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Latest update to Seurat analysis with reprocessed data.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,188 @@ | ||
| library(tidyverse) | ||
| library(mistyR) | ||
| library(rlist) | ||
| library(FNN) | ||
| library(future) | ||
| library(cowplot) | ||
| library(igraph) | ||
| library(ClusterR) | ||
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| plan(multisession, workers = 8) | ||
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| # MISTy ---- | ||
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| all.data <- read_tsv("molcart.misty_celltype_table.tsv") | ||
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| samples <- all.data %>% | ||
| pull(sample_ID) %>% | ||
| unique() | ||
| cts <- all.data %>% | ||
| pull(anno_cell_type_lv2) %>% | ||
| unique() | ||
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| cts.names <- make.names(cts, allow_ = FALSE) | ||
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| samples %>% walk(\(sample){ | ||
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| sample.cells <- all.data %>% | ||
| filter(sample_ID == sample) %>% | ||
| pull(anno_cell_type_lv2) %>% | ||
| map(~ .x == cts) %>% | ||
| list.rbind() %>% | ||
| `colnames<-`(cts.names) %>% | ||
| as_tibble() | ||
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| sample.pos <- all.data %>% | ||
| filter(sample_ID == sample) %>% | ||
| select(X_centroid, Y_centroid) | ||
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| #l <- ceiling(knn.dist(sample.pos, 1) %>% mean()) | ||
| #l <- 20/0.138 | ||
| #l <- 50/0.138 | ||
| l <- 100/0.138 | ||
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| misty.views.cts <- create_initial_view(sample.cells) %>% | ||
| add_paraview(sample.pos, l) %>% | ||
| rename_view(paste0("paraview.", l), "paraview") %>% | ||
| select_markers("intraview", where(~ sd(.) != 0)) | ||
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| run_misty(misty.views.cts, sample, "results_cts_100.sqm", bypass.intra = TRUE) | ||
| }) | ||
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| # Output ---- | ||
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| interaction_communities_info <- function(misty.results, concat.views, view, | ||
| trim = 0, trim.measure = "gain.R2", | ||
| cutoff = 1, resolution = 1) { | ||
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| inv <- sign((stringr::str_detect(trim.measure, "gain") | | ||
| stringr::str_detect(trim.measure, "RMSE", negate = TRUE)) - 0.5) | ||
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| targets <- misty.results$improvements.stats %>% | ||
| dplyr::filter( | ||
| measure == trim.measure, | ||
| inv * mean >= inv * trim | ||
| ) %>% | ||
| dplyr::pull(target) | ||
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| view.wide <- misty.results$importances.aggregated %>% | ||
| filter(view == !!view) %>% | ||
| pivot_wider( | ||
| names_from = "Target", values_from = "Importance", | ||
| id_cols = -c(view, nsamples) | ||
| ) %>% mutate(across(-c(Predictor,all_of(targets)), \(x) x = NA)) | ||
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| mistarget <- setdiff(view.wide$Predictor, colnames(view.wide)[-1]) | ||
| mispred <- setdiff(colnames(view.wide)[-1], view.wide$Predictor) | ||
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| if(length(mispred) != 0){ | ||
| view.wide.aug <- view.wide %>% add_row(Predictor = mispred) | ||
| } else { | ||
| view.wide.aug <- view.wide | ||
| } | ||
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| if(length(mistarget) != 0){ | ||
| view.wide.aug <- view.wide.aug %>% | ||
| bind_cols(mistarget %>% | ||
| map_dfc(~tibble(!!.x := NA))) | ||
| } | ||
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| A <- view.wide.aug %>% | ||
| column_to_rownames("Predictor") %>% | ||
| as.matrix() | ||
| A[A < cutoff | is.na(A)] <- 0 | ||
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| ## !!! Was buggy | ||
| G <- graph.adjacency(A[,rownames(A)], mode = "plus", weighted = TRUE) %>% | ||
| set.vertex.attribute("name", value = names(V(.))) %>% | ||
| delete.vertices(which(degree(.) == 0)) | ||
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| Gdir <- graph.adjacency(A[,rownames(A)], "directed", weighted = TRUE) %>% | ||
| set.vertex.attribute("name", value = names(V(.))) %>% | ||
| delete.vertices(which(degree(.) == 0)) | ||
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| C <- cluster_leiden(G, resolution_parameter = resolution, n_iterations = -1) | ||
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| mem <- membership(C) | ||
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| Gdir <- set_vertex_attr(Gdir, "community", names(mem), as.numeric(mem)) | ||
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| # careful here the first argument is the predictor and the second the target, | ||
| # it might need to come from different view | ||
| corrs <- as_edgelist(Gdir) %>% apply(1, \(x) cor( | ||
| concat.views[[view]][, x[1]], | ||
| concat.views[["intraview"]][, x[2]] | ||
| )) %>% replace_na(0) | ||
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| Gdir <- set_edge_attr(Gdir, "cor", value = corrs) | ||
| return(Gdir) | ||
| } | ||
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| groups <- samples %>% str_extract("(?<=sample_).+(?=_r)") %>% unique() | ||
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| misty.results.g <- groups %>% map(~ collect_results("results_cts_100.sqm", .x)) | ||
| names(misty.results.g) <- groups | ||
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| dir.create("misty_figures_100") | ||
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| misty.results.g %>% iwalk(\(misty.results, cond){ | ||
| plot.list <- list() | ||
| plot_improvement_stats(misty.results, "gain.R2") | ||
| plot.list <- list.append(plot.list, last_plot()) | ||
| plot_interaction_heatmap(misty.results, "paraview", cutoff = 0.5, clean = TRUE, trim = 5) | ||
| plot.list <- list.append(plot.list, last_plot()) | ||
| plot_grid(plotlist = plot.list, ncol = 2) | ||
| ggsave(paste0("misty_figures_100/", cond, "_stats.pdf"), width = 10, height = 10) | ||
| }) | ||
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| dir.create("graphs", recursive = TRUE) | ||
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| names(misty.results.g) %>% walk(\(g){ | ||
| concat.views <- samples %>% str_subset(g) %>% map(\(sample){ | ||
| sample.cells <- all.data %>% | ||
| filter(sample_ID == sample) %>% | ||
| pull(anno_cell_type_lv2) %>% | ||
| map(~ .x == cts) %>% | ||
| list.rbind() %>% | ||
| `colnames<-`(cts.names) %>% | ||
| as_tibble() | ||
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| sample.pos <- all.data %>% | ||
| filter(sample_ID == sample) %>% | ||
| select(X_centroid, Y_centroid) | ||
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| #l <- ceiling(knn.dist(sample.pos, 1) %>% mean()) | ||
| #l <- 20/0.138 | ||
| #l <- 50/0.138 | ||
| l <- 100/0.138 | ||
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| misty.views.cts <- create_initial_view(sample.cells) %>% | ||
| add_paraview(sample.pos, l) %>% | ||
| rename_view(paste0("paraview.", l), "paraview") %>% | ||
| select_markers("intraview", where(~ sd(.) != 0)) | ||
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| }) %>% list.clean() %>% reduce(\(acc, nxt){ | ||
| list( | ||
| intraview = bind_rows(acc[["intraview"]], nxt[["intraview"]]), | ||
| paraview = bind_rows(acc[["paraview"]], nxt[["paraview"]]) | ||
| ) | ||
| }, .init = list(intraview = NULL, paraview = NULL)) | ||
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| out <- interaction_communities_info(misty.results.g[[g]], concat.views, | ||
| view = "paraview", trim = 5, cutoff = 0.5) | ||
| write_graph(out, paste0("graphs/para.100.", g, ".graphml"), "graphml") | ||
| }) | ||
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| cellular_neighborhoods <- function(sample.cells, sample.pos, n, k){ | ||
| misty.views <- create_initial_view(sample.cells) %>% add_paraview(sample.pos, family = "constant", l = n) | ||
| clust <- KMeans_rcpp(misty.views[[paste0("paraview.",n)]], k) | ||
| return(clust$clusters) | ||
| } |
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