{"id":27263,"date":"2024-01-31T16:53:36","date_gmt":"2024-01-31T08:53:36","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=27263"},"modified":"2024-01-31T16:53:37","modified_gmt":"2024-01-31T08:53:37","slug":"%e6%8b%9f%e6%97%b6%e5%ba%8f%e5%88%86%e6%9e%90%e4%b8%8d%e4%bc%9a%e5%81%9a%ef%bc%9fr%e5%8c%85monocle3%e6%9d%a5%e8%a7%a3%e5%86%b3","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=27263","title":{"rendered":"\u62df\u65f6\u5e8f\u5206\u6790\u4e0d\u4f1a\u505a\uff1fR\u5305Monocle3\u6765\u89e3\u51b3"},"content":{"rendered":"

\u4eca\u5929\u5c0f\u679c\u6765\u4e3a\u5927\u5bb6\u4ecb\u7ecd\u7ec6\u80de\u62df\u65f6\u5e8f\u5206\u6790\u4ee5\u53ca\u5982\u4f55\u4f7f\u7528monocle3\u505a\u7ec6\u80de\u62df\u65f6\u5e8f\u5206\u6790\u3002<\/p>\n

\u90a3\u4e48\u4ec0\u4e48\u662f\u7ec6\u80de\u62df\u65f6\u5e8f\u5206\u6790\u5462\uff1f<\/p>\n

\u5728\u751f\u547d\u4f53\u53d1\u80b2\u8fc7\u7a0b\u4e2d\uff0c\u7ec6\u80de\u4f1a\u5728\u7279\u5b9a\u6761\u4ef6\u4e0b\u8fdb\u884c\u5206\u5316\uff0c\u4ece\u4e00\u4e2a\u72b6\u6001\u8fc7\u6e21\u5230\u53e6\u4e00\u4e2a\u72b6\u6001\uff0c\u8868\u8fbe\u5b8c\u5168\u4e0d\u540c\u7684\u57fa\u56e0\u96c6\uff0c\u6709\u7684\u57fa\u56e0\u88ab\u6c89\u9ed8\uff0c\u800c\u6709\u7684\u57fa\u56e0\u88ab\u6fc0\u6d3b\u3002\u8fd9\u4e9b\u72b6\u6001\u901a\u5e38\u5f88\u96be\u8868\u5f81\uff0c\u56e0\u4e3a\u60f3\u8981\u7eaf\u5316\u4e2d\u95f4\u72b6\u6001\u7684\u7ec6\u80de\u5f88\u56f0\u96be\u6216\u4e0d\u53ef\u80fd\u3002\u4f46\u662f\u901a\u8fc7\u5355\u7ec6\u80de\u6d4b\u5e8f\u6280\u672f\u6355\u83b7\u4e00\u7fa4\u7ec6\u80de\u7684\u8f6c\u5f55\u5feb\u7167\uff0c\u53ef\u4ee5\u4f7f\u7528\u8f68\u8ff9\u63a8\u65ad\uff08trajectory inference\uff09\u7684\u65b9\u6cd5\uff0c\u5bf9\u6d4b\u5e8f\u7684\u7ec6\u80de\u6839\u636e\u8868\u8fbe\u6a21\u5f0f\u7684\u76f8\u4f3c\u6027\u8fdb\u884c\u6392\u5e8f\u3002\u7531\u6b64\u4e00\u6765\u5c31\u53ef\u4ee5\u6a21\u62df\u7ec6\u80de\u7684\u52a8\u6001\u53d8\u5316\u3002<\/p>\n

\u53ef\u80fd\u6709\u7684\u5c0f\u4f19\u4f34\u6709\u7591\u95ee\u4e86\uff0c\u4e3a\u4ec0\u4e48\u53eb\u505a\u4f2a\u65f6\u5e8f\u5206\u6790\u5462\uff1f<\/p>\n

\u5982\u679c\u60f3\u8981\u5f97\u5230\u7ec6\u80de\u5206\u5316\u8f68\u8ff9\uff0c\u5206\u5316\u7684\u65f6\u5e8f\u5206\u6790\uff0c\u7406\u8bba\u4e0a\u7684\u505a\u6cd5\u662f\u8bbe\u7f6e\u591a\u4e2a\u65f6\u95f4\u70b9\uff0c\u6bcf\u4e2a\u65f6\u95f4\u70b9\u53d6\u4e00\u6b21\u6837\uff0c\u8fdb\u884c\u6d4b\u5e8f\u4ee5\u53ca\u540e\u7eed\u7684\u5206\u6790\u3002\u4f46\u662f\u8fd9\u79cd\u65b9\u6cd5\u5f88\u96be\u505a\uff0c\u800c\u4e14\u8d39\u65f6\u8d39\u529b\u8d39\u94b1\u3002\u901a\u8fc7\u5355\u7ec6\u80de\u6d4b\u5e8f\u53ef\u4ee5\u4ece\u4e00\u7fa4\u7ec6\u80de\u4e2d\u5f97\u5230\u8f6c\u5f55\u77ac\u65f6\u5feb\u7167\uff0c\u53ef\u4ee5\u4ece\u4e2d\u63a8\u65ad\u51fa\u7ec6\u80de\u7684\u5206\u5316\u8f68\u8ff9\u3002\u8fd9\u79cd\u65b9\u6cd5\u56e0\u4e3a\u53ea\u53d6\u4e86\u4e00\u4e2a\u65f6\u95f4\u70b9\uff0c\u6240\u4ee5\u53eb\u505a\u4f2a\u65f6\u5e8f\u5206\u6790\u3002<\/p>\n

<\/p>\n

Monocle3\u662f\u505a\u4f2a\u65f6\u5e8f\u5206\u6790\u7684\u9996\u9009\u8f6f\u4ef6\u5305\u3002\u5176\u56e2\u961f\u53d1\u8868\u4e86\u4e00\u7bc7NATURE\u4e00\u7bc7NB\u4e24\u7bc7 NM\u3002\u7ecf\u8fc7\u4e24\u4e2a\u5927\u7248\u672c\u7684\u66f4\u65b0\uff0cmonocle3\u8fd8\u53ef\u4ee5\u505a\u7ec6\u80de\u7684\u964d\u7ef4\u548c\u5206\u7c7b\u7b49\u5206\u6790\u3002Monocle\u4f7f\u7528\u7b97\u6cd5\u6765\u5b66\u4e60\u6bcf\u4e2a\u7ec6\u80de\u4f5c\u4e3a\u52a8\u6001\u751f\u7269\u8fc7\u7a0b\u7684\u4e00\u90e8\u5206\u5fc5\u987b\u7ecf\u5386\u7684\u57fa\u56e0\u8868\u8fbe\u53d8\u5316\u5e8f\u5217\u3002\u4e00\u65e6\u4e86\u89e3\u4e86\u57fa\u56e0\u8868\u8fbe\u53d8\u5316\u7684\u603b\u4f53\u201c\u8f68\u8ff9\u201d\uff0cMonocle\u5c31\u53ef\u4ee5\u5c06\u6bcf\u4e2a\u7ec6\u80de\u653e\u7f6e\u5728\u8f68\u8ff9\u4e2d\u7684\u9002\u5f53\u4f4d\u7f6e\u3002\u7136\u540e\uff0c\u5c31\u53ef\u4ee5\u4f7f\u7528Monocle\u7684\u5dee\u5f02\u5206\u6790\u5de5\u5177\u5305\u6765\u5bfb\u627e\u5728\u8f68\u8ff9\u8fc7\u7a0b\u4e2d\u53d7\u8c03\u63a7\u7684\u57fa\u56e0\u3002<\/p>\n

\u63a5\u4e0b\u6765\u5c0f\u679c\u5e26\u5927\u5bb6\u7528monocle3\u6765\u505a\u4f2a\u65f6\u5e8f\u5206\u6790\u3002<\/p>\n

\u4e00\u3001\u5b89\u88c5monocle3<\/h2>\n

\u9996\u5148\u5b89\u88c5Bioconductor, \u6253\u5f00Rstudio\u5e76\u4e14\u8fd0\u884c<\/p>\n

if (!requireNamespace(“BiocManager”, quietly = TRUE))<\/p>\n

install.packages(“BiocManager”)<\/p>\n

BiocManager::install(version = “3.14”)<\/p>\n

\u63a5\u7740\u5b89\u88c5\u4e00\u4e9bBioconductor\u4e0d\u4f1a\u81ea\u52a8\u5b89\u88c5\u7684\u4f9d\u8d56<\/p>\n

BiocManager::install(c(‘BiocGenerics’, ‘DelayedArray’, ‘DelayedMatrixStats’,<\/p>\n

‘limma’, ‘lme4’, ‘S4Vectors’, ‘SingleCellExperiment’,<\/p>\n

‘SummarizedExperiment’, ‘batchelor’, ‘HDF5Array’,<\/p>\n

‘terra’, ‘ggrastr’))<\/p>\n

\u7136\u540e\u5b89\u88c5monocle3<\/p>\n

install.packages(“devtools”)<\/p>\n

devtools::install_github(‘cole-trapnell-lab\/monocle3’)<\/p>\n

\u901a\u5e38\u8fd9\u4f1a\u9700\u8981\u4e00\u5b9a\u7684\u65f6\u95f4\u3002<\/p>\n

\u52a0\u8f7dmonocle3<\/p>\n

library(monocle3)<\/p>\n

\u4e8c\u3001\u6570\u636e\u52a0\u8f7d\u4e0e\u9884\u5904\u7406<\/h2>\n

\u5c0f\u679c\u8fd9\u91cc\u4f7f\u7528\u4e00\u4e2a\u79c0\u4e3d\u9690\u6746\u7ebf\u866b\u6570\u636e\u96c6\uff0c\u8fd9\u4e00\u6570\u636e\u96c6\u6765\u81eaPacker&Zhu\u7b49\u4eba\u3002\u4ed6\u4eec\u7684\u7814\u7a76\u5305\u62ec\u5bf9\u6574\u4e2a\u80da\u80ce\u53d1\u80b2\u8fc7\u7a0b\u7684\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u3002\u6211\u4eec\u5c06\u4f7f\u7528\u6570\u636e\u7684\u4e00\u5c0f\u90e8\u5206\uff0c\u5176\u4e2d\u5305\u62ec\u5927\u591a\u6570\u795e\u7ecf\u5143\u7ec6\u80de\u3002<\/p>\n

#\u52a0\u8f7d\u6570\u636e<\/p>\n

expression_matrix <- readRDS(url(“https:\/\/depts.washington.edu:\/trapnell-lab\/software\/monocle3\/celegans\/data\/packer_embryo_expression.rds”))<\/p>\n

cell_metadata <- readRDS(url(“https:\/\/depts.washington.edu:\/trapnell-lab\/software\/monocle3\/celegans\/data\/packer_embryo_colData.rds”))<\/p>\n

gene_annotation <- readRDS(url(“https:\/\/depts.washington.edu:\/trapnell-lab\/software\/monocle3\/celegans\/data\/packer_embryo_rowData.rds”))<\/p>\n

cds <- new_cell_data_set(expression_matrix,<\/p>\n

cell_metadata = cell_metadata,<\/p>\n

gene_metadata = gene_annotation)<\/p>\n

#\u6570\u636e\u9884\u5904\u7406<\/p>\n

#\u6570\u636e\u9884\u5904\u7406<\/p>\n

cds <- preprocess_cds(cds, num_dim = 50)<\/p>\n

cds <- align_cds(cds, alignment_group = “batch”, residual_model_formula_str = “~ bg.300.loading + bg.400.loading + bg.500.1.loading + bg.500.2.loading + bg.r17.loading + bg.b01.loading + bg.b02.loading”)<\/p>\n

\u4f7f\u7528alignment_group\u5bf9\u5176\u4e0d\u540c\u6279\u6b21\u7ec6\u80de\u3002bg.300\u7b49\u6bcf\u4e00\u5217\u5bf9\u5e94\u4e0e\u7ec6\u80de\u53ef\u80fd\u88ab\u6c61\u67d3\u7684\u4fe1\u53f7\uff0c\u5c06\u8fd9\u4e9b\u9879\u901a\u8fc7residual_model_formula_str\u4f20\u9012\u7ed9align_cds\uff0c\u5728\u540e\u7eed\u7684\u5904\u7406\u4e2d\u51cf\u53bb\u8fd9\u4e9b\u80cc\u666f\u4fe1\u53f7\u3002<\/p>\n

\u964d\u4f4e\u7ef4\u5ea6\u5e76\u53ef\u89c6\u5316\u7ed3\u679c<\/h2>\n

\u63a5\u4e0b\u6765\u53ef\u4ee5\u5bf9\u6570\u636e\u8fdb\u884c\u964d\u7ef4\uff0cmonocle3\u5efa\u8bae\u4f7f\u7528\u9ed8\u8ba4\u7684UMAP\u7b97\u6cd5\u964d\u7ef4\u3002<\/p>\n

cds <- reduce_dimension(cds)<\/p>\n

plot_cells(cds, label_groups_by_cluster=FALSE, \u00a0color_cells_by = “cell.type”)<\/p>\n

<\/p>\n

Monocle\u91cd\u5efa\u4e86\u5e26\u6709\u8bb8\u591a\u5206\u652f\u7684\u8f68\u8ff9\u3002<\/p>\n

\u4f7f\u7528plot_cells\uff08\uff09\u6765\u53ef\u89c6\u5316\u5355\u4e2a\u57fa\u56e0\u5982\u4f55\u6cbf\u8f68\u8ff9\u53d8\u5316\u3002\u8ba9\u6211\u4eec\u6765\u770b\u770b\u4e00\u4e9b\u5728\u7ea4\u6bdb\u795e\u7ecf\u5143\u4e2d\u5177\u6709\u6709\u8da3\u8868\u8fbe\u6a21\u5f0f\u7684\u57fa\u56e0\uff1a<\/p>\n

ciliated_genes <- c(“che-1”,<\/p>\n

“hlh-17”,<\/p>\n

“nhr-6”,<\/p>\n

“dmd-6”,<\/p>\n

“ceh-36”,<\/p>\n

“ham-1”)<\/p>\n

plot_cells(cds,<\/p>\n

genes=ciliated_genes,<\/p>\n

label_cell_groups=FALSE,<\/p>\n

show_trajectory_graph=FALSE)<\/p>\n

<\/p>\n

6\u4e2a\u57fa\u56e0\u7684\u8f68\u8ff9\u53d8\u5316\u3002<\/p>\n

\u805a\u7c7b\u7ec6\u80de<\/h2>\n

\u867d\u7136\u7ec6\u80de\u53ef\u4ee5\u4ece\u4e00\u79cd\u72b6\u6001\u6301\u7eed\u8fc7\u6e21\u5230\u4e0b\u4e00\u79cd\u72b6\u6001\uff0c\u5b83\u4eec\u4e4b\u95f4\u6ca1\u6709\u79bb\u6563\u7684\u8fb9\u754c\uff0c\u4f46\u662fMonocle\u5e76\u4e0d\u8ba4\u4e3a\u6570\u636e\u96c6\u4e2d\u7684\u6240\u6709\u7ec6\u80de\u90fd\u6765\u81ea\u4e00\u4e2a\u5171\u540c\u7684\u8f6c\u5f55\u201c\u7956\u5148\u201d\u3002Monocle\u80fd\u591f\u901a\u8fc7\u5176\u805a\u7c7b\u7a0b\u5e8f\u4e86\u89e3\u7ec6\u80de\u4f55\u65f6\u5e94\u8be5\u653e\u7f6e\u5728\u76f8\u540c\u7684\u8f68\u8ff9\u4e2d\uff0c\u800c\u4e0d\u662f\u653e\u7f6e\u5728\u4e0d\u540c\u7684\u8f68\u8ff9\u4e2d\u3002<\/p>\n

cds <- cluster_cells(cds)<\/p>\n

plot_cells(cds, color_cells_by = “partition”)<\/p>\n

<\/p>\n

\u5c06\u7ec6\u80de\u5206\u4e3a\u4e86\u56db\u7c7b\u3002<\/p>\n

\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u4f7f\u7528\u6700\u5173\u952e\u7684Learn_graph\uff08\uff09\u51fd\u6570\u5728\u6bcf\u4e2a\u5206\u533a\u4e2d\u62df\u5408\u4e00\u4e2a\u4e3b\u8981\u56fe\uff1a<\/p>\n

cds <- learn_graph(cds)<\/p>\n

plot_cells(cds,<\/p>\n

color_cells_by = “cell.type”,<\/p>\n

label_groups_by_cluster=FALSE,<\/p>\n

label_leaves=FALSE,<\/p>\n

label_branch_points=FALSE)<\/p>\n

<\/p>\n

\u8be5\u56fe\u5c06\u7528\u4e8e\u8bb8\u591a\u4e0b\u6e38\u6b65\u9aa4\uff0c\u5982\u5206\u652f\u5206\u6790\u548c\u5fae\u5206\u8868\u8fbe\u5f0f\u3002<\/p>\n

\u7ec6\u80de\u4f2a\u65f6\u5e8f\u5206\u6790<\/h2>\n

\u4e3a\u4e86\u5c06\u7ec6\u80de\u6392\u5217\u6574\u9f50\uff0c\u6211\u4eec\u9700\u8981\u544a\u8bc9Monocle\u751f\u7269\u8fc7\u7a0b\u7684\u201c\u5f00\u59cb\u201d\u5728\u54ea\u91cc\u3002\u6211\u4eec\u901a\u8fc7\u9009\u62e9\u56fe\u4e2d\u6807\u8bb0\u4e3a\u8f68\u8ff9\u201c\u6839\u201d\u7684\u533a\u57df\u6765\u505a\u5230\u8fd9\u4e00\u70b9\u3002\u5728\u65f6\u95f4\u5e8f\u5217\u5b9e\u9a8c\u4e2d\uff0c\u8fd9\u901a\u5e38\u53ef\u4ee5\u901a\u8fc7\u5728UMAP\u7a7a\u95f4\u4e2d\u627e\u5230\u7531\u65e9\u671f\u65f6\u95f4\u70b9\u7684\u7ec6\u80de\u5360\u636e\u7684\u70b9\u6765\u5b9e\u73b0\uff1a<\/p>\n

plot_cells(cds,<\/p>\n

color_cells_by = “embryo.time.bin”,<\/p>\n

label_cell_groups=FALSE,<\/p>\n

label_leaves=TRUE,<\/p>\n

label_branch_points=TRUE,<\/p>\n

graph_label_size=1.5)<\/p>\n

<\/p>\n

\u9ed1\u7ebf\u663e\u793a\u4e86\u56fe\u7684\u7ed3\u6784\uff0c\u5e26\u6570\u5b57\u7684\u5706\u5708\u8868\u793a\u56fe\u5f62\u4e2d\u7684\u7279\u6b8a\u70b9\u3002\u6bcf\u7247\u53f6\u5b50\uff0c\u7528\u6d45\u7070\u8272\u5706\u5708\u8868\u793a\uff0c\u5bf9\u5e94\u4e8e\u8f68\u8ff9\u7684\u4e0d\u540c\u7ed3\u679c\uff08\u5373\u7ec6\u80de\u547d\u8fd0\uff09\u3002\u9ed1\u8272\u5706\u5708\u8868\u793a\u5206\u652f\u8282\u70b9\uff0c\u5176\u4e2d\u7ec6\u80de\u53ef\u4ee5\u4f20\u64ad\u5230\u51e0\u4e2a\u7ed3\u679c\u4e4b\u4e00\u3002<\/p>\n

\uff08\u4e00\uff09\u624b\u52a8\u9009\u53d6\u6839\u7ec6\u80de<\/h3>\n

cds <- order_cells(cds)<\/p>\n

\u73b0\u5728\u6211\u4eec\u5df2\u7ecf\u77e5\u9053\u4e86\u65e9\u671f\u7ec6\u80de\u7684\u4e0b\u843d\uff0c\u6211\u4eec\u53ef\u4ee5\u8c03\u7528order_cells\uff08\uff09\uff0c\u5b83\u5c06\u8ba1\u7b97\u6bcf\u4e2a\u7ec6\u80de\u5728\u4f2a\u65f6\u95f4\u5185\u7684\u4e0b\u843d\u3002\u4e3a\u4e86\u8fd9\u6837\u505a\uff0corder_cells\uff08\uff09\u9700\u8981\u60a8\u6307\u5b9a\u8f68\u8ff9\u56fe\u7684\u6839\u8282\u70b9\u3002\u5982\u679c\u60a8\u4e0d\u63d0\u4f9b\u5b83\u4eec\u4f5c\u4e3a\u53c2\u6570\uff0c\u5b83\u5c06\u542f\u52a8\u7528\u4e8e\u9009\u62e9\u4e00\u4e2a\u6216\u591a\u4e2a\u6839\u8282\u70b9\u7684\u56fe\u5f62\u7528\u6237\u754c\u9762\u3002<\/p>\n

<\/p>\n

\u7ed8\u5236\u7ec6\u80de\u5e76\u901a\u8fc7\u5047\u65f6\u95f4\u5bf9\u5176\u7740\u8272\u663e\u793a\u4e86\u5b83\u4eec\u662f\u5982\u4f55\u6392\u5217\u7684\uff1a<\/p>\n

plot_cells(cds,<\/p>\n

color_cells_by = “pseudotime”,<\/p>\n

label_cell_groups=FALSE,<\/p>\n

label_leaves=FALSE,<\/p>\n

label_branch_points=FALSE,<\/p>\n

graph_label_size=1.5)<\/p>\n

<\/p>\n

\u6709\u4e9b\u5355\u5143\u683c\u662f\u7070\u8272\u7684\u3002\u8fd9\u610f\u5473\u7740\u5b83\u4eec\u6709\u65e0\u9650\u7684\u4f2a\u65f6\u95f4\uff0c\u56e0\u4e3a\u5b83\u4eec\u65e0\u6cd5\u4ece\u6839\u8282\u70b9\u8bbf\u95ee\u3002<\/p>\n

\uff08\u4e8c\uff09\u901a\u8fc7\u4ee3\u7801\u62fe\u53d6\u6839\u8282\u70b9<\/h3>\n

\u901a\u5e38\u9700\u8981\u4ee5\u7f16\u7a0b\u65b9\u5f0f\u6307\u5b9a\u8f68\u8ff9\u7684\u6839\uff0c\u800c\u4e0d\u662f\u624b\u52a8\u62fe\u53d6\u3002\u4e0b\u9762\u7684\u51fd\u6570\u9996\u5148\u6839\u636e\u6700\u63a5\u8fd1\u7684\u8f68\u8ff9\u56fe\u8282\u70b9\u5bf9\u5355\u5143\u683c\u8fdb\u884c\u5206\u7ec4\u3002\u7136\u540e\uff0c\u5b83\u8ba1\u7b97\u6bcf\u4e2a\u8282\u70b9\u7684\u5355\u5143\u683c\u4e2d\u6765\u81ea\u6700\u65e9\u65f6\u95f4\u70b9\u7684\u90e8\u5206\u3002\u7136\u540e\uff0c\u5b83\u9009\u62e9\u65e9\u671f\u7ec6\u80de\u5360\u7528\u6700\u591a\u7684\u8282\u70b9\uff0c\u5e76\u5c06\u5176\u4f5c\u4e3a\u6839\u8fd4\u56de\u3002<\/p>\n

get_earliest_principal_node <- function(cds, time_bin=”130-170″){<\/p>\n

cell_ids <- which(colData(cds)[, “embryo.time.bin”] == time_bin)<\/p>\n

closest_vertex <-<\/p>\n

cds@principal_graph_aux[[“UMAP”]]$pr_graph_cell_proj_closest_vertex<\/p>\n

closest_vertex <- as.matrix(closest_vertex[colnames(cds), ])<\/p>\n

root_pr_nodes <-<\/p>\n

igraph::V(principal_graph(cds)[[“UMAP”]])$name[as.numeric(names<\/p>\n

(which.max(table(closest_vertex[cell_ids,]))))]<\/p>\n

root_pr_nodes<\/p>\n

}<\/p>\n

\u901a\u8fc7root_pr_node\u53c2\u6570\u5c06\u7a0b\u5e8f\u9009\u62e9\u7684\u6839\u8282\u70b9\u4f20\u9012\u7ed9order_cells\uff08\uff09\u4f1a\u4ea7\u751f<\/p>\n

<\/p>\n

\u751f\u62103D\u56fe<\/h3>\n

\u6211\u4eec\u8fd8\u53ef\u4ee5\u751f\u62103D\u7684\u7ec6\u80de\u5206\u5316\u8f68\u8ff9\u56fe\u3002<\/p>\n

cds_3d <- reduce_dimension(cds, max_components = 3)<\/p>\n

cds_3d <- cluster_cells(cds_3d)<\/p>\n

cds_3d <- learn_graph(cds_3d)<\/p>\n

cds_3d <- order_cells(cds_3d, root_pr_nodes=get_earliest_principal_node(cds))<\/p>\n

cds_3d_plot_obj <- plot_cells_3d(cds_3d, color_cells_by=”partition”)<\/p>\n

<\/p>\n

\u5c0f\u679c\u4eca\u5929\u7684\u8bb2\u89e3\u5c31\u5230\u8fd9\u91cc\u4e86~\u6211\u4eec\u4e0b\u671f\u89c1\u3002<\/p>\n

\u672c\u516c\u53f8\u5f00\u53d1\u7684\u4e91\u5e73\u53f0\u751f\u7269\u4fe1\u606f\u5206\u6790\u5de5\u5177\uff0c\u96f6\u4ee3\u7801\u5b8c\u6210\u5206\u6790\uff0c\u540c\u65f6\u4e5f\u6709\u8bb8\u591a\u751f\u7269\u5904\u7406\u7684\u597d\u7528\u5de5\u5177\u54e6~\u4e91\u5e73\u53f0\u7f51\u5740\uff1ahttp:\/\/www.biocloudservice.com\/home.html<\/p>\n","protected":false},"excerpt":{"rendered":"

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