Mercurial > repos > bgruening > music_inspect_eset

annotate scripts/estimateprops.R @ 1:817eb707bbf4 draft

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"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit 683bb72ae92b5759a239b7e3bf4c5a229ed35b54"
author bgruening
date Fri, 26 Nov 2021 15:54:31 +0000
parents 2fed32b5aa02
children 577435e5e6b2
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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 ## Show different in estimation methods
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21 ## Jitter plot of estimated cell type proportions
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22 jitter_fig <- Jitter_Est(
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23 list(data.matrix(estimated_music_props),
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24 data.matrix(estimated_nnls_props)),
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25 method.name = methods, title = "Jitter plot of Est Proportions",
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26 size = 2, alpha = 0.7) + theme_minimal()
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29 ## Make a Plot
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30 ## A more sophisticated jitter plot is provided as below. We separated
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31 ## the T2D subjects and normal subjects by their disease factor levels.
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32 estimated_music_props_flat <- melt(estimated_music_props)
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33 estimated_nnls_props_flat <- melt(estimated_nnls_props)
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35 m_prop <- rbind(estimated_music_props_flat,
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36 estimated_nnls_props_flat)
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37 colnames(m_prop) <- c("Sub", "CellType", "Prop")
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39 if (is.null(celltypes)) {
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40 celltypes <- levels(m_prop$CellType)
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41 message("No celltypes declared, using:")
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42 message(celltypes)
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43 }
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44
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45 if (phenotype_target_threshold == -99) {
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46 phenotype_target_threshold <- -Inf
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47 message("phenotype target threshold set to -Inf")
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48 }
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49
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50 if (is.null(phenotype_factors)) {
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51 phenotype_factors <- colnames(pData(bulk_eset))
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52 }
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53 ## filter out unwanted factors like "sampleID" and "subjectName"
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54 phenotype_factors <- phenotype_factors[
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55 !(phenotype_factors %in% phenotype_factors_always_exclude)]
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56 message("Phenotype Factors to use:")
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57 message(phenotype_factors)
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58
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60 m_prop$CellType <- factor(m_prop$CellType, levels = celltypes) # nolint
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61 m_prop$Method <- factor(rep(methods, each = nrow(estimated_music_props_flat)), # nolint
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62 levels = methods)
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63 m_prop$Disease_factor <- rep(bulk_eset[[phenotype_target]], 2 * length(celltypes)) # nolint
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64 m_prop <- m_prop[!is.na(m_prop$Disease_factor), ]
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65 ## Generate a TRUE/FALSE table of Normal == 1 and Disease == 2
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66 sample_groups <- c("Normal", sample_disease_group)
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67 m_prop$Disease <- factor(sample_groups[(m_prop$Disease_factor > phenotype_target_threshold) + 1], # nolint
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68 levels = sample_groups)
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69
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70 ## Binary to scale: e.g. TRUE / 5 = 0.2
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71 m_prop$D <- (m_prop$Disease == # nolint
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72 sample_disease_group) / sample_disease_group_scale
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73 ## NA's are not included in the comparison below
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74 m_prop <- rbind(subset(m_prop, Disease != sample_disease_group),
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75 subset(m_prop, Disease == sample_disease_group))
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77 jitter_new <- ggplot(m_prop, aes(Method, Prop)) +
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78 geom_point(aes(fill = Method, color = Disease, stroke = D, shape = Disease),
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79 size = 2, alpha = 0.7,
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80 position = position_jitter(width = 0.25, height = 0)) +
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81 facet_wrap(~ CellType, scales = "free") +
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82 scale_colour_manual(values = c("white", "gray20")) +
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83 scale_shape_manual(values = c(21, 24)) + theme_minimal()
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84
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85 ## Plot to compare method effectiveness
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86 ## Create dataframe for beta cell proportions and Disease_factor levels
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87 m_prop_ana <- data.frame(pData(bulk_eset)[rep(1:nrow(estimated_music_props), 2), #nolint
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88 phenotype_factors],
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89 ct.prop = c(estimated_music_props[, 2],
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90 estimated_nnls_props[, 2]),
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91 Method = factor(rep(methods,
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92 each = nrow(estimated_music_props)),
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93 levels = methods))
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94 colnames(m_prop_ana)[1:length(phenotype_factors)] <- phenotype_factors #nolint
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95 m_prop_ana <- subset(m_prop_ana, !is.na(m_prop_ana[phenotype_target]))
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96 m_prop_ana$Disease <- factor(sample_groups[( # nolint
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97 m_prop_ana[phenotype_target] > phenotype_target_threshold) + 1],
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98 sample_groups)
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99 m_prop_ana$D <- (m_prop_ana$Disease == # nolint
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100 sample_disease_group) / sample_disease_group_scale
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101
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102 jitt_compare <- ggplot(m_prop_ana, aes_string(phenotype_target, "ct.prop")) +
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103 geom_smooth(method = "lm", se = FALSE, col = "black", lwd = 0.25) +
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104 geom_point(aes(fill = Method, color = Disease, stroke = D, shape = Disease),
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105 size = 2, alpha = 0.7) + facet_wrap(~ Method) +
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106 ggtitle(compare_title) + theme_minimal() +
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107 scale_colour_manual(values = c("white", "gray20")) +
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108 scale_shape_manual(values = c(21, 24))
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109
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110 ## BoxPlot
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111 plot_box <- Boxplot_Est(list(
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112 data.matrix(estimated_music_props),
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113 data.matrix(estimated_nnls_props)),
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114 method.name = c("MuSiC", "NNLS")) +
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115 theme(axis.text.x = element_text(angle = -90),
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116 axis.text.y = element_text(size = 8)) +
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117 ggtitle(element_blank()) + theme_minimal()
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118
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119 ## Heatmap
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120 plot_hmap <- Prop_heat_Est(list(
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121 data.matrix(estimated_music_props),
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122 data.matrix(estimated_nnls_props)),
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123 method.name = c("MuSiC", "NNLS")) +
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124 theme(axis.text.x = element_text(angle = -90),
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125 axis.text.y = element_text(size = 6))
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126
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127 pdf(file = outfile_pdf, width = 8, height = 8)
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128 plot_grid(jitter_fig, plot_box, labels = "auto", ncol = 1, nrow = 2)
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129 plot_grid(jitter_new, jitt_compare, labels = "auto", ncol = 1, nrow = 2)
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130 plot_hmap
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131 message(dev.off())
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132
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133 ## Output Proportions
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134
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135 write.table(est_prop$Est.prop.weighted,
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136 file = paste0("report_data/prop_",
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137 "Music Estimated Proportions of Cell Types",
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138 ".tabular"),
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139 quote = F, sep = "\t", col.names = NA)
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140 write.table(est_prop$Est.prop.allgene,
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141 file = paste0("report_data/prop_",
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142 "NNLS Estimated Proportions of Cell Types",
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143 ".tabular"),
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144 quote = F, sep = "\t", col.names = NA)
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145 write.table(est_prop$Weight.gene,
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146 file = paste0("report_data/weightgene_",
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147 "Music Estimated Proportions of Cell Types (by Gene)",
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148 ".tabular"),
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149 quote = F, sep = "\t", col.names = NA)
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150 write.table(est_prop$r.squared.full,
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151 file = paste0("report_data/rsquared_",
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152 "Music R-sqr Estimated Proportions of Each Subject",
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153 ".tabular"),
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154 quote = F, sep = "\t", col.names = NA)
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155 write.table(est_prop$Var.prop,
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156 file = paste0("report_data/varprop_",
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157 "Matrix of Variance of MuSiC Estimates",
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158 ".tabular"),
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159 quote = F, sep = "\t", col.names = NA)
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160
0
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161
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162 ## Summary table
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163 for (meth in methods) {
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164 ##lm_beta_meth = lm(ct.prop ~ age + bmi + hba1c + gender, data =
1
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165 sub_data <- subset(m_prop_ana, Method == meth)
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166 ## We can only do regression where there are more than 1 factors
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167 ## so we must find and exclude the ones which are not
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168 gt1_facts <- sapply(phenotype_factors, function(facname) {
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169 return(length(unique(sort(sub_data[[facname]]))) == 1)
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170 })
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171 form_factors <- phenotype_factors
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172 exclude_facts <- names(gt1_facts)[gt1_facts]
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173 if (length(exclude_facts) > 0) {
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174 message("Factors with only one level will be excluded:")
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175 message(exclude_facts)
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176 form_factors <- phenotype_factors[
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177 !(phenotype_factors %in% exclude_facts)]
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178 }
0
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179 lm_beta_meth <- lm(as.formula(
1
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180 paste("ct.prop", paste(form_factors, collapse = " + "),
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181 sep = " ~ ")), data = sub_data)
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182 message(paste0("Summary: ", meth))
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183 capture.output(summary(lm_beta_meth),
1
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184 file = paste0("report_data/summ_Log of ",
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185 meth,
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186 " fitting.txt"))
0
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187 }