# 定义root cell, 推断拟时方向 # 结合先验知识自定(示例数据) cds <- order_cells(cds) # 可视化 plot_cells(cds, label_cell_groups = F, color_cells_by = "pseudotime", label_branch_points = F, graph_label_size = 0, cell_size=2, trajectory_graph_color='black', trajectory_graph_segment...
cds <- learn_graph(cds) #轨迹推断plot_cells(cds, color_cells_by = "partition", cell_size = 0.5) 6. 选择合适的起点 可以通过算法计算起始点,也可以通过手动选择起始点,考虑到生物学意义,推荐手动设置起点。 cds <- order_cells(cds, root_cells = root.cell) #选择对应的细胞群作为起点 7. 下游...
如果在调用 order_cells() 函数时没有明确指定 root_cells 参数,该工具就会被激活。在这里,已经预先选定了一些细胞作为起点,并将这些选择保存到了一个文件中,以确保结果的可重现性。 # load the pre-selected HSCshsc<-readLines("hsc_cells.txt")# order cellserythroid.cds<-order_cells(erythroid.cds,reductio...
## 识别轨迹 cds<-learn_graph(cds)p=plot_cells(cds,label_groups_by_cluster=FALSE,label_leaves=FALSE,label_branch_points=FALSE) 代码语言:javascript 复制 ##细胞按拟时排序 # cds<-order_cells(cds)存在bug,使用辅助线选择root细胞 p+
手动选择root # 解决order_cells(cds)报错"object 'V1' not found"# rownames(cds@principal_graph_aux[["UMAP"]]$dp_mst) <- NULL# colnames(cds@int_colData@listData$reducedDims@listData$UMAP) <- NULLcds<-order_cells(cds) 运行上面的代码后,会跳出这个窗口。可以手动在图上选择一个位置,然后点击...
## Step 4: Cluster the cells cds <- cluster_cells(cds) ### === Order cells in pseudotime along a trajectory(可选) ## Step 5: Learn a graph cds <- learn_graph(cds) ## Step 6: Order cells cds <- order_cells(cds) plot_cells...
cds <- order_cells(cds) plot_cells(cds, color_cells_by = "pseudotime", label_cell_groups=FALSE, label_leaves=FALSE, label_branch_points=FALSE, graph_label_size=1.5) 也可以使用3D轨迹,只需要在降维的时候使用3个维度就可以了。 cds_3d <- reduce_dimension(cds, max_components = 3) ...
4.Order cells in pseudotime along a trajectory 手动选择root需要根据自己的生物学背景知识 ## Learn a graph cds %<>% learn_graph() plot_cells(cds,trajectory_graph_segment_size =1.5, group_label_size =6,group_cells_by="cluster", color_cells_by ="cell_type", label_groups_by_cluster =FALSE...
当时Biomamba就表示发表的文献还都是以monocle2为主流,没想到目前我读过的文章依旧是以monocle2为主,去除那些我刻意检索monocle3的文章,我至今方才读到一篇通过monocle3完成拟时序分析的单细胞论文,题为Single-Nucleus RNA Sequencing Identifies New Classes of Proximal Tubular Epithelial Cells in Kidney Fibrosis,投稿...
4.Order cells in pseudotime along a trajectory 手动选择root需要根据自己的生物学背景知识 ## Learn a graphcds %<>% learn_graph()plot_cells(cds,trajectory_graph_segment_size = 1.5, group_label_size = 6,group_cells_by="cluster", color_cells_by = "cell_type", label_groups_by_cluster = ...