## ----- echo = false -----------------------------------------------------------------------------------------------------------------库（Knitr）## ----警告= f，消息= f ------------------------------------------------------------------------------------------------------------------------------------------------库（Tradeseq）图书馆（rcolorbrewer）图书馆（SingleCellexperiment）＃对于可重复性调色板（brewer.pal（8，“ dark2”））数据（countmatrix，package =“ tradeseq”）counts < - as.matrix（countmatrix）rm（countmatrix）rm（Countmatrix）data（cellType，package =“ traveSeq”）＃＃＃----- eval = false -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- set.seed（22）＃library（monocle3）＃＃创建一个cell_data_set对象＃cds <-new_cell_data_set（counts，cell_metadata = pd，＃gene_metadata）= data.frame（gene_short_name = rownames（counts），＃row.names = rownames（counts）））＃＃＃在data＃cds <-preprocess_cds（cds，method =“ pca”）上运行umap umapredy_dimension（cds，preprocess_method =“ pca”，＃reduction_method =“ umap”）＃＃＃＃＃＃＃＃＃＃＃priont coloring by Paul et al＃plot_cells（cds，cds，label_groups_by_cluste）r = false，cell_size = 1，＃color_cells_by =“ cellType”）＃＃运行群集方法。这是构造图＃cds <-cluster_cells（cds，reduction_method =“ umap”）＃＃可视化簇＃plot_cells（cds，color_cells_by =“ cluster”，cell_size = 1）＃plot_cells = 1）请注意，要运行该代码的其余部分，应完全连接该图＃CDS < - Learn_graph（cds，use_partition = false）＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃（cds）[pd $ cellType ==“ multipotent祖细胞”]＃CLESSEST_VERTEX <-cds@principal_graph_aux [[“ umap”] $ pr_graph_cell_proj_closest_vertex_vertex＃closest_vertex＃-CESSCEST_VERTEX [cell_ids，]＃clessest_vertex < - as.numeric（names（whe.max（table（clessest_vertex）））））＃mst <-mstal_graph（cds）$ umap＃umap＃root_pr_nodes <-igraph <-igraph :: v（mst）$ names $ names $ names $ names $ name[closest_vertex] # # # We compute the trajectory # cds <- order_cells(cds, root_pr_nodes = root_pr_nodes) # plot_cells(cds, color_cells_by = "pseudotime") ## ---- eval = FALSE------------------------------------------------------------ # library(magrittr) # # Get the closest vertice for every cell # y_to_cells <- principal_graph_aux(cds)$UMAP$pr_graph_cell_proj_closest_vertex %>% # as.data.frame() # y_to_cells$cells <- rownames(y_to_cells) # y_to_cells$Y <- y_to_cells$V1 # # # Get the root vertices # # It is the same node as above # root <- cds@principal_graph_aux$UMAP$root_pr_nodes # # # Get the other endpoints # endpoints <- names(which(igraph::degree(mst) == 1)) # endpoints <- endpoints[!endpoints %in% root] # # # For each endpoint # cellWeights <- lapply(endpoints, function(endpoint) { # # We find the path between the endpoint and the root # path <- igraph::shortest_paths(mst, root, endpoint)$vpath[[1]] # path <- as.character(path) # # We find the cells that map along that path # df <- y_to_cells[y_to_cells$Y %in% path, ] # df <- data.frame(weights = as.numeric(colnames(cds) %in% df$cells)) # colnames(df) <- endpoint # return(df) # }) %>% do.call(what = 'cbind', args = .) %>% # as.matrix() # rownames(cellWeights) <- colnames(cds) # pseudotime <- matrix(pseudotime(cds), ncol = ncol(cellWeights), # nrow = ncol(cds), byrow = FALSE) ## ---- eval = FALSE------------------------------------------------------------ # sce <- fitGAM(counts = counts, # pseudotime = pseudotime, # cellWeights = cellWeights) ## ----------------------------------------------------------------------------- sessionInfo()