Mercurial > repos > bgruening > music_inspect_eset

annotate scripts/dendrogram.R @ 2:577435e5e6b2 draft

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"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit 20f8561478535013e111d982b99639f48f1bea79"
author bgruening
date Sat, 29 Jan 2022 12:51:27 +0000
parents 817eb707bbf4
children
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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, check.names = FALSE,
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14 stringsAsFactors = FALSE)$V1)
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15 }
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17 args <- commandArgs(trailingOnly = TRUE)
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18 source(args[1])
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21 ## Perform the estimation
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22 ## Produce the first step information
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23 sub.basis <- music_basis(scrna_eset, clusters = celltypes_label,
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24 samples = samples_label,
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25 select.ct = celltypes)
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27 ## Plot the dendrogram of design matrix and cross-subject mean of
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28 ## realtive abundance
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29 ## Hierarchical clustering using Complete Linkage
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30 d1 <- dist(t(log(sub.basis$Disgn.mtx + 1e-6)), method = "euclidean")
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31 hc1 <- hclust(d1, method = "complete")
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32 ## Hierarchical clustering using Complete Linkage
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33 d2 <- dist(t(log(sub.basis$M.theta + 1e-8)), method = "euclidean")
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34 hc2 <- hclust(d2, method = "complete")
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35
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36
1
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37 if (length(data.to.use) > 0) {
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38 ## We then perform bulk tissue cell type estimation with pre-grouping
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39 ## of cell types: C, list_of_cell_types, marker genes name, marker
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40 ## genes list.
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41 ## data.to.use = list(
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42 ## "C1" = list(cell.types = c("Neutro"),
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43 ## marker.names=NULL,
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44 ## marker.list=NULL),
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45 ## "C2" = list(cell.types = c("Podo"),
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46 ## marker.names=NULL,
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47 ## marker.list=NULL),
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48 ## "C3" = list(cell.types = c("Endo","CD-PC","LOH","CD-IC","DCT","PT"),
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49 ## marker.names = "Epithelial",
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50 ## marker.list = read_list("../test-data/epith.markers")),
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51 ## "C4" = list(cell.types = c("Macro","Fib","B lymph","NK","T lymph"),
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52 ## marker.names = "Immune",
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53 ## marker.list = read_list("../test-data/immune.markers"))
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54 ## )
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55 grouped_celltypes <- lapply(data.to.use, function(x) {
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56 x$cell.types
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57 })
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58 marker_groups <- lapply(data.to.use, function(x) {
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59 x$marker.list
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60 })
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61 names(marker_groups) <- names(data.to.use)
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62
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63
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64 cl_type <- as.character(scrna_eset[[celltypes_label]])
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65
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66 for (cl in seq_len(length(grouped_celltypes))) {
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67 cl_type[cl_type %in%
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68 grouped_celltypes[[cl]]] <- names(grouped_celltypes)[cl]
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69 }
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70 pData(scrna_eset)[[clustertype_label]] <- factor(
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71 cl_type, levels = c(names(grouped_celltypes),
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72 "CD-Trans", "Novel1", "Novel2"))
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73
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74 est_bulk <- music_prop.cluster(
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75 bulk.eset = bulk_eset, sc.eset = scrna_eset,
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76 group.markers = marker_groups, clusters = celltypes_label,
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77 groups = clustertype_label, samples = samples_label,
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78 clusters.type = grouped_celltypes
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79 )
0
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80
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81 estimated_music_props <- est_bulk$Est.prop.weighted.cluster
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82 ## NNLS is not calculated here
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83
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84 ## Show different in estimation methods
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85 ## Jitter plot of estimated cell type proportions
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86 methods_list <- c("MuSiC")
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87
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88 jitter_fig <- Jitter_Est(
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89 list(data.matrix(estimated_music_props)),
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90 method.name = methods_list, title = "Jitter plot of Est Proportions",
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91 size = 2, alpha = 0.7) +
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92 theme_minimal() +
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93 labs(x = element_blank(), y = element_blank()) +
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94 theme(axis.text = element_text(size = 6),
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95 axis.text.x = element_blank(),
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96 legend.position = "none")
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97
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98 plot_box <- Boxplot_Est(list(
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99 data.matrix(estimated_music_props)),
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100 method.name = methods_list) +
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101 theme_minimal() +
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102 labs(x = element_blank(), y = element_blank()) +
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103 theme(axis.text = element_text(size = 6),
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104 axis.text.x = element_blank(),
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105 legend.position = "none")
0
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106
1
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107 plot_hmap <- Prop_heat_Est(list(
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108 data.matrix(estimated_music_props)),
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109 method.name = methods_list) +
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110 labs(x = element_blank(), y = element_blank()) +
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111 theme(axis.text.y = element_text(size = 6),
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112 axis.text.x = element_text(angle = -90, size = 5),
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113 plot.title = element_text(size = 9),
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114 legend.key.width = unit(0.15, "cm"),
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115 legend.text = element_text(size = 5),
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116 legend.title = element_text(size = 5))
0
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117
1
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118 }
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119
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120 pdf(file = outfile_pdf, width = 8, height = 8)
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121 par(mfrow = c(1, 2))
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122 plot(hc1, cex = 0.6, hang = -1, main = "Cluster log(Design Matrix)")
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123 plot(hc2, cex = 0.6, hang = -1, main = "Cluster log(Mean of RA)")
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124 if (length(data.to.use) > 0) {
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125 plot_grid(jitter_fig, plot_box, plot_hmap, ncol = 2, nrow = 2)
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126 }
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127 message(dev.off())
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128
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129 if (length(data.to.use) > 0) {
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130 write.table(estimated_music_props,
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131 file = outfile_tab, quote = F, col.names = NA, sep = "\t")
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132 }