Mercurial > repos > bgruening > music_deconvolution

annotate scripts/dendrogram.R @ 0:224721e76869 draft

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"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit 08c6fd3885bdfbf8b5c3f4dcc2d04729b577e3e1"
author bgruening
date Sun, 12 Sep 2021 19:48:48 +0000
parents
children 3ca0132c182a
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224721e76869 "planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit 08c6fd3885bdfbf8b5c3f4dcc2d04729b577e3e1"
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1 ##
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2 suppressWarnings(suppressPackageStartupMessages(library(xbioc)))
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3 suppressWarnings(suppressPackageStartupMessages(library(MuSiC)))
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4 suppressWarnings(suppressPackageStartupMessages(library(reshape2)))
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5 suppressWarnings(suppressPackageStartupMessages(library(cowplot)))
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6 ## We use this script to generate a clustering dendrogram of cell
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7 ## types, using the prior labelling from scRNA.
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8
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9 read_list <- function(lfile) {
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10 if (lfile == "None") {
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11 return(NULL)
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12 }
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13 return(read.table(file = lfile, header = FALSE,
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14 stringsAsFactors = FALSE)$V1)
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15 }
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16
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17 args <- commandArgs(trailingOnly = TRUE)
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18 source(args[1])
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20 ## We then perform bulk tissue cell type estimation with pre-grouping
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21 ## of cell types: C, list_of_cell_types, marker genes name, marker
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22 ## genes list.
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23 ## data.to.use = list(
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24 ## "C1" = list(cell.types = c("Neutro"),
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25 ## marker.names=NULL,
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26 ## marker.list=NULL),
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27 ## "C2" = list(cell.types = c("Podo"),
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28 ## marker.names=NULL,
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29 ## marker.list=NULL),
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30 ## "C3" = list(cell.types = c("Endo","CD-PC","LOH","CD-IC","DCT","PT"),
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31 ## marker.names = "Epithelial",
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32 ## marker.list = read_list("../test-data/epith.markers")),
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33 ## "C4" = list(cell.types = c("Macro","Fib","B lymph","NK","T lymph"),
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34 ## marker.names = "Immune",
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35 ## marker.list = read_list("../test-data/immune.markers"))
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36 ## )
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37 grouped_celltypes <- lapply(data.to.use, function(x) {
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38 x$cell.types
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39 })
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40 marker_groups <- lapply(data.to.use, function(x) {
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41 x$marker.list
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42 })
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43 names(marker_groups) <- names(data.to.use)
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44
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45
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46 ## Perform the estimation
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47 ## Produce the first step information
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48 sub.basis <- music_basis(scrna_eset, clusters = celltypes_label,
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49 samples = samples_label,
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50 select.ct = celltypes)
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51
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52 ## Plot the dendrogram of design matrix and cross-subject mean of
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53 ## realtive abundance
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54 par(mfrow = c(1, 2))
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55 d <- dist(t(log(sub.basis$Disgn.mtx + 1e-6)), method = "euclidean")
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56 ## Hierarchical clustering using Complete Linkage
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57 hc1 <- hclust(d, method = "complete")
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58 ## Plot the obtained dendrogram
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59 plot(hc1, cex = 0.6, hang = -1, main = "Cluster log(Design Matrix)")
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60 d <- dist(t(log(sub.basis$M.theta + 1e-8)), method = "euclidean")
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61 ## Hierarchical clustering using Complete Linkage
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62 hc2 <- hclust(d, method = "complete")
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63 ## Plot the obtained dendrogram
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64 pdf(file = outfile_pdf, width = 8, height = 8)
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65 plot(hc2, cex = 0.6, hang = -1, main = "Cluster log(Mean of RA)")
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66
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67 cl_type <- as.character(scrna_eset[[celltypes_label]])
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68
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69 for (cl in seq_len(length(grouped_celltypes))) {
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70 cl_type[cl_type %in% grouped_celltypes[[cl]]] <- names(grouped_celltypes)[cl]
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71 }
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72 pData(scrna_eset)[[clustertype_label]] <- factor(
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73 cl_type, levels = c(names(grouped_celltypes),
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74 "CD-Trans", "Novel1", "Novel2"))
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75
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76 est_bulk <- music_prop.cluster(
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77 bulk.eset = bulk_eset, sc.eset = scrna_eset,
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78 group.markers = marker_groups, clusters = celltypes_label,
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79 groups = clustertype_label, samples = samples_label,
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80 clusters.type = grouped_celltypes)
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81
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82 write.table(est_bulk, file = outfile_tab, quote = F, col.names = NA, sep = "\t")
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83 dev.off()