annotate utils.R @ 0:57c644d3f24c draft

planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/recetox_aplcms commit 19de0924a65bc65cbbf7c1fc17e9b5348305f95c
author recetox
date Fri, 10 Jun 2022 10:17:46 +0000
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children abe783e0daca
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57c644d3f24c planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/recetox_aplcms commit 19de0924a65bc65cbbf7c1fc17e9b5348305f95c
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1 library(recetox.aplcms)
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3 align_features <- function(sample_names, ...) {
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4 aligned <- feature.align(...)
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5 feature_names <- seq_len(nrow(aligned$pk.times))
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7 list(
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8 mz_tolerance = as.numeric(aligned$mz.tol),
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9 rt_tolerance = as.numeric(aligned$chr.tol),
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10 rt_crosstab = as_feature_crosstab(feature_names, sample_names, aligned$pk.times),
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11 int_crosstab = as_feature_crosstab(feature_names, sample_names, aligned$aligned.ftrs)
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12 )
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13 }
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15 get_sample_name <- function(filename) {
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16 tools::file_path_sans_ext(basename(filename))
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17 }
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19 as_feature_crosstab <- function(feature_names, sample_names, data) {
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20 colnames(data) <- c("mz", "rt", "mz_min", "mz_max", sample_names)
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21 rownames(data) <- feature_names
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22 as.data.frame(data)
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23 }
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25 as_feature_sample_table <- function(rt_crosstab, int_crosstab) {
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26 feature_names <- rownames(rt_crosstab)
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27 sample_names <- colnames(rt_crosstab)[- (1:4)]
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29 feature_table <- data.frame(
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30 feature = feature_names,
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31 mz = rt_crosstab[, 1],
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32 rt = rt_crosstab[, 2]
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33 )
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35 # series of conversions to produce a table type from data.frame
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36 rt_crosstab <- as.table(as.matrix(rt_crosstab[, - (1:4)]))
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37 int_crosstab <- as.table(as.matrix(int_crosstab[, - (1:4)]))
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39 crosstab_axes <- list(feature = feature_names, sample = sample_names)
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40 dimnames(rt_crosstab) <- dimnames(int_crosstab) <- crosstab_axes
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42 x <- as.data.frame(rt_crosstab, responseName = "sample_rt")
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43 y <- as.data.frame(int_crosstab, responseName = "sample_intensity")
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45 data <- merge(x, y, by = c("feature", "sample"))
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46 data <- merge(feature_table, data, by = "feature")
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47 data
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48 }
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50 load_features <- function(files) {
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51 files_list <- sort_samples_by_acquisition_number(files)
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52 features <- lapply(files_list, arrow::read_parquet)
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53 features <- lapply(features, as.matrix)
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54 return(features)
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55 }
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57 save_data_as_parquet_files <- function(data, subdir) {
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58 dir.create(subdir)
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59 for (i in 0:(length(data) - 1)) {
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60 filename <- file.path(subdir, paste0(subdir, "_features_", i, ".parquet"))
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61 arrow::write_parquet(as.data.frame(data[i + 1]), filename)
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62 }
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63 }
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65 save_aligned_features <- function(aligned, rt_file, int_file, tol_file) {
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66 arrow::write_parquet(as.data.frame(aligned$rt_crosstab), rt_file)
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67 arrow::write_parquet(as.data.frame(aligned$int_crosstab), int_file)
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69 mz_tolerance <- c(aligned$mz_tolerance)
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70 rt_tolerance <- c(aligned$rt_tolerance)
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71 arrow::write_parquet(data.frame(mz_tolerance, rt_tolerance), tol_file)
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72 }
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74 load_aligned_features <- function(rt_file, int_file, tol_file) {
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75 rt_cross_table <- arrow::read_parquet(rt_file)
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76 int_cross_table <- arrow::read_parquet(int_file)
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77 tolerances_table <- arrow::read_parquet(tol_file)
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79 result <- list()
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80 result$mz_tolerance <- tolerances_table$mz_tolerance
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81 result$rt_tolerance <- tolerances_table$rt_tolerance
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82 result$rt_crosstab <- rt_cross_table
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83 result$int_crosstab <- int_cross_table
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84 return(result)
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85 }
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86
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87 recover_signals <- function(cluster,
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88 filenames,
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89 extracted,
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90 corrected,
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91 aligned,
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92 mz_tol = 1e-05,
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93 mz_range = NA,
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94 rt_range = NA,
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95 use_observed_range = TRUE,
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96 min_bandwidth = NA,
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97 max_bandwidth = NA,
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98 recover_min_count = 3) {
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99 if (!is(cluster, "cluster")) {
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100 cluster <- parallel::makeCluster(cluster)
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101 on.exit(parallel::stopCluster(cluster))
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102 }
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103
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104 clusterExport(cluster, c("extracted", "corrected", "aligned", "recover.weaker"))
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105 clusterEvalQ(cluster, library("splines"))
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106
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107 recovered <- parLapply(cluster, seq_along(filenames), function(i) {
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108 recover.weaker(
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109 loc = i,
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110 filename = filenames[[i]],
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111 this.f1 = extracted[[i]],
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112 this.f2 = corrected[[i]],
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113 pk.times = aligned$rt_crosstab,
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114 aligned.ftrs = aligned$int_crosstab,
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115 orig.tol = mz_tol,
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116 align.mz.tol = aligned$mz_tolerance,
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117 align.chr.tol = aligned$rt_tolerance,
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118 mz.range = mz_range,
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119 chr.range = rt_range,
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120 use.observed.range = use_observed_range,
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121 bandwidth = 0.5,
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122 min.bw = min_bandwidth,
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123 max.bw = max_bandwidth,
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124 recover.min.count = recover_min_count
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125 )
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126 })
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127
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128 feature_table <- aligned$rt_crosstab[, 1:4]
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129 rt_crosstab <- cbind(feature_table, sapply(recovered, function(x) x$this.times))
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130 int_crosstab <- cbind(feature_table, sapply(recovered, function(x) x$this.ftrs))
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131
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132 feature_names <- rownames(feature_table)
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133 sample_names <- colnames(aligned$rt_crosstab[, - (1:4)])
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134
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135 list(
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136 extracted_features = lapply(recovered, function(x) x$this.f1),
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137 corrected_features = lapply(recovered, function(x) x$this.f2),
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138 rt_crosstab = as_feature_crosstab(feature_names, sample_names, rt_crosstab),
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139 int_crosstab = as_feature_crosstab(feature_names, sample_names, int_crosstab)
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140 )
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141 }
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142
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143 create_feature_sample_table <- function(features) {
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144 table <- as_feature_sample_table(
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145 rt_crosstab = features$rt_crosstab,
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146 int_crosstab = features$int_crosstab
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147 )
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148 return(table)
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149 }