Mercurial > repos > recetox > ramclustr

annotate ramclustr_wrapper.R @ 12:844e308330c4 draft default tip

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planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/ramclustr commit f79a5b51599254817727bc9028b9797ea994cb4e
author recetox
date Tue, 27 Jun 2023 14:23:19 +0000
parents 62bf6ef633d0
children
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1 store_output <- function(ramclustr_obj,
308bd645da47 planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/ramclustr commit 8714723083359049eb1a715a946851e07f8a3a20
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2 output_merge_msp,
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3 output_spec_abundance,
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4 msp_file) {
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5 RAMClustR::write.msp(ramclustr_obj, one.file = output_merge_msp)
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6 write.csv(ramclustr_obj$SpecAbund,
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7 file = output_spec_abundance,
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8 row.names = TRUE, quote = FALSE
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9 )
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11 if (!is.null(msp_file)) {
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12 exp_name <- ramclustr_obj$ExpDes[[1]][which(
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13 row.names(ramclustr_obj$ExpDes[[1]]) == "Experiment"
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14 ), 1]
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15 filename <- paste("spectra/", exp_name, ".msp", sep = "")
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16 file.copy(from = filename, to = msp_file, overwrite = TRUE)
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17 }
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18 }
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20 load_experiment_definition <- function(filename) {
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21 experiment <- RAMClustR::defineExperiment(csv = filename)
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22 return(experiment)
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23 }
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25 read_metadata <- function(filename) {
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26 data <- read.csv(filename, header = TRUE, stringsAsFactors = FALSE)
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28 if (!"qc" %in% colnames(data)) {
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29 if ("sampleType" %in% colnames(data)) {
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30 data$qc <- ifelse(data$sampleType == "qc", TRUE, FALSE)
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31 }
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32 }
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34 if (!"order" %in% colnames(data)) {
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35 if ("injectionOrder" %in% colnames(data)) {
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36 names(data)[names(data) == "injectionOrder"] <- "order"
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37 }
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38 }
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40 return(data)
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41 }
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43 read_ramclustr_aplcms <- function(ms1_featuredefinitions = NULL,
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44 ms1_featurevalues = NULL,
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45 df_phenodata = NULL,
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46 phenodata_ext = NULL,
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47 exp_des = NULL,
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48 st = NULL,
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49 ensure_no_na = TRUE) {
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50 ms1_featuredefinitions <- arrow::read_parquet(ms1_featuredefinitions)
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51 ms1_featurevalues <- arrow::read_parquet(ms1_featurevalues)
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53 if (!is.null(df_phenodata)) {
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54 if (phenodata_ext == "csv") {
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55 df_phenodata <- read.csv(
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56 file = df_phenodata,
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57 header = TRUE, check.names = FALSE
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58 )
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59 } else {
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60 df_phenodata <- read.csv(
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61 file = df_phenodata,
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62 header = TRUE, check.names = FALSE, sep = "\t"
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63 )
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64 }
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65 }
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66 if (!is.null(exp_des)) {
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67 exp_des <- load_experiment_definition(exp_des)
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68 }
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70 feature_values <- ms1_featurevalues[-1]
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71 feature_values <- t(feature_values)
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72 colnames(feature_values) <- ms1_featurevalues[[1]]
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74 feature_definitions <- data.frame(ms1_featuredefinitions)
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76 ramclustr_obj <- RAMClustR::rc.get.df.data(
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77 ms1_featureDefinitions = feature_definitions,
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78 ms1_featureValues = feature_values,
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79 phenoData = df_phenodata,
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80 ExpDes = exp_des,
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81 st = st,
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82 ensure.no.na = ensure_no_na
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83 )
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84 return(ramclustr_obj)
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85 }
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87 apply_normalisation <- function(ramclustr_obj = NULL,
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88 normalize_method,
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89 metadata_file = NULL,
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90 qc_inj_range,
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91 p_cut,
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92 rsq_cut,
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93 p_adjust) {
0
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94 batch <- NULL
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95 order <- NULL
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96 qc <- NULL
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97
10
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98 if (normalize_method == "TIC") {
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99 ramclustr_obj <- RAMClustR::rc.feature.normalize.tic(
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100 ramclustObj =
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101 ramclustr_obj
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102 )
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103 } else if (normalize_method == "quantile") {
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104 ramclustr_obj <- RAMClustR::rc.feature.normalize.quantile(ramclustr_obj)
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105 } else if (normalize_method == "batch.qc") {
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106 if (!(is.null(metadata_file) || metadata_file == "None")) {
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107 metadata <- read_metadata(metadata_file)
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108 batch <- metadata$batch
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109 order <- metadata$order
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110 qc <- metadata$qc
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111 }
0
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112
10
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113 ramclustr_obj <- RAMClustR::rc.feature.normalize.batch.qc(
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114 order = order,
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115 batch = batch,
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116 qc = qc,
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117 ramclustObj = ramclustr_obj,
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118 qc.inj.range = qc_inj_range
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119 )
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120 } else {
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121 if (!(is.null(metadata_file) || metadata_file == "None")) {
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122 metadata <- read_metadata(metadata_file)
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123 batch <- metadata$batch
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124 order <- metadata$order
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125 qc <- metadata$qc
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126 }
0
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127
10
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128 ramclustr_obj <- RAMClustR::rc.feature.normalize.qc(
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129 order = order,
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130 batch = batch,
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131 qc = qc,
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132 ramclustObj = ramclustr_obj,
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133 p.cut = p_cut,
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134 rsq.cut = rsq_cut,
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135 p.adjust = p_adjust
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136 )
4
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137 }
10
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138 return(ramclustr_obj)
0
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139 }