# 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?)+$
+
+
+
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+
+
+ ^(([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
+
+
+
+
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+
+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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+
+ 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
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diff -r 000000000000 -r 10bf0f89035f test-data/GSE50244bulkeset.subset.rds
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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
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diff -r 000000000000 -r 10bf0f89035f test-data/default_output_no_disease.pdf
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diff -r 000000000000 -r 10bf0f89035f test-data/default_output_no_disease_nnls.pdf
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diff -r 000000000000 -r 10bf0f89035f test-data/dendro.pdf
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diff -r 000000000000 -r 10bf0f89035f test-data/dendro_1.pdf
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diff -r 000000000000 -r 10bf0f89035f test-data/epith.markers
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/epith.markers Thu Feb 10 12:53:49 2022 +0000
@@ -0,0 +1,2614 @@
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diff -r 000000000000 -r 10bf0f89035f test-data/immune.markers
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/immune.markers Thu Feb 10 12:53:49 2022 +0000
@@ -0,0 +1,8126 @@
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diff -r 000000000000 -r 10bf0f89035f test-data/mouse_scrna_exprs.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/mouse_scrna_exprs.tabular Thu Feb 10 12:53:49 2022 +0000
@@ -0,0 +1,101 @@
+ TGGTTCCGTCGGCTCA-2 CGAGCCAAGCGTCAAG-4 GAATGAAGTTTGGGCC-5 CTCGTACGTTGCCTCT-7 TTCTCAATCCACGCAG-5 CCTTCCCCATACCATG-4 ACTTTCACAGCTGGCT-7 TGGGAAGCAAAGTGCG-7 TCTATTGAGTAGGCCA-7 TCGGTAACATCACGTA-2 GGGTTGCCAGCTGTAT-2 TGCGGGTGTCATATCG-6 ACTTGTTTCATATCGG-5 CCAATCCCACGGCGTT-2 CTAAGACCACCAGGCT-7 TTAACTCAGTAGGCCA-6 GTACTCCGTAACGCGA-1 GCCTCTAGTTGTACAC-2 TTCGAAGTCCTGCAGG-3 TTCTACAAGTTGTAGA-7 CCGTTCAGTTGAACTC-7 GTGTTAGTCAGCTCGG-1 GGATTACGTGTGCGTC-6 TACGGTATCCGTTGTC-6 TTAGTTCGTATTAGCC-5 CCCAATCGTAGCGATG-3 ACACCAATCTGCGTAA-7 AATCCAGTCCAAACTG-7 CAGAATCAGCAATATG-1 GCACATAAGCCGGTAA-5 CCTTCCCAGGAGTTTA-5 CGGAGCTAGGACTGGT-5 TACGGATGTAAATGTG-4 GGCAATTCATTCACTT-2 CTCGGGAGTCTGCGGT-4 CATTCGCGTCCTCTTG-2 CGCGTTTAGATCGATA-1 GGGTTGCCACCAACCG-4 TGTGTTTCATCGATGT-2 AGAGTGGAGCTGTTCA-7 CTCACACGTCTCACCT-3 AGTTGGTTCCACGAAT-7 ATCTGCCAGACCACGA-6 TGTATTCCATTGAGCT-7 TGAAAGAGTAGCCTAT-7 AAATGCCAGAACTGTA-7 TTTGCGCTCTACCAGA-4 ACATACGGTTTCCACC-6 GCCTCTAGTTCCACAA-7 GGGAGATGTACTCTCC-6 GAACGGATCTTGTACT-7 TACCTTATCCTAGAAC-1 GCGCGATAGATGCCAG-2 GACAGAGCAAGTTGTC-7 TGACTAGGTATGAATG-3 CACACTCAGTCACGCC-6 ATTGGTGGTTAGGGTG-5 AGCAGCCCAGCGTAAG-2 CATTCGCAGCCTTGAT-6 GCGAGAACATAGACTC-2 AGTCTTTGTAATAGCA-7 TCGCGAGCAGACACTT-7 CGGAGTCCAGCAGTTT-2 GGTGTTACACACATGT-7 TTCTCAAGTAAGTGTA-2 TGCTGCTAGTCAATAG-2 GATGAGGTCTACCAGA-2 ACATACGGTTGTACAC-5 ACGAGGACAGCTATTG-7 CGATGTATCGGCGGTT-2 CTGCGGATCACAACGT-2 CGAACATAGTTGAGTA-5 TAGTTGGTCGCGATCG-6 GCAGCCACAATGTAAG-4 CTCACACCAATAACGA-7 CCTTTCTCATGAAGTA-2 AGTGTCAAGAGCAATT-7 AGCATACGTAAAGGAG-1 ACACCAATCTCGCTTG-5 GGGATGAGTATCAGTC-6 TGACAACAGAAGCCCA-2 CGAATGTTCACAATGC-1 GCACTCTTCCGCATAA-1 CACCAGGTCCCAAGAT-2 GTTACAGCACCGCTAG-6 TAGCCGGCAGTACACT-2 ACGATGTGTTAAAGAC-2 CCTTCCCAGTCTCGGC-7 TAGTGGTTCTCTGTCG-7 TAGCCGGAGGCTAGCA-5 TTGTAGGTCAGCACAT-1 GAATAAGCAGCTTCGG-7 TCGCGAGAGTCCGGTC-3 TCAACGAAGAGTAAGG-2 CAGGTGCCACGAAATA-5 TGTGTTTCACTATCTT-2 TGGCCAGAGTGAAGAG-6 ACCAGTAAGTAGCCGA-2 GCGGGTTAGAAGGTTT-1 CAGTCCTGTCATTAGC-2
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+Actr1b 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 1 2 0 0 0 0 1 0 0 2 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0
+Zap70 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+Tmem131 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+Inpp4a 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
+Coa5 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
diff -r 000000000000 -r 10bf0f89035f test-data/mouse_scrna_pheno.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/mouse_scrna_pheno.tabular Thu Feb 10 12:53:49 2022 +0000
@@ -0,0 +1,101 @@
+ sampleID SubjectName cellTypeID cellType
+TGGTTCCGTCGGCTCA-2 2 Mouse2 3 PT
+CGAGCCAAGCGTCAAG-4 4 Mouse4 5 DCT
+GAATGAAGTTTGGGCC-5 5 Mouse5 3 PT
+CTCGTACGTTGCCTCT-7 7 Mouse7 3 PT
+TTCTCAATCCACGCAG-5 5 Mouse5 4 LOH
+CCTTCCCCATACCATG-4 4 Mouse4 14 T lymph
+ACTTTCACAGCTGGCT-7 7 Mouse7 3 PT
+TGGGAAGCAAAGTGCG-7 7 Mouse7 3 PT
+TCTATTGAGTAGGCCA-7 7 Mouse7 3 PT
+TCGGTAACATCACGTA-2 2 Mouse2 3 PT
+GGGTTGCCAGCTGTAT-2 2 Mouse2 2 Podo
+TGCGGGTGTCATATCG-6 6 Mouse6 3 PT
+ACTTGTTTCATATCGG-5 5 Mouse5 14 T lymph
+CCAATCCCACGGCGTT-2 2 Mouse2 5 DCT
+CTAAGACCACCAGGCT-7 7 Mouse7 3 PT
+TTAACTCAGTAGGCCA-6 6 Mouse6 3 PT
+GTACTCCGTAACGCGA-1 1 Mouse1 3 PT
+GCCTCTAGTTGTACAC-2 2 Mouse2 3 PT
+TTCGAAGTCCTGCAGG-3 3 Mouse3 3 PT
+TTCTACAAGTTGTAGA-7 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