4f1a\u8f83\u957f\uff0c\u6709\u6761\u4ef6\u7684\u5c0f\u4f19\u4f34\u5efa\u8bae\u4f7f\u7528\u670d\u52a1\u5668\u8fdb\u884c\u8fd0\u884c\u54e6~<\/p>\n

seurat_obj <- CalculateDoublets(UMI = seurat_obj, method = “scrublet”, dbs_thr =’none’, dbs_remove = FALSE)<\/p>\n

# step 3 \u53cc\u91cd\u7ec6\u80de\u79fb\u9664<\/p>\n

seurat_obj <- CalculateDoublets(UMI = seurat_obj, method = “scrublet”, dbs_thr = 0.22, dbs_remove = TRUE)<\/p>\n

# \u53c2\u6570\u8bf4\u660e:<\/p>\n

@para UMI: \u8f93\u5165\u7684\u5355\u7ec6\u80de\u8f6c\u5f55\u7ec4\u6570\u636e\u5bf9\u8c61<\/p>\n

@para method: \u53cc\u91cd\u7ec6\u80de\u68c0\u6d4b\u65b9\u6cd5\uff0c\u53ef\u4ee5\u9009\u62e9”scrublet”,”scDblFinder”\u5176\u4e2d\u4e00\u79cd\u65b9\u5f0f<\/p>\n

# \u6765\u81ea\u5c0f\u679c\u7684\u6e29\u99a8\u63d0\u793a\uff0cdbs_thr\u53c2\u6570\u662f\u5728\u9009\u62e9scrublet\u65b9\u6cd5\u7684\u60c5\u51b5\u4e0b\u624d\u8fdb\u884c\u9009\u62e9\u7684\u54e6<\/p>\n

@para dbs_thr: \u8bbe\u5b9a\u7684\u53cc\u91cd\u7ec6\u80de\u9608\u503c\uff0c\u8fd9\u91cc\u8bbe\u7f6e\u4e3a’none’<\/p>\n

@para dbs_thr: \u8bbe\u5b9a\u7684\u53cc\u91cd\u7ec6\u80de\u9608\u503c\uff0c\u8fd9\u91cc\u8bbe\u7f6e\u4e3a0.22<\/p>\n

@para dbs_remove: \u662f\u5426\u79fb\u9664\u53cc\u91cd\u7ec6\u80de<\/p>\n

# step 4\u6807\u51c6\u5316<\/p>\n

seurat_obj <- Normalize(UMI = seurat_obj, variable_genes = 2000)<\/p>\n

# \u53c2\u6570\u8bf4\u660e:<\/p>\n

# @para UMI: \u8f93\u5165\u7684\u5355\u7ec6\u80de\u8f6c\u5f55\u7ec4\u6570\u636e\u5bf9\u8c61<\/p>\n

# @para variable_genes: \u9009\u62e9\u7684\u53d8\u5f02\u57fa\u56e0\u6570\u91cf\uff0c\u8fd9\u91cc\u8bbe\u7f6e\u4e3a2000<\/p>\n

# step 5 \u56de\u5f52<\/p>\n

seurat_obj <- ApplyRegression(UMI = seurat_obj, organism = “human”, variables = “none”, explore_PC = FALSE)<\/p>\n

# step 6\u5bf9PC\u503c\u8fdb\u884c\u89c2\u6d4b\u540e\u8fdb\u884c\u56de\u5f52\u7814\u7a76<\/p>\n

seurat_obj <- ApplyRegression(UMI = seurat_obj, organism = “human”, variables = “none”, explore_PC = TRUE)<\/p>\n

# \u53c2\u6570\u8bf4\u660e:<\/p>\n

@para UMI: \u8f93\u5165\u7684\u5355\u7ec6\u80de\u8f6c\u5f55\u7ec4\u6570\u636e\u5bf9\u8c61<\/p>\n

@para organism: \u7814\u7a76\u7684\u7269\u79cd\uff0c\u8fd9\u91cc\u9009\u62e9\u4e86”human”<\/p>\n

@para variables: \u8fdb\u884c\u56de\u5f52\u5206\u6790\u7684\u53d8\u91cf\uff0c\u8fd9\u91cc\u8bbe\u7f6e\u4e3a”none”<\/p>\n

# \u5728\u6211\u4eec\u63a2\u7d22\u4e3b\u6210\u5206\u7684\u4e2a\u6570\u540e\u4fbf\u53ef\u5bf9\u8be5\u53c2\u6570\u8fdb\u884c\u8c03\u6574<\/p>\n

@para explore_PC: \u662f\u5426\u63a2\u7d22\u4e3b\u6210\u5206\uff0c\u8fd9\u91cc\u8bbe\u7f6e\u4e3aFALSE<\/p>\n

@para explore_PC: \u662f\u5426\u63a2\u7d22\u4e3b\u6210\u5206\uff0c\u8fd9\u91cc\u8bbe\u7f6e\u4e3aTRUE<\/p>\n

# step 7\u7ec6\u80de\u7c7b\u578b\u6ce8\u91ca<\/p>\n

seurat_obj <- MakeAnnotation(UMI = seurat_obj, organism = “human”, marker.list = “none”, cluster_res= 0.8)<\/p>\n

# \u53c2\u6570\u8bf4\u660e:<\/p>\n

@para UMI: \u8f93\u5165\u7684\u5355\u7ec6\u80de\u8f6c\u5f55\u7ec4\u6570\u636e\u5bf9\u8c61<\/p>\n

@para organism: \u7814\u7a76\u7684\u7269\u79cd\uff0c\u8fd9\u91cc\u9009\u62e9\u4e86”human”<\/p>\n

@para marker.list: \u7ec6\u80de\u7c7b\u578b\u6807\u8bb0\u57fa\u56e0\u5217\u8868\uff0c\u8fd9\u91cc\u8bbe\u7f6e\u4e3a”none”<\/p>\n

@para cluster_res: \u805a\u7c7b\u5206\u8fa8\u7387\uff0c\u8fd9\u91cc\u8bbe\u7f6e\u4e3a0.8<\/p>\n

\u53ef\u4ee5\u770b\u51fa\uff0c\u867d\u7136\u8fd9\u6bb5\u4ee3\u7801\u67097\u884c\uff0c\u4f46\u5b9e\u9645\u4e0a\u6709\u4e24\u884c\u4ee3\u7801\u662f\u4e3a\u4e86\u91cd\u590d\u8fd0\u884c\u4ee5\u9009\u62e9\u53c2\u6570\u7684\uff0c\u56e0\u6b64\u6211\u4eec\u53ef\u4ee5\u53ea\u75285\u884c\u4ee3\u7801\u5c31\u5b8c\u6210\u5355\u7ec6\u80de\u8f6c\u5f55\u7ec4\u5206\u6790\u3002<\/p>\n

popsicleR\u8fd9\u4e2aR\u5305\u7684\u6e90\u4ee3\u7801\u5c06\u8fd1\u67092000\u884c\uff0c\u611f\u5174\u8da3\u7684\u5c0f\u4f19\u4f34\u53ef\u4ee5\u8fdb\u5165\u5b98\u65b9\u6587\u6863\u8fdb\u884c\u5b66\u4e60\u54e6\uff08https:\/\/github.com\/bicciatolab\/popsicleR\/\uff09\uff0c\u8fd9\u4e0d\u4ec5\u4ec5\u662f\u5bf9\u5355\u7ec6\u80de\u8f6c\u5f55\u7ec4\u5206\u6790\u6d41\u7a0b\u7684\u5b66\u4e60\uff0c\u8fd8\u53ef\u4ee5\u901a\u8fc7\u9605\u8bfb\u6e90\u7801\u4e86\u89e3\u5230\u5404\u79cd\u5404\u6837\u7684\u8868\u683c\u7684\u7ed8\u5236\u4ee3\u7801\uff0c\u5728\u65e5\u540e\u7684\u7814\u7a76\u5b66\u4e60\u4e2d\u9887\u6709\u76ca\u5904\u3002<\/p>\n

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