diff scripts/dendrogram.R @ 0:2fed32b5aa02 draft

"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit 08c6fd3885bdfbf8b5c3f4dcc2d04729b577e3e1"

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/scripts/dendrogram.R	Sun Sep 12 19:48:26 2021 +0000
@@ -0,0 +1,83 @@
+##
+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,
+                      stringsAsFactors = FALSE)$V1)
+}
+
+args <- commandArgs(trailingOnly = TRUE)
+source(args[1])
+
+## We then perform bulk tissue cell type estimation with pre-grouping
+## of cell types: C, list_of_cell_types, marker genes name, marker
+## genes list.
+## data.to.use = list(
+##     "C1" = list(cell.types = c("Neutro"),
+##                 marker.names=NULL,
+##                 marker.list=NULL),
+##     "C2" = list(cell.types = c("Podo"),
+##                 marker.names=NULL,
+##                 marker.list=NULL),
+##     "C3" = list(cell.types = c("Endo","CD-PC","LOH","CD-IC","DCT","PT"),
+##                 marker.names = "Epithelial",
+##                 marker.list = read_list("../test-data/epith.markers")),
+##     "C4" = list(cell.types = c("Macro","Fib","B lymph","NK","T lymph"),
+##                 marker.names = "Immune",
+##                 marker.list = read_list("../test-data/immune.markers"))
+## )
+grouped_celltypes <- lapply(data.to.use, function(x) {
+    x$cell.types
+})
+marker_groups <- lapply(data.to.use, function(x) {
+    x$marker.list
+})
+names(marker_groups) <- names(data.to.use)
+
+
+## Perform the estimation
+## Produce the first step information
+sub.basis <- music_basis(scrna_eset, clusters = celltypes_label,
+                         samples = samples_label,
+                         select.ct = celltypes)
+
+## Plot the dendrogram of design matrix and cross-subject mean of
+## realtive abundance
+par(mfrow = c(1, 2))
+d <- dist(t(log(sub.basis$Disgn.mtx + 1e-6)), method = "euclidean")
+## Hierarchical clustering using Complete Linkage
+hc1 <- hclust(d, method = "complete")
+## Plot the obtained dendrogram
+plot(hc1, cex = 0.6, hang = -1, main = "Cluster log(Design Matrix)")
+d <- dist(t(log(sub.basis$M.theta + 1e-8)), method = "euclidean")
+## Hierarchical clustering using Complete Linkage
+hc2 <- hclust(d, method = "complete")
+## Plot the obtained dendrogram
+pdf(file = outfile_pdf, width = 8, height = 8)
+plot(hc2, cex = 0.6, hang = -1, main = "Cluster log(Mean of RA)")
+
+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)
+
+write.table(est_bulk, file = outfile_tab, quote = F, col.names = NA, sep = "\t")
+dev.off()