Mercurial > repos > bgruening > music_construct_eset

annotate scripts/estimateprops.R @ 2:7902cd31b9b5 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 12:52:10 +0000
parents be91cb6f48e7
children 7ffaa0968da3
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1 suppressWarnings(suppressPackageStartupMessages(library(xbioc)))
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2 suppressWarnings(suppressPackageStartupMessages(library(MuSiC)))
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3 suppressWarnings(suppressPackageStartupMessages(library(reshape2)))
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4 suppressWarnings(suppressPackageStartupMessages(library(cowplot)))
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5 ## We use this script to estimate the effectiveness of proportion methods
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7 ## Load Conf
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8 args <- commandArgs(trailingOnly = TRUE)
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9 source(args[1])
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11 ## Estimate cell type proportions
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12 est_prop <- music_prop(
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13 bulk.eset = bulk_eset, sc.eset = scrna_eset,
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14 clusters = celltypes_label,
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15 samples = samples_label, select.ct = celltypes, verbose = T)
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17 estimated_music_props <- est_prop$Est.prop.weighted
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18 estimated_nnls_props <- est_prop$Est.prop.allgene
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20 scale_yaxes <- function(gplot, value) {
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21 if (is.na(value)) {
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22 gplot
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23 } else {
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24 gplot + scale_y_continuous(lim = c(0, value))
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25 }
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26 }
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27
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28 ## Show different in estimation methods
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29 ## Jitter plot of estimated cell type proportions
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30 jitter_fig <- scale_yaxes(Jitter_Est(
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31 list(data.matrix(estimated_music_props),
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32 data.matrix(estimated_nnls_props)),
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33 method.name = methods, title = "Jitter plot of Est Proportions",
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34 size = 2, alpha = 0.7) + theme_minimal(), maxyscale)
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37 ## Make a Plot
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38 ## A more sophisticated jitter plot is provided as below. We separated
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39 ## the T2D subjects and normal subjects by their disease factor levels.
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40 estimated_music_props_flat <- melt(estimated_music_props)
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41 estimated_nnls_props_flat <- melt(estimated_nnls_props)
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43 m_prop <- rbind(estimated_music_props_flat,
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44 estimated_nnls_props_flat)
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45 colnames(m_prop) <- c("Sub", "CellType", "Prop")
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47 if (is.null(celltypes)) {
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48 celltypes <- levels(m_prop$CellType)
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49 message("No celltypes declared, using:")
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50 message(celltypes)
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51 }
0
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52
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53 if (is.null(phenotype_factors)) {
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54 phenotype_factors <- colnames(pData(bulk_eset))
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55 }
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56 ## filter out unwanted factors like "sampleID" and "subjectName"
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57 phenotype_factors <- phenotype_factors[
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58 !(phenotype_factors %in% phenotype_factors_always_exclude)]
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59 message("Phenotype Factors to use:")
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60 message(paste0(phenotype_factors, collapse = ", "))
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62 m_prop$CellType <- factor(m_prop$CellType, levels = celltypes) # nolint
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63 m_prop$Method <- factor(rep(methods, each = nrow(estimated_music_props_flat)), # nolint
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64 levels = methods)
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65
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66 if (use_disease_factor) {
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67
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68 if (phenotype_target_threshold == -99) {
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69 phenotype_target_threshold <- -Inf
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70 message("phenotype target threshold set to -Inf")
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71 }
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73 m_prop$Disease_factor <- rep(bulk_eset[[phenotype_target]], 2 * length(celltypes)) # nolint
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74 m_prop <- m_prop[!is.na(m_prop$Disease_factor), ]
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75 ## Generate a TRUE/FALSE table of Normal == 1 and Disease == 2
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76 sample_groups <- c("Normal", sample_disease_group)
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77 m_prop$Disease <- factor(sample_groups[(m_prop$Disease_factor > phenotype_target_threshold) + 1], # nolint
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78 levels = sample_groups)
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79
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80 ## Binary to scale: e.g. TRUE / 5 = 0.2
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81 m_prop$D <- (m_prop$Disease == # nolint
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82 sample_disease_group) / sample_disease_group_scale
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83 ## NA's are not included in the comparison below
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84 m_prop <- rbind(subset(m_prop, Disease != sample_disease_group),
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85 subset(m_prop, Disease == sample_disease_group))
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86
2
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87 jitter_new <- scale_yaxes(ggplot(m_prop, aes(Method, Prop)) +
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88 geom_point(aes(fill = Method, color = Disease, stroke = D, shape = Disease),
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89 size = 2, alpha = 0.7,
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90 position = position_jitter(width = 0.25, height = 0)) +
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91 facet_wrap(~ CellType, scales = "free") +
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92 scale_colour_manual(values = c("white", "gray20")) +
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93 scale_shape_manual(values = c(21, 24)) + theme_minimal(), maxyscale)
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94
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95 }
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96
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97 if (use_disease_factor) {
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98
2
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99 ## Plot to compare method effectiveness
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100 ## Create dataframe for beta cell proportions and Disease_factor levels
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101 ## - Ugly code. Essentially, doubles the cell type proportions for each
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102 ## set of MuSiC and NNLS methods
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103 m_prop_ana <- data.frame(pData(bulk_eset)[rep(1:nrow(estimated_music_props), 2), #nolint
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104 phenotype_factors],
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105 ## get proportions of target cell type
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106 ct.prop = c(estimated_music_props[, phenotype_scrna_target],
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107 estimated_nnls_props[, phenotype_scrna_target]),
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108 ##
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109 Method = factor(rep(methods,
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110 each = nrow(estimated_music_props)),
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111 levels = methods))
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112 ## - fix headers
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113 colnames(m_prop_ana)[1:length(phenotype_factors)] <- phenotype_factors #nolint
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114 ## - drop NA for target phenotype (e.g. hba1c)
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115 m_prop_ana <- subset(m_prop_ana, !is.na(m_prop_ana[phenotype_target]))
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116 m_prop_ana$Disease <- factor( # nolint
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117 ## - Here we set Normal/Disease assignments across the two MuSiC and NNLS methods
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118 sample_groups[(
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119 m_prop_ana[phenotype_target] > phenotype_target_threshold) + 1
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120 ],
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121 sample_groups)
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122 ## - Then we scale this binary assignment to a plotable factor
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123 m_prop_ana$D <- (m_prop_ana$Disease == # nolint
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124 sample_disease_group) / sample_disease_group_scale
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125
2
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126 jitt_compare <- scale_yaxes(ggplot(m_prop_ana, aes_string(phenotype_target, "ct.prop")) +
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127 geom_smooth(method = "lm", se = FALSE, col = "black", lwd = 0.25) +
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128 geom_point(aes(fill = Method, color = Disease, stroke = D, shape = Disease),
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129 size = 2, alpha = 0.7) + facet_wrap(~ Method) +
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130 ggtitle(paste0(toupper(phenotype_target), " vs. ",
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131 toupper(phenotype_scrna_target), " Cell Type Proportion")) +
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132 theme_minimal() +
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133 ylab(paste0("Proportion of ",
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134 phenotype_scrna_target, " cells")) +
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135 xlab(paste0("Level of bulk factor (", phenotype_target, ")")) +
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136 scale_colour_manual(values = c("white", "gray20")) +
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137 scale_shape_manual(values = c(21, 24)), maxyscale)
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138 }
0
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139
1
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140 ## BoxPlot
2
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141 plot_box <- scale_yaxes(Boxplot_Est(list(
1
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142 data.matrix(estimated_music_props),
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143 data.matrix(estimated_nnls_props)),
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144 method.name = c("MuSiC", "NNLS")) +
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145 theme(axis.text.x = element_text(angle = -90),
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146 axis.text.y = element_text(size = 8)) +
2
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147 ggtitle(element_blank()) + theme_minimal(), maxyscale)
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148
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149 ## Heatmap
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150 plot_hmap <- Prop_heat_Est(list(
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151 data.matrix(estimated_music_props),
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152 data.matrix(estimated_nnls_props)),
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153 method.name = c("MuSiC", "NNLS")) +
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154 theme(axis.text.x = element_text(angle = -90),
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155 axis.text.y = element_text(size = 6))
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156
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157 pdf(file = outfile_pdf, width = 8, height = 8)
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158 if (length(celltypes) <= 8) {
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159 plot_grid(jitter_fig, plot_box, labels = "auto", ncol = 1, nrow = 2)
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160 } else {
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161 print(jitter_fig)
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162 plot_box
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163 }
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164 if (use_disease_factor) {
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165 plot_grid(jitter_new, jitt_compare, labels = "auto", ncol = 1, nrow = 2)
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166 }
1
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167 plot_hmap
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168 message(dev.off())
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169
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170 ## Output Proportions
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171
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172 write.table(est_prop$Est.prop.weighted,
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173 file = paste0("report_data/prop_",
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174 "Music Estimated Proportions of Cell Types",
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175 ".tabular"),
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176 quote = F, sep = "\t", col.names = NA)
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177 write.table(est_prop$Est.prop.allgene,
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178 file = paste0("report_data/prop_",
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179 "NNLS Estimated Proportions of Cell Types",
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180 ".tabular"),
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181 quote = F, sep = "\t", col.names = NA)
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182 write.table(est_prop$Weight.gene,
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183 file = paste0("report_data/weightgene_",
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184 "Music Estimated Proportions of Cell Types (by Gene)",
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185 ".tabular"),
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186 quote = F, sep = "\t", col.names = NA)
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187 write.table(est_prop$r.squared.full,
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188 file = paste0("report_data/rsquared_",
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189 "Music R-sqr Estimated Proportions of Each Subject",
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190 ".tabular"),
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191 quote = F, sep = "\t", col.names = NA)
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192 write.table(est_prop$Var.prop,
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193 file = paste0("report_data/varprop_",
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194 "Matrix of Variance of MuSiC Estimates",
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195 ".tabular"),
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196 quote = F, sep = "\t", col.names = NA)
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197
0
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198
2
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199 if (use_disease_factor) {
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200 ## Summary table of linear regressions of disease factors
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201 for (meth in methods) {
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202 ##lm_beta_meth = lm(ct.prop ~ age + bmi + hba1c + gender, data =
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203 sub_data <- subset(m_prop_ana, Method == meth)
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204
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205 ## We can only do regression where there are more than 1 factors
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206 ## so we must find and exclude the ones which are not
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207 gt1_facts <- sapply(phenotype_factors, function(facname) {
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208 return(length(unique(sort(sub_data[[facname]]))) == 1)
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209 })
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210 form_factors <- phenotype_factors
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211 exclude_facts <- names(gt1_facts)[gt1_facts]
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212 if (length(exclude_facts) > 0) {
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213 message("Factors with only one level will be excluded:")
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214 message(exclude_facts)
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215 form_factors <- phenotype_factors[
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216 !(phenotype_factors %in% exclude_facts)]
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217 }
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218 lm_beta_meth <- lm(as.formula(
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219 paste("ct.prop", paste(form_factors, collapse = " + "),
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220 sep = " ~ ")), data = sub_data)
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221 message(paste0("Summary: ", meth))
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222 capture.output(summary(lm_beta_meth),
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223 file = paste0("report_data/summ_Log of ",
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224 meth,
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225 " fitting.txt"))
1
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226 }
0
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227 }