branch<-length(gbm_cds@auxOrderingData[[gbm_cds@dim_reduce_type]]$branch_points) if (branch != 0){ for(i in 1:branch){ print(paste0("Find branch ",i," related genes need some time ",lubridate::now())) BEAM_res <-BEAM(gbm_cds, branch_point = i, cores = 4,progenitor_method ...
]my_branched_heatmap<-plot_genes_branched_heatmap(cds1[row.names(subset(BEAM_res,qval<1e-4)),],branch_point=1,num_clusters=4,cores=4,use_gene_short_name=TRUE,show_rownames=FALSE,return_heatmap=TRUE)
BEAM_res <- BEAM(cds[ordergene,],branch_point = 1,cores = 2) #报错内容 monocle2 Error in if (progenitor_method == "duplicate") { : the condition has length > 1 Calls: trajecorty_sub -> BEAM -> branchTest -> buildBranchCellDataSet 修改方法: (1)参考monocle中遇到的问题 - 简书中的...
在monocle中的分析中发现,细胞群能从一个轨迹分叉成不同的分支点,Monocle采用分支表达式分析建模,主要是BEAM函数,可以将分叉过程重构为一个分支轨迹,从而分析不同细胞命运下的差异。 # BEAM进行统计分析(此处,分析branch_point = 1这个分支处的细胞命名分叉是如何进行的)BEAM_res<-BEAM(HSMM_top[Time_genes,],branc...
6BEAM_res <- BEAM(lung, branch_point =1, cores =1) 7BEAM_res <- BEAM_res[order(BEAM_res$qval),] 8BEAM_res <- BEAM_res[,c("gene_short_name","pval","qval")] 使用一种特殊类型的热图,您可以可视化所有明显依赖于分支的基因的变化。这张热图同时显示了两种血统的变化。它还要求您选择要检...
解决的问题:沿着拟时序的方向,经过不同的branch,发生了哪些基因的变化? 经常在文献里面看到的monocle2热图就是这种分析。 BEAM_res=BEAM(test,branch_point = 1,cores = 1) #会返回每个基因的显著性,显著的基因就是那些随不同branch变化的基因 #这一步很慢 ...
从结果说明可以看到,Pre−branch包含的细胞为 2, 3, 5。 'cell fate 1' and 'cell fate 2' cell fate 1和cell fate 2到底指什么?比如还是这里的branch point 3为例: Cell fate 1 corresponds to the state with small id (in this case, state 1) while cell fate 2 corresponds to sate with big...
其实也就是需要改变参数branch_states即可,代码如下: ## 更改细胞 tmp1=plot_genes_branched_heatmap(Mono_mococle_data[row.names(subset(BEAM_res,qval<1e-4)),],branch_point=1,num_clusters=3,#这些基因被分成几个group cores=1,branch_labels=c("Cell fate2","Cell fate1"),branch_states=c(3,2...
(A) The ordering of CD8 + T cells along pseudotime in a two-dimensional state-space defined by Monocle2. Cell orders are inferred from the expression of most dispersed genes across CD8 + T cell populations sans MAIT. Each point corresponds to a single cell, and each color ...
Branch time point detection algorithm : S4 对象 主要是基于CellDataSet对象来进行下游分析,继承自ExpressionSet对象,也是常见的3个组成: exprs, a numeric matrix of expression values, where rows are genes, and columns are cells phenoData, anAnnotatedDataFrameobject, where rows are cells, and columns are ...