2. 提取行聚类信息 kk <- plot_genes_branched_heatmap(num_clusters=3, return_heatmap=T) pp <- cutree(kk$ph$tree_row, k=3)
heatmap_gene<-row.names(BEAM_res)[order(BEAM_res$qval)][1:50] heat<-plot_genes_branched_heatmap(gbm_cds[heatmap_gene,], branch_point = i, num_clusters = 2, cores = 4, use_gene_short_name = T, show_rownames = T, return_heatmap=T) png(paste0(outputDir,'/',"branch",i,"...
monocle画图中屏幕上的数据如何储存 1.问题描述:使用monocle的plot_genes_branched_heatmap命令,画出图 但是每一个cluster是哪些基因ID? 2.解决方法:画图命令增加“ return_heatmap = TRUE”。就会在屏幕上输出,每个基因对应的分支号,然后每一支挑一些有代表性的写一下。 3.拓展,在屏幕上复制太麻烦了,并且太多了...
plot_genes_branched_heatmap(lung[row.names(subset(BEAM_res,qval<1e-4)),],branch_point=1,num_clusters=4,cores=1,use_gene_short_name=T,show_rownames=T) 代码语言:javascript 复制 lung_genes<-row.names(subset(fData(lung),gene_short_name%in%c("Ccnd2","Sftpb","Pdpn")))plot_genes_bran...
plot_genes_branched_heatmap(my_cds_subset[row.names(subset(BEAM_res, qval < 1e-4)),], branch_point = 1, num_clusters = 4, cores = 8, use_gene_short_name = TRUE, show_rownames = TRUE) 拟时分析的内容很丰富,也很多,在不同的研究中有不同的意义,这里只是简单展示了几种常见的可视化结...
tmp1=plot_genes_branched_heatmap(Mono_mococle_data[row.names(subset(BEAM_res,qval<1e-4)),],branch_point=1,num_clusters=3,#这些基因被分成几个group cores=1,branch_labels=c("Cell fate2","Cell fate1"),#hmcols=NULL,#默认值 hmcols=colorRampPalette(rev(brewer.pal(9,"PRGn")))(62),...
test=detectGenes(test,min_expr = 0.1) #计算每个基因在多少细胞中表达 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 2. 选择基因 选择研究的生物学过程涉及到的基因集,这一步对于轨迹形状的影响很大。 可以选择数据集中的高变基因,或者是在seurat中分析得到的marker基因列表。如果是...
plot_cell_trajectory(HSMM, color_by = "State") 图片 BEAM进行统计分析 BEAM_res <- BEAM(HSMM, branch_point = 1, cores = 8)BEAM_res <- BEAM_res[order(BEAM_res$qval),]BEAM_res <- BEAM_res[,c("gene_short_name", "pval", "qval")]plot_genes_branched_heatmap(HSMM[row.names(subset...
The package is pretty cool. Thanks to Junjun. But T got an error when plotting heatmap with results from monocle2. Here is the message: df = plot_genes_branched_heatmap2(cds[c(indGene), ], branch_point = 1, num_clusters = 4, show_rowname...
it works after i modified that. And it fix the same error when i use plot_genes_branched_pseudotime. however, i still got some warning: plot_genes_branched_heatmap(cds[row.names(subset(BEAM_res, qval < 1e-4)),], branch_point = 1, num_clusters = 4, cores = 4,use_gene_short_na...