1. 各个branch的数据提取 kk <- plot_genes_branched_heatmap() kk$BranchA_exprs 2. 提取行聚类信息 kk <- plot_genes_branched_heatmap(num_clusters=3, return_heatmap=T) pp <- cutree(kk$ph$tree_row, k=3)
heat<-plot_genes_branched_heatmap(gbm_cds[heatmap_gene,], branch_point = i, num_clusters = 2, cores = 4, use_gene_short_name = T, show_rownames = T, return_heatmap=T) png(paste0(outputDir,'/',"branch",i,"_pseudo_heatmap.png"),w=8,h=8,res=300,units="in") grid::grid...
monocle画图中屏幕上的数据如何储存 1.问题描述:使用monocle的plot_genes_branched_heatmap命令,画出图 但是每一个cluster是哪些基因ID? 2.解决方法:画图命令增加“ return_heatmap = TRUE”。就会在屏幕上输出,每个基因对应的分支号,然后每一支挑一些有代表性的写一下。 3.拓展,在屏幕上复制太麻烦了,并且太多了...
BEAM_res=BEAM_res[,c("gene_short_name","pval","qval")]saveRDS(BEAM_res,file="BEAM_res.rds")BEAM_res<-readRDS("BEAM_res.rds")tmp1=plot_genes_branched_heatmap(Mono_mococle_data[row.names(subset(BEAM_res,qval<1e-4)),],branch_point=1,num_clusters=3,#这些基因被分成几个group cores...
plot_genes_branched_heatmap(lung[row.names(subset(BEAM_res,qval<1e-4)),],branch_point=1,num_clusters=4,cores=1,use_gene_short_name=T,show_rownames=T) 代码语言:javascript 复制 lung_genes<-row.names(subset(fData(lung),gene_short_name%in%c("Ccnd2","Sftpb","Pdpn")))plot_genes_bran...
plot_genes_branched_heatmap(my_cds_subset[row.names(subset(BEAM_res, qval < 1e-4)),], branch_point = 1, num_clusters = 4, cores = 8, use_gene_short_name = TRUE, show_rownames = TRUE) 拟时分析的内容很丰富,也很多,在不同的研究中有不同的意义,这里只是简单展示了几种常见的可视化结...
plot_cell_trajectory(HSMM, color_by = "State") 图片 BEAM进行统计分析 BEAM_res <- BEAM(HSMM, branch_point = 1, cores = 8)BEAM_res <- BEAM_res[order(BEAM_res$qval),]BEAM_res <- BEAM_res[,c("gene_short_name", "pval", "qval")]plot_genes_branched_heatmap(HSMM[row.names(subset...
test_ordering_genes=unique(marker_gene$gene) test=setOrderingFilter(test,ordering_genes = test_ordering_genes) #指明哪些基因用于后续的聚类/排序 1. 2. 3. 4. 3. 推断轨迹,并按照拟时序给细胞排序 test=reduceDimension(test,reduction_method = "DDRTree",max_components = 2, norm_method = 'log',...
1. ├─ClusterGVis::visCluster(object = df, plot.type = "heatmap") 2. │└─data.frame(object$wide.res, check.names = FALSE) %>% ... 3. ├─dplyr::arrange(., as.numeric(as.character(cluster))) 4. └─dplyr:::arrange.data.frame(., as.numeric(as.character(cluster))) ...
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...