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Monocle3往期
Monocle3（1）：单细胞拟时分析---分析（详细注释版）
##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")
#plotlabel_cell_groups = F,color_cells_by = "pseudotime",label_branch_points = F,label_roots =F,label_leaves =F,graph_label_size = 0,cell_size=2,trajectory_graph_color='black',trajectory_graph_segment_size = 2)
#在monocle3分析中，我们使用graph_test函数分析过拟时差异基因，这个过程比较慢modulated_genes <- graph_test(cds_data, neighbor_graph = "principal_graph", cores = 5)plot_genes <- row.names(subset(modulated_genes, q_value < 0.01 & morans_I > 0.25))
#plotht1 = ks_monocle3_heatmap(cds = cds_data,graph_gene = plot_genes,celltype_color = c("#E69253", "#EDB931", "#E4502E", "#4f372d"),cluster_color =c("#8f657d", "#42819F", "#86AA7D", "#CBB396"),num_clusters=4)
#label genegenes <- c("C1QB","C1QC","C1QA","MRC1","LGMN","MS4A7","MAF","FOLR2","HLA-DPA1","CLEC10A","IL10RA","CD163","KCTD12","CLEC7A","MS4A6A","CD14","ITM2A","CYTL1","MDK","SELP","CD24","S100A8","S100A9","S100A12")ht2 = ks_monocle3_heatmap(cds = cds_data,graph_gene = plot_genes,celltype_color = c("#E69253", "#EDB931", "#E4502E", "#4f372d"),cluster_color =c("#8f657d", "#42819F", "#86AA7D", "#CBB396"),num_clusters=4,labels = T,label_genes = genes)
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