# 轨迹推断 cds <- learn_graph(cds,verbose=T, use_partition=T, #默认是T,T时是顾及全局的情况 ) plot_cells(cds, color_cells_by = 'celltype', label_groups_by_cluster=FALSE, cell_size=1,group_label_size=4, trajectory_graph_color='#023858', trajectory_graph_segment_size = 1) ...
##run cds_data <- ks_run_monocle3(object=cytotrace2_sce, idents="cluster", use_partition=F, learn_graph_control=list(minimal_branch_len=9.5,euclidean_distance_ratio=1), define_root=T, know_root=T, root_state="YSMP") #plot plot_cells(cds_data, label_cell_groups = F, color_cells_...
library(ggplot2)library(monocle3)source('./ks_run_monocle3.R') #monocle3 analysis#运行monocle3分析0参照monocle3视频教程cds_data <- ks_run_monocle3(object=cytotrace2_sce,idents="cluster",use_partition=F,learn_graph_control=list(minimal_branch_len=9.5,euclidean_distance_ratio=1),define_root=T...
cds<-learn_graph(cds,verbose=T,use_partition=T,#默认是T,T时是顾及全局的情况)plot_cells(cds,color_cells_by='celltype',label_groups_by_cluster=FALSE,cell_size=1,group_label_size=4,trajectory_graph_color='#023858',trajectory_graph_segment_size=1) 6.定义起点-轨迹可视化 代码语言:javascript ...
erythroid.cds<-as.cell_data_set(erythroid)erythroid.cds<-cluster_cells(cds=erythroid.cds,reduction_method="UMAP")erythroid.cds<-learn_graph(erythroid.cds,use_partition=TRUE)lymphoid.cds<-as.cell_data_set(lymphoid)lymphoid.cds<-cluster_cells(cds=lymphoid.cds,reduction_method="UMAP")lymphoid.cds<...
##runcds_data<-ks_run_monocle3(object=cytotrace2_sce,idents="cluster",use_partition=F,learn_graph_control=list(minimal_branch_len=9.5,euclidean_distance_ratio=1),define_root=T,know_root=T,root_state="YSMP") #plotplot_cells(cds_data,label_cell_groups = F,color_cells_by="pseudotime",la...
group_cells_by="partition")#根据cluster分组绘制 ## Warning in if (ordering_type == "cluster_row_col") {: the condition has length > ## 1 and only the first element will be used ## Warning in if (ordering_type == "cluster_row_col") {: the condition has length > ...
# have NA as the cell type so use this function with caution. consensus <- function(x) { uniqx <- unique(na.omit(x)) uniqx[which.max(tabulate(match(x, uniqx)))] } l_partition <- unique(partitions(cds)) l_cell_type <- list() ...
自动对UMAP图分区(partition),可以选择多个起点,轨迹分析算法的逻辑更符合生物学现实。 除了轨迹分析的主要功能,monocle3差异分析方法也有其独到之处,可以做一些与seurat不好实现的分析。 monocel3的安装 先安装一些依赖包,大家安装前可以查看一下这些包是否已经安装过了。
monocle.object = learn_graph(monocle.object, learn_graph_control=list(ncenter=500), use_partition = FALSE) plot_cells(monocle.object, color_cells_by="partition", group_cells_by="partition") a helper function to identify the root principal points: make cluster 2 the root get_earliest_principa...