## 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()