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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,104 @@ | ||
| #' Plot HPO pseudotime | ||
| #' | ||
| #' Plot the Human Phenotype Ontology (HPO) in genomic latent space, | ||
| #' then compute pseudotime trajectories between a subset of phenotypes that | ||
| #' are symptoms of a given disease (or set of diseases). | ||
| #' @param obj \link{Seurat} object of HPO terms generated by \link{prepare_hpo}. | ||
| #' @param disease_ids One or more disease IDs found within \code{dt_genes}. | ||
| #' @inheritParams prepare_hpo | ||
| #' @inheritParams monocle3::learn_graph | ||
| #' @inheritDotParams monocle3::plot_cells | ||
| #' @export | ||
| plot_hpo_pseudotime <- function(obj, | ||
| dt_genes = HPOExplorer::load_phenotype_to_genes(1), | ||
| disease_ids=dt_genes$disease_id[1:3], | ||
| learn_graph_control=list(prune_graph=FALSE), | ||
| ... | ||
| ){ | ||
| requireNamespace("monocle3") | ||
| requireNamespace("SeuratWrappers") | ||
| gene_symbol <- disease_id <- NULL; | ||
|
|
||
| #### Add disease metadata | ||
| ## Using gene overlap (670998 p2d pairs) ## | ||
| # nrow(unique(dt_genes[,c("hpo_id","disease_id")])) | ||
| p2d <- dt_genes[disease_id %in% disease_ids, | ||
| list(count=data.table::uniqueN(gene_symbol)), | ||
| by=c("hpo_id","disease_id")]#|> | ||
| # data.table::dcast.data.table(formula = hpo_id~disease_id, | ||
| # value.var = "count") | ||
| if(nrow(p2d)==0) stopper("No hpo_ids found for the given disease_ids.") | ||
| # p2d <- p2d[colnames(ref),] | ||
| # ## Using annotation overlap (258276 p2d pairs) | ||
| # nrow(unique(dt_annot[,c("hpo_id","disease_id")])) | ||
| # p2d <- dt_annot[,count:=1]|> | ||
| # data.table::dcast.data.table(formula = hpo_id~disease_id, | ||
| # value.var = "count", | ||
| # ) | ||
| #### Merge with rest of annotations #### | ||
| ## make Seurat way too slow having this many columns... | ||
| # dt_annot_melt <- data.table::merge.data.table(dt_annot_melt, | ||
| # p2d, | ||
| # by.x = "id", | ||
| # by.y = "hpo_id", | ||
| # all.x = TRUE) | ||
|
|
||
| hpo_ids <- intersect(unique(p2d$hpo_id), | ||
| colnames(obj)) | ||
| if(length(hpo_ids)==0) { | ||
| stopper("No hpo_ids oevrlapping with samples (colnames) in obj.") | ||
| } | ||
| cds <- SeuratWrappers::as.cell_data_set(obj) | ||
| cds_sub <- monocle3::cluster_cells( | ||
| cds = cds[,hpo_ids], | ||
| k = max(as.integer(cds@colData$seurat_clusters))) | ||
| cds_sub <- monocle3::learn_graph(cds_sub, | ||
| learn_graph_control=learn_graph_control) | ||
| cds_sub <- monocle3::order_cells(cds_sub, root_cells = colnames(cds_sub)) | ||
| monocle3::principal_graph(cds) <- monocle3::principal_graph(cds_sub) | ||
| monocle3::principal_graph_aux(cds) <- monocle3::principal_graph_aux(cds_sub) | ||
| plt <- monocle3::plot_cells(cds, | ||
| # color_cells_by = "ancestor_name_abnormality", | ||
| # group_cells_by = "top_celltype" | ||
| ) | ||
| return( | ||
| list(data=cds, | ||
| plot=plt) | ||
| ) | ||
| # pseudo_dt <- t(cds@principal_graph_aux$UMAP$pr_graph_cell_proj_dist)|>`colnames<-`(c("umap1","umap2")) | ||
|
|
||
| # gm <- ref@graphs$freq_nn[highlights$hpo_id, | ||
| # highlights$hpo_id] | ||
| # g <- igraph::graph_from_adjacency_matrix(gm) | ||
| # gdt <- KGExplorer::graph_to_dt(g)[object!=subject] | ||
| # ggraph(g, layout = 'stress') + | ||
| # geom_edge_density() + | ||
| # geom_edge_link(alpha = 0.25) | ||
| # dp <- Seurat::DimPlot(ref, | ||
| # group.by = disease_id, | ||
| # cols.highlight = "red", | ||
| # alpha = .7, | ||
| # sizes.highlight = highlights[[disease_id]], | ||
| # cells.highlight = list( highlights[["hpo_id"]])|> | ||
| # `names<-`(disease_id)) | ||
| # highlight_df <- subset(dp[[1]]$data,highlight!="Unselected") | ||
| # | ||
| # highlight_dt <- merge(gdt, | ||
| # data.table::data.table(highlight_df,keep.rownames = "subject"), | ||
| # by="subject")|> | ||
| # merge(data.table::data.table(highlight_df,keep.rownames = "object"), | ||
| # by=c("object","highlight",disease_id)) |> | ||
| # data.table::setnames(c("umap_1.x","umap_2.x","umap_1.y","umap_2.y"), | ||
| # c("x","y","xend","yend")) | ||
| # # ggplot2::ggplot() | ||
| # dp + ggplot2::geom_segment(data = highlight_dt, | ||
| # mapping = ggplot2::aes(x = x, | ||
| # xend = xend, | ||
| # y = y, | ||
| # yend = yend), | ||
| # inherit.aes = FALSE) | ||
| # dp+ | ||
| # ggplot2::geom_step(data = highlight_df, | ||
| # ggplot2::aes(x=umap_1, | ||
| # y=umap_2)) | ||
| } |
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