Mercurial > repos > bgruening > music_deconvolution

annotate scripts/dendrogram.R.orig @ 6:fb36f390cc52 draft

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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d5c7ca22af1d4f0eaa7a607886554bebb95e8c50
author bgruening
date Mon, 28 Oct 2024 17:32:19 +0000
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fb36f390cc52 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d5c7ca22af1d4f0eaa7a607886554bebb95e8c50
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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 <<<<<<< HEAD
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14 return(read.table(file = lfile, header = FALSE, check.names = FALSE,
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15 =======
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16 return(read.table(file = lfile, header = FALSE, check.names=FALSE,
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17 >>>>>>> 768a6e5b (v3 update:)
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18 stringsAsFactors = FALSE)$V1)
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19 }
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21 args <- commandArgs(trailingOnly = TRUE)
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22 source(args[1])
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25 ## Perform the estimation
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26 ## Produce the first step information
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27 sub.basis <- music_basis(scrna_eset, clusters = celltypes_label,
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28 samples = samples_label,
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29 select.ct = celltypes)
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30
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31 ## Plot the dendrogram of design matrix and cross-subject mean of
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32 ## realtive abundance
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33 ## Hierarchical clustering using Complete Linkage
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34 d1 <- dist(t(log(sub.basis$Disgn.mtx + 1e-6)), method = "euclidean")
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35 hc1 <- hclust(d1, method = "complete")
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36 ## Hierarchical clustering using Complete Linkage
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37 d2 <- dist(t(log(sub.basis$M.theta + 1e-8)), method = "euclidean")
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38 hc2 <- hclust(d2, method = "complete")
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39
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40
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41 if (length(data.to.use) > 0) {
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42 ## We then perform bulk tissue cell type estimation with pre-grouping
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43 ## of cell types: C, list_of_cell_types, marker genes name, marker
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44 ## genes list.
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45 ## data.to.use = list(
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46 ## "C1" = list(cell.types = c("Neutro"),
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47 ## marker.names=NULL,
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48 ## marker.list=NULL),
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49 ## "C2" = list(cell.types = c("Podo"),
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50 ## marker.names=NULL,
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51 ## marker.list=NULL),
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52 ## "C3" = list(cell.types = c("Endo","CD-PC","LOH","CD-IC","DCT","PT"),
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53 ## marker.names = "Epithelial",
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54 ## marker.list = read_list("../test-data/epith.markers")),
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55 ## "C4" = list(cell.types = c("Macro","Fib","B lymph","NK","T lymph"),
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56 ## marker.names = "Immune",
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57 ## marker.list = read_list("../test-data/immune.markers"))
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58 ## )
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59 grouped_celltypes <- lapply(data.to.use, function(x) {
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60 x$cell.types
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61 })
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62 marker_groups <- lapply(data.to.use, function(x) {
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63 x$marker.list
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64 })
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65 names(marker_groups) <- names(data.to.use)
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66
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67
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68 cl_type <- as.character(scrna_eset[[celltypes_label]])
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69
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70 for (cl in seq_len(length(grouped_celltypes))) {
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71 cl_type[cl_type %in%
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72 grouped_celltypes[[cl]]] <- names(grouped_celltypes)[cl]
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73 }
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74 pData(scrna_eset)[[clustertype_label]] <- factor(
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75 cl_type, levels = c(names(grouped_celltypes),
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76 "CD-Trans", "Novel1", "Novel2"))
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77
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78 est_bulk <- music_prop.cluster(
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79 bulk.eset = bulk_eset, sc.eset = scrna_eset,
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80 group.markers = marker_groups, clusters = celltypes_label,
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81 groups = clustertype_label, samples = samples_label,
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82 clusters.type = grouped_celltypes
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83 )
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84
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85 estimated_music_props <- est_bulk$Est.prop.weighted.cluster
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86 ## NNLS is not calculated here
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87
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88 ## Show different in estimation methods
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89 ## Jitter plot of estimated cell type proportions
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90 methods_list <- c("MuSiC")
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91
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92 jitter_fig <- Jitter_Est(
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93 list(data.matrix(estimated_music_props)),
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94 method.name = methods_list, title = "Jitter plot of Est Proportions",
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95 size = 2, alpha = 0.7) +
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96 theme_minimal() +
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97 labs(x = element_blank(), y = element_blank()) +
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98 theme(axis.text = element_text(size = 6),
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99 axis.text.x = element_blank(),
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100 legend.position = "none")
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101
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102 plot_box <- Boxplot_Est(list(
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103 data.matrix(estimated_music_props)),
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104 method.name = methods_list) +
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105 theme_minimal() +
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106 labs(x = element_blank(), y = element_blank()) +
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107 theme(axis.text = element_text(size = 6),
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108 axis.text.x = element_blank(),
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109 legend.position = "none")
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110
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111 plot_hmap <- Prop_heat_Est(list(
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112 data.matrix(estimated_music_props)),
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113 method.name = methods_list) +
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114 labs(x = element_blank(), y = element_blank()) +
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115 theme(axis.text.y = element_text(size = 6),
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116 axis.text.x = element_text(angle = -90, size = 5),
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117 plot.title = element_text(size = 9),
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118 legend.key.width = unit(0.15, "cm"),
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119 legend.text = element_text(size = 5),
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120 legend.title = element_text(size = 5))
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121
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122 }
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123
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124 pdf(file = outfile_pdf, width = 8, height = 8)
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125 par(mfrow = c(1, 2))
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126 plot(hc1, cex = 0.6, hang = -1, main = "Cluster log(Design Matrix)")
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127 plot(hc2, cex = 0.6, hang = -1, main = "Cluster log(Mean of RA)")
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128 if (length(data.to.use) > 0) {
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129 plot_grid(jitter_fig, plot_box, plot_hmap, ncol = 2, nrow = 2)
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130 }
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131 message(dev.off())
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132
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133 if (length(data.to.use) > 0) {
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134 write.table(estimated_music_props,
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135 file = outfile_tab, quote = F, col.names = NA, sep = "\t")
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136 }