# HG changeset patch # User bgruening # Date 1644497629 0 # Node ID 10bf0f89035ffa6455277d4c1bfecd9aab368bc0 "planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit 8beed1a19fcd9dc59f7746e1dfa735a2d5f29784" diff -r 000000000000 -r 10bf0f89035f macros.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/macros.xml Thu Feb 10 12:53:49 2022 +0000 @@ -0,0 +1,35 @@ + + 3 + + 0.1.1 + rdata + + + + music-deconvolution + r-cowplot + r-reshape2 + + + + ^(([A-Za-z0-9+_ -]+)\s?,?)*$ + + + ^(([A-Za-z0-9+_ -]+)\s?)+$ + + + + + + + + ^(([A-Za-z0-9+_ -@.:/]+)\s?)*$ + + + diff -r 000000000000 -r 10bf0f89035f music_compare.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/music_compare.xml Thu Feb 10 12:53:49 2022 +0000 @@ -0,0 +1,208 @@ + + estimate and compare cell type proportions in multiple sets of bulk RNA-seq data + + macros.xml + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +null_str_vec = function(gstr){ + tokens = unlist(as.vector(strsplit(gstr, split=","))) + if (length(tokens) == 0){ + return(NULL) + } + if (length(tokens) == 1){ + return(tokens[[1]]) + } + return(tokens) +} + +files = list( +#for $s, $scgroup in enumerate($scrna_groups): + '$scgroup.name' = list( + dataset = '$scgroup.scrna_eset', + label_cell = null_str_vec('$scgroup.adv.celltypes_label'), + label_sample = null_str_vec('$scgroup.adv.samples_label'), + celltype = null_str_vec('$scgroup.adv.celltypes'), + bulk = list( + #for $b, $bulkgroup in enumerate($scgroup.bulk_groups): + '$bulkgroup.name' = list( + dataset = null_str_vec('$bulkgroup.bulk_eset'), + factor_group = null_str_vec('$bulkgroup.factor_group'), + pheno_facts = null_str_vec('$bulkgroup.adv.phenotype_factors'), + pheno_excl = null_str_vec('$bulkgroup.adv.phenotype_factors_always_exclude') + #if $b < len($scgroup.bulk_groups) - 1: + ), + #else + ) + #end if + #end for + #if $s < len($scrna_groups) - 1: + ) + ), + #else + ) + ) + #end if +#end for +) + +heat_grouped_p = as.logical('$heat_grouped_p') +out_filt = list(cells = null_str_vec('$filter.out_list_cells'), + facts = null_str_vec('$filter.out_list_facts')) +est_method = null_str_vec('$est_method') + +out_heatmulti_pdf = '$out_heatmulti_pdf' +out_heatsumm_pdf = '$out_heatsumm_pdf' + + + + + + + + +
+ + + + + + + +
+ + + + + +
+ + + + + + +
+
+
+ + + + + +
+ + +
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+ + + + + + + + + + + + + + + + +
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+ +
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+ + + + + + + + + + + + + + + + + + + + +
+ + + https://doi.org/10.1038/s41467-018-08023-x + +
\ No newline at end of file diff -r 000000000000 -r 10bf0f89035f scripts/compare.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scripts/compare.R Thu Feb 10 12:53:49 2022 +0000 @@ -0,0 +1,440 @@ +suppressWarnings(suppressPackageStartupMessages(library(xbioc))) +suppressWarnings(suppressPackageStartupMessages(library(MuSiC))) +suppressWarnings(suppressPackageStartupMessages(library(reshape2))) +suppressWarnings(suppressPackageStartupMessages(library(cowplot))) +## We use this script to estimate the effectiveness of proportion methods + +## Load Conf +args <- commandArgs(trailingOnly = TRUE) +source(args[1]) + +method_key <- list("MuSiC" = "est_music", + "NNLS" = "est_nnls")[[est_method]] + + +scale_yaxes <- function(gplot, value) { + if (is.na(value)) { + gplot + } else { + gplot + scale_y_continuous(lim = c(0, value)) + } +} + + +set_factor_data <- function(bulk_data, factor_name = NULL) { + if (is.null(factor_name)) { + factor_name <- "None" ## change to something plottable + } + pdat <- pData(bulk_data) + sam_fact <- NULL + if (factor_name %in% colnames(pdat)) { + sam_fact <- cbind(rownames(pdat), + as.character(pdat[[factor_name]])) + cat(paste0(" - factor: ", factor_name, + " found in phenotypes\n")) + } else { + ## We assign this as the factor for the entire dataset + sam_fact <- cbind(rownames(pdat), + factor_name) + cat(paste0(" - factor: assigning \"", factor_name, + "\" to whole dataset\n")) + } + colnames(sam_fact) <- c("Samples", "Factors") + return(as.data.frame(sam_fact)) +} + +## Due to limiting sizes, we need to load and unload +## possibly very large datasets. +process_pair <- function(sc_data, bulk_data, + ctypes_label, samples_label, ctypes, + factor_group) { + ## - Generate + est_prop <- music_prop( + bulk.eset = bulk_data, sc.eset = sc_data, + clusters = ctypes_label, + samples = samples_label, select.ct = ctypes, verbose = T) + ## - + estimated_music_props <- est_prop$Est.prop.weighted + estimated_nnls_props <- est_prop$Est.prop.allgene + ## - + fact_data <- set_factor_data(bulk_data, factor_group) + ## - + return(list(est_music = estimated_music_props, + est_nnls = estimated_nnls_props, + bulk_sample_totals = colSums(exprs(bulk_data)), + plot_groups = fact_data)) +} + +music_on_all <- function(files) { + results <- list() + for (sc_name in names(files)) { + cat(paste0("sc-group:", sc_name, "\n")) + scgroup <- files[[sc_name]] + ## - sc Data + sc_est <- readRDS(scgroup$dataset) + ## - params + celltypes_label <- scgroup$label_cell + samples_label <- scgroup$label_sample + celltypes <- scgroup$celltype + + results[[sc_name]] <- list() + for (bulk_name in names(scgroup$bulk)) { + cat(paste0(" - bulk-group:", bulk_name, "\n")) + bulkgroup <- scgroup$bulk[[bulk_name]] + ## - bulk Data + bulk_est <- readRDS(bulkgroup$dataset) + ## - bulk params + pheno_facts <- bulkgroup$pheno_facts + pheno_excl <- bulkgroup$pheno_excl + ## + results[[sc_name]][[bulk_name]] <- process_pair( + sc_est, bulk_est, + celltypes_label, samples_label, + celltypes, bulkgroup$factor_group) + ## + rm(bulk_est) ## unload + } + rm(sc_est) ## unload + } + return(results) +} + +plot_all_individual_heatmaps <- function(results) { + pdf(out_heatmulti_pdf, width = 8, height = 8) + for (sc_name in names(results)) { + for (bk_name in names(results[[sc_name]])) { + res <- results[[sc_name]][[bk_name]] + plot_hmap <- Prop_heat_Est( + data.matrix(res[[method_key]]), method.name = est_method) + + ggtitle(paste0("[", est_method, "Cell type ", + "proportions in ", + bk_name, " (Bulk) based on ", + sc_name, " (scRNA)")) + + xlab("Cell Types (scRNA)") + + ylab("Samples (Bulk)") + + theme(axis.text.x = element_text(angle = -90), + axis.text.y = element_text(size = 6)) + print(plot_hmap) + } + } + dev.off() +} + +merge_factors_spread <- function(grudat_spread, factor_groups) { + ## Generated + merge_it <- function(matr, plot_groups, valname) { + ren <- melt(lapply(matr, function(mat) { + mat["ct"] <- rownames(mat); return(mat)})) + ## - Grab factors and merge into list + ren_new <- merge(ren, plot_groups, by.x = "variable", by.y = "Samples") + colnames(ren_new) <- c("Sample", "Cell", valname, "Bulk", "Factors") + return(ren_new) + } + tab <- merge(merge_it(grudat$spread$prop, factor_groups, "value.prop"), + merge_it(grudat$spread$scale, factor_groups, "value.scale"), + by = c("Sample", "Cell", "Bulk", "Factors")) + return(tab) +} + + +plot_grouped_heatmaps <- function(results) { + pdf(out_heatmulti_pdf, width = 8, height = 8) + for (sc_name in names(results)) { + named_list <- sapply( + names(results[[sc_name]]), + function(n) { + ## We transpose the data here, because + ## the plotting function omits by default + ## the Y-axis which are the samples. + ## Since the celltypes are the common factor + ## these should be the Y-axis instead. + t(data.matrix(results[[sc_name]][[n]][[method_key]])) + }, simplify = F, USE.NAMES = T) + named_methods <- names(results[[sc_name]]) + ## + plot_hmap <- Prop_heat_Est( + named_list, + method.name = named_methods) + + ggtitle(paste0("[", est_method, "] Cell type ", + "proportions of ", + "Bulk Datasets based on ", + sc_name, " (scRNA)")) + + xlab("Samples (Bulk)") + + ylab("Cell Types (scRNA)") + + theme(axis.text.x = element_text(angle = -90), + axis.text.y = element_text(size = 6)) + print(plot_hmap) + } + dev.off() +} + +## Desired plots +## 1. Pie chart: +## - Per Bulk dataset (using just normalised proportions) +## - Per Bulk dataset (multiplying proportions by nreads) + +unlist_names <- function(results, method, prepend_bkname=FALSE) { + unique(sort( + unlist(lapply(names(results), function(scname) { + lapply(names(results[[scname]]), function(bkname) { + res <- get(method)(results[[scname]][[bkname]][[method_key]]) + if (prepend_bkname) { + ## We *do not* assume unique bulk sample names + ## across different bulk datasets. + res <- paste0(bkname, "::", res) + } + return(res) + }) + })) + )) +} + +summarized_matrix <- function(results) { # nolint + ## We assume that cell types MUST be unique, but that sample + ## names do not need to be. For this reason, we must prepend + ## the bulk dataset name to the individual sample names. + all_celltypes <- unlist_names(results, "colnames") + all_samples <- unlist_names(results, "rownames", prepend_bkname = TRUE) + + ## Iterate through all possible samples and populate a table. + ddff <- data.frame() + ddff_scale <- data.frame() + for (cell in all_celltypes) { + for (sample in all_samples) { + group_sname <- unlist(strsplit(sample, split = "::")) + bulk <- group_sname[1] + id_sample <- group_sname[2] + for (scgroup in names(results)) { + if (bulk %in% names(results[[scgroup]])) { + mat_prop <- results[[scgroup]][[bulk]][[method_key]] + vec_counts <- results[[scgroup]][[bulk]]$bulk_sample_totals + ## - We use sample instead of id_sample because we need to + ## extract bulk sets from the complete matrix later. It's + ## messy, yes. + if (cell %in% colnames(mat_prop)) { + ddff[cell, sample] <- mat_prop[id_sample, cell] + ddff_scale[cell, sample] <- mat_prop[id_sample, cell] * vec_counts[[id_sample]] #nolint + } else { + ddff[cell, sample] <- 0 + ddff_scale[cell, sample] <- 0 + } + } + } + } + } + return(list(prop = ddff, scaled = ddff_scale)) +} + +flatten_factor_list <- function(results) { + ## Get a 2d DF of all factors across all bulk samples. + res <- c() + for (scgroup in names(results)) { + for (bulkgroup in names(results[[scgroup]])) { + dat <- results[[scgroup]][[bulkgroup]]$plot_groups + dat$Samples <- paste0(bulkgroup, "::", dat$Samples) #nolint + res <- rbind(res, dat) + } + } + return(res) +} + +group_by_dataset <- function(summat) { + bulk_names <- unlist( + lapply(names(files), function(x) names(files[[x]]$bulk))) + mat_names <- colnames(summat$prop) + bd <- list() + bd_scale <- list() + bd_spread_scale <- list() + bd_spread_prop <- list() + for (bname in bulk_names) { + subs <- mat_names[startsWith(mat_names, paste0(bname, "::"))] + ## - + bd[[bname]] <- rowSums(summat$prop[, subs]) + bd_scale[[bname]] <- rowSums(summat$scaled[, subs]) + bd_spread_scale[[bname]] <- summat$scaled[, subs] + bd_spread_prop[[bname]] <- summat$prop[, subs] + } + return(list(prop = as.data.frame(bd), + scaled = as.data.frame(bd_scale), + spread = list(scale = bd_spread_scale, + prop = bd_spread_prop))) +} + +summarize_heatmaps <- function(grudat_spread_melt, do_factors) { + ## - + do_single <- function(grudat_melted, yaxis, xaxis, fillval, title, + ylabs = element_blank(), xlabs = element_blank(), + use_log = TRUE, size = 11) { + ## Convert from matrix to long format + melted <- grudat_melted ## copy? + if (use_log) { + melted[[fillval]] <- log10(melted[[fillval]] + 1) + } + return(ggplot(melted) + + geom_tile(aes_string(y = yaxis, x = xaxis, fill = fillval), + colour = "white") + + scale_fill_gradient2(low = "steelblue", high = "red", + mid = "white", name = element_blank()) + + theme(axis.text.x = element_text(angle = -90, hjust = 0, + size = size)) + + ggtitle(label = title) + xlab(xlabs) + ylab(ylabs)) + } + + do_gridplot <- function(title, xvar, plot="both", ncol=2, size = 11) { + do_logged <- (plot %in% c("log", "both")) + do_normal <- (plot %in% c("normal", "both")) + plist <- list() + if (do_logged) { + plist[["1"]] <- do_single(grudat_spread_melt, "Cell", xvar, + "value.scale", "Reads (log10+1)", + size = size) + plist[["2"]] <- do_single(grudat_spread_melt, "Cell", xvar, + "value.prop", "Sample (log10+1)", + size = size) + } + if (do_normal) { + plist[["A"]] <- do_single(grudat_spread_melt, "Cell", xvar, + "value.scale", "Reads", use_log = F, + size = size) + plist[["B"]] <- do_single(grudat_spread_melt, "Cell", xvar, + "value.prop", "Sample", use_log = F, + size = size) + } + return(plot_grid(ggdraw() + draw_label(title, fontface = "bold"), + plot_grid(plotlist = plist, ncol = ncol), + ncol = 1, rel_heights = c(0.05, 0.95))) + + } + p1 <- do_gridplot("Cell Types vs Bulk Datasets", "Bulk", "both", ) + p2a <- do_gridplot("Cell Types vs Samples", "Sample", "normal", 1, + size = 8) + p2b <- do_gridplot("Cell Types vs Samples (log10+1)", "Sample", "log", 1, + size = 8) + p3 <- ggplot + theme_void() + if (do_factors) { + p3 <- do_gridplot("Cell Types against Factors", "Factors", "both") + } + return(list(bulk = p1, + samples = list(log = p2b, normal = p2a), + factors = p3)) +} + +summarize_boxplots <- function(grudat_spread, do_factors) { + common1 <- ggplot(grudat_spread, aes(x = value.prop)) + ggtitle("Sample") + + xlab(element_blank()) + ylab(element_blank()) + common2 <- ggplot(grudat_spread, aes(x = value.scale)) + ggtitle("Reads") + + xlab(element_blank()) + ylab(element_blank()) + + A <- B <- list() #nolint + ## Cell type by sample + A$p1 <- common2 + geom_boxplot(aes(y = Cell, color = Bulk)) + A$p2 <- common1 + geom_boxplot(aes(y = Cell, color = Bulk)) + ## Sample by Cell type + B$p1 <- common2 + geom_boxplot(aes(y = Bulk, color = Cell)) + + ylab("Bulk Dataset") + B$p2 <- common1 + geom_boxplot(aes(y = Bulk, color = Cell)) + + ylab("Bulk Dataset") + ## -- Factor plots are optional + A$p3 <- B$p3 <- A$p4 <- B$p4 <- ggplot() + theme_void() + + if (do_factors) { + A$p3 <- common1 + geom_boxplot(aes(y = Cell, color = Factors)) + A$p4 <- common2 + geom_boxplot(aes(y = Cell, color = Factors)) + B$p3 <- common1 + geom_boxplot(aes(y = Bulk, color = Factors)) + + ylab("Bulk Dataset") + B$p4 <- common2 + geom_boxplot(aes(y = Bulk, color = Factors)) + + ylab("Bulk Dataset") + } + + title_a <- "Cell Types against Bulk" + title_b <- "Bulk Datasets against Cells" + if (do_factors) { + title_a <- paste0(title_a, " and Factors") + title_b <- paste0(title_b, " and Factors") + } + + a_all <- plot_grid(ggdraw() + draw_label(title_a, fontface = "bold"), + plot_grid(plotlist = A, ncol = 2), + ncol = 1, rel_heights = c(0.05, 0.95)) + b_all <- plot_grid(ggdraw() + draw_label(title_b, fontface = "bold"), + plot_grid(plotlist = B, ncol = 2), + ncol = 1, rel_heights = c(0.05, 0.95)) + return(list(cell = a_all, bulk = b_all)) +} + +filter_output <- function(grudat_spread_melt, out_filt) { + print_red <- function(comment, red_list) { + cat(paste(comment, paste(red_list, collapse = ", "), "\n")) + } + grudat_filt <- grudat_spread_melt + print_red("Total Cell types:", unique(grudat_filt$Cell)) + if (!is.null(out_filt$cells)) { + grudat_filt <- grudat_filt[grudat_filt$Cell %in% out_filt$cells, ] + print_red(" - selecting:", out_filt$cells) + } + print_red("Total Factors:", unique(grudat_spread_melt$Factors)) + if (!is.null(out_filt$facts)) { + grudat_filt <- grudat_filt[grudat_filt$Factors %in% out_filt$facts, ] + print_red(" - selecting:", out_filt$facts) + } + return(grudat_filt) +} + + +results <- music_on_all(files) + +if (heat_grouped_p) { + plot_grouped_heatmaps(results) +} else { + plot_all_individual_heatmaps(results) +} + +save.image("/tmp/sesh.rds") + +summat <- summarized_matrix(results) +grudat <- group_by_dataset(summat) +grudat_spread_melt <- merge_factors_spread(grudat$spread, + flatten_factor_list(results)) + + + +## The output filters ONLY apply to boxplots, since these take +do_factors <- (length(unique(grudat_spread_melt[["Factors"]])) > 1) + +grudat_spread_melt_filt <- filter_output(grudat_spread_melt, out_filt) + +heat_maps <- summarize_heatmaps(grudat_spread_melt_filt, do_factors) +box_plots <- summarize_boxplots(grudat_spread_melt_filt, do_factors) + +pdf(out_heatsumm_pdf, width = 14, height = 14) +print(heat_maps) +print(box_plots) +dev.off() + +## Generate output tables +stats_prop <- lapply(grudat$spread$prop, function(x) { + t(apply(x, 1, summary))}) +stats_scale <- lapply(grudat$spread$scale, function(x) { + t(apply(x, 1, summary))}) + +writable2 <- function(obj, prefix, title) { + write.table(obj, + file = paste0("report_data/", prefix, "_", + title, ".tabular"), + quote = F, sep = "\t", col.names = NA) +} +## Make the value table printable +grudat_spread_melt$value.scale <- as.integer(grudat_spread_melt$value.scale) # nolint +colnames(grudat_spread_melt) <- c("Sample", "Cell", "Bulk", "Factors", + "CT Prop in Sample", "Number of Reads") + +writable2(grudat_spread_melt, "values", "Data Table") +writable2(summat$prop, "values", "Matrix of Cell Type Sample Proportions") +writable2({ + aa <- as.matrix(summat$scaled); mode(aa) <- "integer"; aa +}, "values", "Matrix of Cell Type Read Counts") + +for (bname in names(stats_prop)) { + writable2(stats_prop[[bname]], "stats", paste0(bname, ": Sample Props")) + writable2(stats_scale[[bname]], "stats", paste0(bname, ": Read Props")) +} diff -r 000000000000 -r 10bf0f89035f scripts/dendrogram.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scripts/dendrogram.R Thu Feb 10 12:53:49 2022 +0000 @@ -0,0 +1,132 @@ +## +suppressWarnings(suppressPackageStartupMessages(library(xbioc))) +suppressWarnings(suppressPackageStartupMessages(library(MuSiC))) +suppressWarnings(suppressPackageStartupMessages(library(reshape2))) +suppressWarnings(suppressPackageStartupMessages(library(cowplot))) +## We use this script to generate a clustering dendrogram of cell +## types, using the prior labelling from scRNA. + +read_list <- function(lfile) { + if (lfile == "None") { + return(NULL) + } + return(read.table(file = lfile, header = FALSE, check.names = FALSE, + stringsAsFactors = FALSE)$V1) +} + +args <- commandArgs(trailingOnly = TRUE) +source(args[1]) + + +## Perform the estimation +## Produce the first step information +sub.basis <- music_basis(scrna_eset, clusters = celltypes_label, + samples = samples_label, + select.ct = celltypes) + +## Plot the dendrogram of design matrix and cross-subject mean of +## realtive abundance +## Hierarchical clustering using Complete Linkage +d1 <- dist(t(log(sub.basis$Disgn.mtx + 1e-6)), method = "euclidean") +hc1 <- hclust(d1, method = "complete") +## Hierarchical clustering using Complete Linkage +d2 <- dist(t(log(sub.basis$M.theta + 1e-8)), method = "euclidean") +hc2 <- hclust(d2, method = "complete") + + +if (length(data.to.use) > 0) { + ## We then perform bulk tissue cell type estimation with pre-grouping + ## of cell types: C, list_of_cell_types, marker genes name, marker + ## genes list. + ## data.to.use = list( + ## "C1" = list(cell.types = c("Neutro"), + ## marker.names=NULL, + ## marker.list=NULL), + ## "C2" = list(cell.types = c("Podo"), + ## marker.names=NULL, + ## marker.list=NULL), + ## "C3" = list(cell.types = c("Endo","CD-PC","LOH","CD-IC","DCT","PT"), + ## marker.names = "Epithelial", + ## marker.list = read_list("../test-data/epith.markers")), + ## "C4" = list(cell.types = c("Macro","Fib","B lymph","NK","T lymph"), + ## marker.names = "Immune", + ## marker.list = read_list("../test-data/immune.markers")) + ## ) + grouped_celltypes <- lapply(data.to.use, function(x) { + x$cell.types + }) + marker_groups <- lapply(data.to.use, function(x) { + x$marker.list + }) + names(marker_groups) <- names(data.to.use) + + + cl_type <- as.character(scrna_eset[[celltypes_label]]) + + for (cl in seq_len(length(grouped_celltypes))) { + cl_type[cl_type %in% + grouped_celltypes[[cl]]] <- names(grouped_celltypes)[cl] + } + pData(scrna_eset)[[clustertype_label]] <- factor( + cl_type, levels = c(names(grouped_celltypes), + "CD-Trans", "Novel1", "Novel2")) + + est_bulk <- music_prop.cluster( + bulk.eset = bulk_eset, sc.eset = scrna_eset, + group.markers = marker_groups, clusters = celltypes_label, + groups = clustertype_label, samples = samples_label, + clusters.type = grouped_celltypes + ) + + estimated_music_props <- est_bulk$Est.prop.weighted.cluster + ## NNLS is not calculated here + + ## Show different in estimation methods + ## Jitter plot of estimated cell type proportions + methods_list <- c("MuSiC") + + jitter_fig <- Jitter_Est( + list(data.matrix(estimated_music_props)), + method.name = methods_list, title = "Jitter plot of Est Proportions", + size = 2, alpha = 0.7) + + theme_minimal() + + labs(x = element_blank(), y = element_blank()) + + theme(axis.text = element_text(size = 6), + axis.text.x = element_blank(), + legend.position = "none") + + plot_box <- Boxplot_Est(list( + data.matrix(estimated_music_props)), + method.name = methods_list) + + theme_minimal() + + labs(x = element_blank(), y = element_blank()) + + theme(axis.text = element_text(size = 6), + axis.text.x = element_blank(), + legend.position = "none") + + plot_hmap <- Prop_heat_Est(list( + data.matrix(estimated_music_props)), + method.name = methods_list) + + labs(x = element_blank(), y = element_blank()) + + theme(axis.text.y = element_text(size = 6), + axis.text.x = element_text(angle = -90, size = 5), + plot.title = element_text(size = 9), + legend.key.width = unit(0.15, "cm"), + legend.text = element_text(size = 5), + legend.title = element_text(size = 5)) + +} + +pdf(file = outfile_pdf, width = 8, height = 8) +par(mfrow = c(1, 2)) +plot(hc1, cex = 0.6, hang = -1, main = "Cluster log(Design Matrix)") +plot(hc2, cex = 0.6, hang = -1, main = "Cluster log(Mean of RA)") +if (length(data.to.use) > 0) { + plot_grid(jitter_fig, plot_box, plot_hmap, ncol = 2, nrow = 2) +} +message(dev.off()) + +if (length(data.to.use) > 0) { + write.table(estimated_music_props, + file = outfile_tab, quote = F, col.names = NA, sep = "\t") +} diff -r 000000000000 -r 10bf0f89035f scripts/estimateprops.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scripts/estimateprops.R Thu Feb 10 12:53:49 2022 +0000 @@ -0,0 +1,250 @@ +suppressWarnings(suppressPackageStartupMessages(library(xbioc))) +suppressWarnings(suppressPackageStartupMessages(library(MuSiC))) +suppressWarnings(suppressPackageStartupMessages(library(reshape2))) +suppressWarnings(suppressPackageStartupMessages(library(cowplot))) +## We use this script to estimate the effectiveness of proportion methods + +## Load Conf +args <- commandArgs(trailingOnly = TRUE) +source(args[1]) + +## Estimate cell type proportions +est_prop <- music_prop( + bulk.eset = bulk_eset, sc.eset = scrna_eset, + clusters = celltypes_label, + samples = samples_label, select.ct = celltypes, verbose = T) + + +estimated_music_props <- est_prop$Est.prop.weighted +estimated_nnls_props <- est_prop$Est.prop.allgene +## +estimated_music_props_flat <- melt(estimated_music_props) +estimated_nnls_props_flat <- melt(estimated_nnls_props) + +scale_yaxes <- function(gplot, value) { + if (is.na(value)) { + gplot + } else { + gplot + scale_y_continuous(lim = c(0, value)) + } +} + +sieve_data <- function(func, music_data, nnls_data) { + if (func == "list") { + res <- list(if ("MuSiC" %in% methods) music_data else NULL, + if ("NNLS" %in% methods) nnls_data else NULL) + res[lengths(res) > 0] ## filter out NULL elements + } else if (func == "rbind") { + rbind(if ("MuSiC" %in% methods) music_data else NULL, + if ("NNLS" %in% methods) nnls_data else NULL) + } else if (func == "c") { + c(if ("MuSiC" %in% methods) music_data else NULL, + if ("NNLS" %in% methods) nnls_data else NULL) + } +} + + +## Show different in estimation methods +## Jitter plot of estimated cell type proportions +jitter_fig <- scale_yaxes(Jitter_Est( + sieve_data("list", + data.matrix(estimated_music_props), + data.matrix(estimated_nnls_props)), + method.name = methods, title = "Jitter plot of Est Proportions", + size = 2, alpha = 0.7) + theme_minimal(), maxyscale) + +## Make a Plot +## A more sophisticated jitter plot is provided as below. We separated +## the T2D subjects and normal subjects by their disease factor levels. +m_prop <- sieve_data("rbind", + estimated_music_props_flat, + estimated_nnls_props_flat) +colnames(m_prop) <- c("Sub", "CellType", "Prop") + +if (is.null(celltypes)) { + celltypes <- levels(m_prop$CellType) + message("No celltypes declared, using:") + message(celltypes) +} + +if (is.null(phenotype_factors)) { + phenotype_factors <- colnames(pData(bulk_eset)) +} +## filter out unwanted factors like "sampleID" and "subjectName" +phenotype_factors <- phenotype_factors[ + !(phenotype_factors %in% phenotype_factors_always_exclude)] +message("Phenotype Factors to use:") +message(paste0(phenotype_factors, collapse = ", ")) + +m_prop$CellType <- factor(m_prop$CellType, levels = celltypes) # nolint +m_prop$Method <- factor(rep(methods, each = nrow(estimated_music_props_flat)), # nolint + levels = methods) + +if (use_disease_factor) { + + if (phenotype_target_threshold == -99) { + phenotype_target_threshold <- -Inf + message("phenotype target threshold set to -Inf") + } + ## the "2" here is to do with the sample groups, not number of methods + m_prop$Disease_factor <- rep(bulk_eset[[phenotype_target]], 2 * length(celltypes)) # nolint + m_prop <- m_prop[!is.na(m_prop$Disease_factor), ] + ## Generate a TRUE/FALSE table of Normal == 1 and Disease == 2 + sample_groups <- c("Normal", sample_disease_group) + m_prop$Disease <- factor(sample_groups[(m_prop$Disease_factor > phenotype_target_threshold) + 1], # nolint + levels = sample_groups) + + ## Binary to scale: e.g. TRUE / 5 = 0.2 + m_prop$D <- (m_prop$Disease == # nolint + sample_disease_group) / sample_disease_group_scale + ## NA's are not included in the comparison below + m_prop <- rbind(subset(m_prop, Disease != sample_disease_group), + subset(m_prop, Disease == sample_disease_group)) + + jitter_new <- scale_yaxes( + ggplot(m_prop, aes(Method, Prop)) + + geom_point(aes(fill = Method, color = Disease, + stroke = D, shape = Disease), + size = 2, alpha = 0.7, + position = position_jitter(width = 0.25, height = 0)) + + facet_wrap(~ CellType, scales = "free") + + scale_colour_manual(values = c("white", "gray20")) + + scale_shape_manual(values = c(21, 24)) + theme_minimal(), maxyscale) + +} + +if (use_disease_factor) { + + ## Plot to compare method effectiveness + ## Create dataframe for beta cell proportions and Disease_factor levels + ## - Ugly code. Essentially, doubles the cell type proportions for each + ## set of MuSiC and NNLS methods + m_prop_ana <- data.frame( + pData(bulk_eset)[rep(1:nrow(estimated_music_props), length(methods)), #nolint + phenotype_factors], + ## get proportions of target cell type + ct.prop = sieve_data("c", + estimated_music_props[, phenotype_scrna_target], + estimated_nnls_props[, phenotype_scrna_target]), + ## + Method = factor(rep(methods, + each = nrow(estimated_music_props)), + levels = methods)) + ## - fix headers + colnames(m_prop_ana)[1:length(phenotype_factors)] <- phenotype_factors #nolint + ## - drop NA for target phenotype (e.g. hba1c) + m_prop_ana <- subset(m_prop_ana, !is.na(m_prop_ana[phenotype_target])) + m_prop_ana$Disease <- factor( # nolint + ## - Here we set Normal/Disease assignments across the methods + sample_groups[( + m_prop_ana[phenotype_target] > phenotype_target_threshold) + 1 + ], + sample_groups) + ## - Then we scale this binary assignment to a plotable factor + m_prop_ana$D <- (m_prop_ana$Disease == # nolint + sample_disease_group) / sample_disease_group_scale + + jitt_compare <- scale_yaxes( + ggplot(m_prop_ana, aes_string(phenotype_target, "ct.prop")) + + geom_smooth(method = "lm", se = FALSE, col = "black", lwd = 0.25) + + geom_point(aes(fill = Method, color = Disease, + stroke = D, shape = Disease), + size = 2, alpha = 0.7) + facet_wrap(~ Method) + + ggtitle(paste0(toupper(phenotype_target), " vs. ", + toupper(phenotype_scrna_target), + " Cell Type Proportion")) + + theme_minimal() + + ylab(paste0("Proportion of ", + phenotype_scrna_target, " cells")) + + xlab(paste0("Level of bulk factor (", phenotype_target, ")")) + + scale_colour_manual(values = c("white", "gray20")) + + scale_shape_manual(values = c(21, 24)), maxyscale) +} + +## BoxPlot +plot_box <- scale_yaxes(Boxplot_Est( + sieve_data("list", + data.matrix(estimated_music_props), + data.matrix(estimated_nnls_props)), + method.name = methods) + + theme(axis.text.x = element_text(angle = -90), + axis.text.y = element_text(size = 8)) + + ggtitle(element_blank()) + theme_minimal(), maxyscale) + +## Heatmap +plot_hmap <- Prop_heat_Est( + sieve_data( + "list", + data.matrix(estimated_music_props), + data.matrix(estimated_nnls_props)), + method.name = methods) + + theme(axis.text.x = element_text(angle = -90), + axis.text.y = element_text(size = 6)) + +pdf(file = outfile_pdf, width = 8, height = 8) +if (length(celltypes) <= 8) { + plot_grid(jitter_fig, plot_box, labels = "auto", ncol = 1, nrow = 2) +} else { + print(jitter_fig) + plot_box +} +if (use_disease_factor) { + plot_grid(jitter_new, jitt_compare, labels = "auto", ncol = 1, nrow = 2) +} +plot_hmap +message(dev.off()) + +writable <- function(obj, prefix, title) { + write.table(obj, + file = paste0("report_data/", prefix, "_", + title, ".tabular"), + quote = F, sep = "\t", col.names = NA) +} + +## Output Proportions +if ("NNLS" %in% methods) { + writable(est_prop$Est.prop.allgene, "prop", + "NNLS Estimated Proportions of Cell Types") +} + +if ("MuSiC" %in% methods) { + writable(est_prop$Est.prop.weighted, "prop", + "Music Estimated Proportions of Cell Types") + writable(est_prop$Weight.gene, "weightgene", + "Music Estimated Proportions of Cell Types (by Gene)") + writable(est_prop$r.squared.full, "rsquared", + "Music R-sqr Estimated Proportions of Each Subject") + writable(est_prop$Var.prop, "varprop", + "Matrix of Variance of MuSiC Estimates") +} + + +if (use_disease_factor) { + ## Summary table of linear regressions of disease factors + for (meth in methods) { + ##lm_beta_meth = lm(ct.prop ~ age + bmi + hba1c + gender, data = + sub_data <- subset(m_prop_ana, Method == meth) + + ## We can only do regression where there are more than 1 factors + ## so we must find and exclude the ones which are not + gt1_facts <- sapply(phenotype_factors, function(facname) { + return(length(unique(sort(sub_data[[facname]]))) == 1) + }) + form_factors <- phenotype_factors + exclude_facts <- names(gt1_facts)[gt1_facts] + if (length(exclude_facts) > 0) { + message("Factors with only one level will be excluded:") + message(exclude_facts) + form_factors <- phenotype_factors[ + !(phenotype_factors %in% exclude_facts)] + } + lm_beta_meth <- lm(as.formula( + paste("ct.prop", paste(form_factors, collapse = " + "), + sep = " ~ ")), data = sub_data) + message(paste0("Summary: ", meth)) + capture.output(summary(lm_beta_meth), + file = paste0("report_data/summ_Log of ", + meth, + " fitting.txt")) + } +} diff -r 000000000000 -r 10bf0f89035f test-data/EMTABesethealthy.subset.rds Binary file test-data/EMTABesethealthy.subset.rds has changed diff -r 000000000000 -r 10bf0f89035f test-data/GSE50244bulkeset.subset.rds Binary file test-data/GSE50244bulkeset.subset.rds has changed diff -r 000000000000 -r 10bf0f89035f test-data/Mousebulkeset.rds Binary file test-data/Mousebulkeset.rds has changed diff -r 000000000000 -r 10bf0f89035f test-data/Mousesubeset.degenesonly2.half.rds Binary file test-data/Mousesubeset.degenesonly2.half.rds has changed diff -r 000000000000 -r 10bf0f89035f test-data/array.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/array.tsv Thu Feb 10 12:53:49 2022 +0000 @@ -0,0 +1,4 @@ + Sample1 Sample2 +Gene1 1 3 +Gene2 2 4 +Gene3 3 5 diff -r 000000000000 -r 10bf0f89035f test-data/default_output.pdf Binary file test-data/default_output.pdf has changed diff -r 000000000000 -r 10bf0f89035f test-data/default_output_no_disease.pdf Binary file test-data/default_output_no_disease.pdf has changed diff -r 000000000000 -r 10bf0f89035f test-data/default_output_no_disease_nnls.pdf Binary file test-data/default_output_no_disease_nnls.pdf has changed diff -r 000000000000 -r 10bf0f89035f test-data/dendro.pdf Binary file test-data/dendro.pdf has changed diff -r 000000000000 -r 10bf0f89035f test-data/dendro_1.pdf Binary file test-data/dendro_1.pdf has changed diff -r 000000000000 -r 10bf0f89035f test-data/epith.markers --- /dev/null Thu Jan 01 00:00:00 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7 Mouse7 3 PT +CCGTTCAGTTGAACTC-7 7 Mouse7 7 CD-IC +GTGTTAGTCAGCTCGG-1 1 Mouse1 4 LOH +GGATTACGTGTGCGTC-6 6 Mouse6 3 PT +TACGGTATCCGTTGTC-6 6 Mouse6 3 PT +TTAGTTCGTATTAGCC-5 5 Mouse5 3 PT +CCCAATCGTAGCGATG-3 3 Mouse3 3 PT +ACACCAATCTGCGTAA-7 7 Mouse7 5 DCT +AATCCAGTCCAAACTG-7 7 Mouse7 5 DCT +CAGAATCAGCAATATG-1 1 Mouse1 3 PT +GCACATAAGCCGGTAA-5 5 Mouse5 5 DCT +CCTTCCCAGGAGTTTA-5 5 Mouse5 3 PT +CGGAGCTAGGACTGGT-5 5 Mouse5 4 LOH +TACGGATGTAAATGTG-4 4 Mouse4 3 PT +GGCAATTCATTCACTT-2 2 Mouse2 3 PT +CTCGGGAGTCTGCGGT-4 4 Mouse4 6 CD-PC +CATTCGCGTCCTCTTG-2 2 Mouse2 8 CD-Trans +CGCGTTTAGATCGATA-1 1 Mouse1 6 CD-PC +GGGTTGCCACCAACCG-4 4 Mouse4 7 CD-IC +TGTGTTTCATCGATGT-2 2 Mouse2 3 PT +AGAGTGGAGCTGTTCA-7 7 Mouse7 3 PT +CTCACACGTCTCACCT-3 3 Mouse3 3 PT +AGTTGGTTCCACGAAT-7 7 Mouse7 3 PT +ATCTGCCAGACCACGA-6 6 Mouse6 3 PT +TGTATTCCATTGAGCT-7 7 Mouse7 3 PT +TGAAAGAGTAGCCTAT-7 7 Mouse7 3 PT +AAATGCCAGAACTGTA-7 7 Mouse7 5 DCT +TTTGCGCTCTACCAGA-4 4 Mouse4 3 PT +ACATACGGTTTCCACC-6 6 Mouse6 3 PT +GCCTCTAGTTCCACAA-7 7 Mouse7 3 PT +GGGAGATGTACTCTCC-6 6 Mouse6 1 Endo +GAACGGATCTTGTACT-7 7 Mouse7 3 PT +TACCTTATCCTAGAAC-1 1 Mouse1 3 PT +GCGCGATAGATGCCAG-2 2 Mouse2 3 PT +GACAGAGCAAGTTGTC-7 7 Mouse7 3 PT +TGACTAGGTATGAATG-3 3 Mouse3 4 LOH +CACACTCAGTCACGCC-6 6 Mouse6 3 PT +ATTGGTGGTTAGGGTG-5 5 Mouse5 3 PT +AGCAGCCCAGCGTAAG-2 2 Mouse2 1 Endo +CATTCGCAGCCTTGAT-6 6 Mouse6 3 PT +GCGAGAACATAGACTC-2 2 Mouse2 14 T lymph +AGTCTTTGTAATAGCA-7 7 Mouse7 3 PT +TCGCGAGCAGACACTT-7 7 Mouse7 3 PT +CGGAGTCCAGCAGTTT-2 2 Mouse2 3 PT +GGTGTTACACACATGT-7 7 Mouse7 3 PT +TTCTCAAGTAAGTGTA-2 2 Mouse2 1 Endo +TGCTGCTAGTCAATAG-2 2 Mouse2 3 PT +GATGAGGTCTACCAGA-2 2 Mouse2 3 PT +ACATACGGTTGTACAC-5 5 Mouse5 3 PT +ACGAGGACAGCTATTG-7 7 Mouse7 4 LOH +CGATGTATCGGCGGTT-2 2 Mouse2 3 PT +CTGCGGATCACAACGT-2 2 Mouse2 13 B lymph +CGAACATAGTTGAGTA-5 5 Mouse5 3 PT +TAGTTGGTCGCGATCG-6 6 Mouse6 5 DCT +GCAGCCACAATGTAAG-4 4 Mouse4 1 Endo +CTCACACCAATAACGA-7 7 Mouse7 3 PT +CCTTTCTCATGAAGTA-2 2 Mouse2 7 CD-IC +AGTGTCAAGAGCAATT-7 7 Mouse7 3 PT +AGCATACGTAAAGGAG-1 1 Mouse1 6 CD-PC +ACACCAATCTCGCTTG-5 5 Mouse5 3 PT +GGGATGAGTATCAGTC-6 6 Mouse6 3 PT +TGACAACAGAAGCCCA-2 2 Mouse2 3 PT +CGAATGTTCACAATGC-1 1 Mouse1 1 Endo +GCACTCTTCCGCATAA-1 1 Mouse1 3 PT +CACCAGGTCCCAAGAT-2 2 Mouse2 3 PT +GTTACAGCACCGCTAG-6 6 Mouse6 3 PT +TAGCCGGCAGTACACT-2 2 Mouse2 11 Macro +ACGATGTGTTAAAGAC-2 2 Mouse2 9 Novel1 +CCTTCCCAGTCTCGGC-7 7 Mouse7 3 PT +TAGTGGTTCTCTGTCG-7 7 Mouse7 7 CD-IC +TAGCCGGAGGCTAGCA-5 5 Mouse5 7 CD-IC +TTGTAGGTCAGCACAT-1 1 Mouse1 4 LOH +GAATAAGCAGCTTCGG-7 7 Mouse7 3 PT +TCGCGAGAGTCCGGTC-3 3 Mouse3 14 T lymph +TCAACGAAGAGTAAGG-2 2 Mouse2 5 DCT +CAGGTGCCACGAAATA-5 5 Mouse5 3 PT +TGTGTTTCACTATCTT-2 2 Mouse2 3 PT +TGGCCAGAGTGAAGAG-6 6 Mouse6 3 PT +ACCAGTAAGTAGCCGA-2 2 Mouse2 6 CD-PC +GCGGGTTAGAAGGTTT-1 1 Mouse1 3 PT +CAGTCCTGTCATTAGC-2 2 Mouse2 7 CD-IC diff -r 000000000000 -r 10bf0f89035f test-data/out_filt1.pdf Binary file test-data/out_filt1.pdf has changed diff -r 000000000000 -r 10bf0f89035f test-data/out_heat2.pdf Binary file test-data/out_heat2.pdf has changed diff -r 000000000000 -r 10bf0f89035f test-data/pheno.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/pheno.tsv Thu Feb 10 12:53:49 2022 +0000 @@ -0,0 +1,3 @@ + gender type score +Sample1 1 3 5 +Sample2 2 4 9