Mercurial > repos > galaxyp > mqppep_preproc

annotate mqppep_anova.R @ 3:bae3a23461c9 draft

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planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 3dcf0d08f006b888061ff83eadc65e550d751869
author galaxyp
date Tue, 31 Jan 2023 22:27:00 +0000
parents b76c75521d91
children
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1 #!/usr/bin/env Rscript
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2 # libraries
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3 library(optparse)
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4 library(stringr)
1
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5 library(tinytex)
0
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6
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7 # ref for parameterizing Rmd document: https://stackoverflow.com/a/37940285
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8
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9 # parse options
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10 option_list <- list(
1
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11
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12 # files
0
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13 make_option(
8dfd5d2b5903 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 3a7b3609d6e514c9e8f980ecb684960c6b2252fe
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14 c("-a", "--alphaFile"),
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15 action = "store",
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16 default = NA,
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17 type = "character",
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18 help = paste0("List of alpha cutoff values for significance testing;",
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19 " path to text file having one column and no header")
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20 ),
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21 make_option(
1
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22 c("-M", "--anova_ksea_metadata"),
0
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23 action = "store",
1
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24 default = "anova_ksea_metadata.tsv",
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25 type = "character",
1
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26 help = "Phosphopeptide metadata, ANOVA FDR, and KSEA enribhments"
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27 ),
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28 make_option(
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29 c("-o", "--imputedDataFile"),
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30 action = "store",
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31 default = "output_imputed.tsv",
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32 type = "character",
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33 help = "Imputed Phosphopeptide Intensities output file path"
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34 ),
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35 make_option(
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36 c("-n", "--imputedQNLTDataFile"),
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37 action = "store",
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38 default = "output_imp_qn_lt.tsv",
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39 type = "character",
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40 help =
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41 paste(
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42 "Imputed, Quantile-Normalized Log-Transformed Phosphopeptide",
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43 "Intensities output file path"
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44 )
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45 ),
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46 make_option(
1
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47 c("-i", "--inputFile"),
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48 action = "store",
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49 default = NA,
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50 type = "character",
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51 help = "Phosphopeptide Intensities sparse input file path"
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52 ),
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53 make_option(
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54 c("-K", "--ksea_sqlite"),
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55 action = "store",
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56 default = NA,
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57 type = "character",
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58 help = "Path to 'ksea_sqlite' output produced by this tool"
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59 ),
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60 make_option(
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61 c("-S", "--preproc_sqlite"),
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62 action = "store",
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63 default = NA,
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64 type = "character",
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65 help = "Path to 'preproc_sqlite' produced by `mqppep_mrgfltr.py`"
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66 ),
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67 make_option(
0
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68 c("-r", "--reportFile"),
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69 action = "store",
1
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70 default = "mqppep_anova.pdf",
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71 type = "character",
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72 help = "PDF report file path"
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73 ),
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74
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75 # parameters
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76 make_option(
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77 c("-f", "--firstDataColumn"),
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78 action = "store",
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79 default = "^Intensity[^_]",
0
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80 type = "character",
1
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81 help = "First column of intensity values"
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82 ),
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83 make_option(
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84 c("-m", "--imputationMethod"),
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85 action = "store",
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86 default = "random",
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87 type = "character",
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88 help = paste0("Method for missing-value imputation,",
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89 " one of c('group-median','median','mean','random')")
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90 ),
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91 make_option(
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92 c("-C", "--intensityMinValuesPerClass"),
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93 action = "store",
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94 default = "0",
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95 type = "integer",
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96 help = "Minimum number of observed values per class"
0
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97 ),
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98 make_option(
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99 c("-k", "--ksea_cutoff_statistic"),
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100 action = "store",
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101 default = "FDR",
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102 type = "character",
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103 help = paste0("Method for missing-value imputation,",
3
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104 " one of c('FDR','p.value'), but don't expect 'p.value' to work well.")
0
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105 ),
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106 make_option(
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107 c("-t", "--ksea_cutoff_threshold"),
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108 action = "store",
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109 default = 0.05,
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110 type = "double",
3
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111 help = paste0(
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112 "Maximum score to be used to score a kinase enrichment as significant")
0
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113 ),
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114 make_option(
1
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115 c("-c", "--kseaMinSubstrateCount"),
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116 action = "store",
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117 default = "1",
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118 type = "integer",
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119 help = "Minimum number of substrates to consider any kinase for KSEA"
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120 ),
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121 make_option(
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122 c("--kseaUseAbsoluteLog2FC"),
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123 action = "store_true",
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124 default = "FALSE",
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125 type = "logical",
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126 help = paste0("Should abs(log2(fold-change)) be used for KSEA?",
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127 " (TRUE may alter number of hits.)")
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128 ),
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129 make_option(
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130 c("-p", "--meanPercentile"),
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131 action = "store",
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132 default = 3,
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133 type = "integer",
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134 help = paste0("Mean percentile for randomly generated imputed values;",
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135 ", range [1,99]")
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136 ),
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137 make_option(
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138 c("--minQuality"),
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139 action = "store",
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140 default = 0,
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141 type = "integer",
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142 help = paste0("Minimum quality (higher value reduces number of substrates",
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143 " accepted; you may want to keep below 100), range [0,infinity]")
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144 ),
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145 make_option(
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146 c("--oneWayManyCategories"),
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147 action = "store",
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148 default = "aov",
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149 type = "character",
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150 help = "Name of R function for one-way tests among more than two categories"
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151 ),
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152 make_option(
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153 c("--oneWayTwoCategories"),
0
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154 action = "store",
1
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155 default = "two.way",
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156 type = "character",
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157 help = "Name of R function for one-way tests between two categories"
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158 ),
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159 make_option(
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160 c("-s", "--regexSampleNames"),
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161 action = "store",
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162 default = "\\.(\\d+)[A-Z]$",
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163 type = "character",
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164 help = "Regular expression extracting sample-names"
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165 ),
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166 make_option(
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167 c("-g", "--regexSampleGrouping"),
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168 action = "store",
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169 default = "(\\d+)",
0
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170 type = "character",
1
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171 help = paste0("Regular expression extracting sample-group",
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172 " from an extracted sample-name")
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173 ),
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174 make_option(
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175 c("-d", "--sdPercentile"),
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176 action = "store",
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177 default = 3,
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178 type = "double",
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179 help = paste0("Adjustment value for standard deviation of",
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180 " randomly generated imputed values; real")
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181 ),
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182 make_option(
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183 c("-F", "--sampleGroupFilter"),
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184 action = "store",
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185 default = "none",
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186 type = "character",
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187 help = paste0("Should no filter be applied to sample group names (none)",
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188 " or should the filter specify samples to include or exclude?")
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189 ),
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190 make_option(
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191 c("--sampleGroupFilterMode"),
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192 action = "store",
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193 default = "r",
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194 type = "character",
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195 help = paste0("First character ('f', 'p', or 'r') indicating regular",
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196 "expression matching mode ('fixed', 'perl', or 'grep'; ",
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197 "see https://rdrr.io/r/base/grep.html). Second character may be 'i;",
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198 "to make search ignore case.")
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199 ),
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200 make_option(
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201 c("-G", "--sampleGroupFilterPatterns"),
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202 action = "store",
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203 default = ".*",
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204 type = "character",
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205 help = paste0("Regular expression extracting sample-group",
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206 " from an extracted sample-name")
0
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207 )
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208 )
1
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209
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210 tryCatch(
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211 args <- parse_args(
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212 OptionParser(
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213 option_list = option_list,
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214 add_help_option = TRUE
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215 ),
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216 print_help_and_exit = TRUE
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217 ),
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218 error = function(e) {
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219 parse_args(
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220 OptionParser(
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221 option_list = option_list,
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222 add_help_option = TRUE
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223 ),
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224 print_help_and_exit = TRUE
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225 )
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226 stop(as.character(e))
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227 }
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228 )
0
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229 print("args is:")
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230 cat(str(args))
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231
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232 # Check parameter values
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233
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234 if (! file.exists(args$inputFile)) {
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235 stop((paste("Input file", args$inputFile, "does not exist")))
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236 }
1
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237
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238 # files
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239 alpha_file <- args$alphaFile
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240 anova_ksea_metadata_file <- args$anova_ksea_metadata
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241 imp_qn_lt_data_file <- args$imputedQNLTDataFile
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242 imputed_data_file <- args$imputedDataFile
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243 input_file <- args$inputFile
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244 ksea_sqlite_file <- args$ksea_sqlite
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245 preproc_sqlite_file <- args$preproc_sqlite
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246 report_file_name <- args$reportFile
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247
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248 # parameters
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249 # firstDataColumn - see below
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250 group_filter <- args$sampleGroupFilter
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251 group_filter_mode <- args$sampleGroupFilterMode
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252 # imputationMethod - see below
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253 intensity_min_values_per_class <- args$intensityMinValuesPerClass
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254 ksea_cutoff_statistic <- args$ksea_cutoff_statistic
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255 ksea_cutoff_threshold <- args$ksea_cutoff_threshold
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256 ksea_min_substrate_count <- args$kseaMinSubstrateCount
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257 ksea_use_absolute_log2_fc <- args$kseaUseAbsoluteLog2FC
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258 # mean_percentile - see below
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259 min_quality <- args$minQuality
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260 # regexSampleNames - see below
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261 # regexSampleGrouping - see below
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262 # sampleGroupFilterPatterns - see below (becomes group_filter_patterns)
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263 # sd_percentile - see below
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264
0
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265 if (
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266 sum(
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267 grepl(
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268 pattern = ksea_cutoff_statistic,
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269 x = c("FDR", "p.value")
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270 )
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271 ) < 1
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272 ) {
3
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273 print(sprintf(
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274 "bad ksea_cutoff_statistic argument: %s", ksea_cutoff_statistic))
0
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275 return(-1)
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276 }
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277
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278 imputation_method <- args$imputationMethod
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279 if (
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280 sum(
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281 grepl(
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282 pattern = imputation_method,
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283 x = c("group-median", "median", "mean", "random")
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284 )
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285 ) < 1
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286 ) {
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287 print(sprintf("bad imputationMethod argument: %s", imputation_method))
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288 return(-1)
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289 }
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290
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291 # read with default values, when applicable
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292 mean_percentile <- args$meanPercentile
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293 sd_percentile <- args$sdPercentile
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294 # in the case of 'random" these values are ignored by the client script
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295 if (imputation_method == "random") {
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296 print("mean_percentile is:")
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297 cat(str(mean_percentile))
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298
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299 print("sd_percentile is:")
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300 cat(str(mean_percentile))
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301 }
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302
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303 # convert string parameters that are passed in via config files:
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304 # - firstDataColumn
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305 # - regexSampleNames
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306 # - regexSampleGrouping
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307 read_config_file_string <- function(fname, limit) {
1
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308 cat(sprintf("read_config_file_string: fname = '%s'\n", fname))
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309 cat(sprintf("length(fname) = '%s'\n", length(fname)))
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310 result <-
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311 if (file.exists(fname)) {
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312 cat(sprintf("reading '%s' ...\n", fname))
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313 readChar(fname, limit)
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314 } else {
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315 cat(sprintf("not a file: '%s'\n", fname))
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316 fname
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317 }
0
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318 # eliminate any leading whitespace
1
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319 result <- gsub("^[ \t\n]*", "", result)
0
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320 # eliminate any trailing whitespace
1
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321 result <- gsub("[ \t\n]*$", "", result)
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322 # substitute characters escaped by Galaxy sanitizer
1
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323 result <- gsub("__lt__", "<", result)
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324 result <- gsub("__le__", "<=", result)
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325 result <- gsub("__eq__", "==", result)
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326 result <- gsub("__ne__", "!=", result)
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327 result <- gsub("__gt__", ">", result)
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328 result <- gsub("__ge__", ">=", result)
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329 result <- gsub("__sq__", "'", result)
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330 result <- gsub("__dq__", '"', result)
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331 result <- gsub("__ob__", "[", result)
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332 result <- gsub("__cb__", "]", result)
0
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333 }
1
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334 nc <- 1000
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335
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336 sink(stderr())
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337
0
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338 cat(paste0("first_data_column file: ", args$firstDataColumn, "\n"))
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339 first_data_column <- read_config_file_string(args$firstDataColumn, nc)
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340 cat(paste0("first_data_column: ", first_data_column, "\n"))
1
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341
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342 cat(paste0("regex_sample_grouping file: ", args$regexSampleGrouping, "\n"))
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343 regex_sample_grouping <- read_config_file_string(args$regexSampleGrouping, nc)
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344 cat(paste0("regex_sample_grouping: ", regex_sample_grouping, "\n"))
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345
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346 cat(paste0("regex_sample_names file: ", args$regexSampleNames, "\n"))
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347 regex_sample_names <- read_config_file_string(args$regexSampleNames, nc)
0
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348 cat(paste0("regex_sample_names: ", regex_sample_names, "\n"))
1
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349
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350 if (group_filter != "none") {
3
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351 cat(paste0("group_filter_patterns file: '",
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352 args$sampleGroupFilterPatterns, "'\n"))
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353 group_filter_patterns <-
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354 read_config_file_string(args$sampleGroupFilterPatterns, nc)
1
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355 } else {
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356 group_filter_patterns <- ".*"
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357 }
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358 cat(paste0("group_filter_patterns: ", group_filter_patterns, "\n"))
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359
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360 sink()
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361
0
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362
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363 # from: https://github.com/molgenis/molgenis-pipelines/wiki/
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364 # How-to-source-another_file.R-from-within-your-R-script
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365 # Function location_of_this_script returns the location of this .R script
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366 # (may be needed to source other files in same dir)
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367 location_of_this_script <- function() {
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368 this_file <- NULL
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369 # This file may be 'sourced'
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370 for (i in - (1:sys.nframe())) {
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371 if (identical(sys.function(i), base::source)) {
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372 this_file <- (normalizePath(sys.frame(i)$ofile))
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373 }
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374 }
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375
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376 if (!is.null(this_file)) return(dirname(this_file))
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377
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378 # But it may also be called from the command line
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379 cmd_args <- commandArgs(trailingOnly = FALSE)
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380 cmd_args_trailing <- commandArgs(trailingOnly = TRUE)
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381 cmd_args <- cmd_args[
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382 seq.int(
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383 from = 1,
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384 length.out = length(cmd_args) - length(cmd_args_trailing)
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385 )
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386 ]
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387 res <- gsub("^(?:--file=(.*)|.*)$", "\\1", cmd_args)
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388
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389 # If multiple --file arguments are given, R uses the last one
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390 res <- tail(res[res != ""], 1)
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391 if (0 < length(res)) return(dirname(res))
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392
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393 # Both are not the case. Maybe we are in an R GUI?
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394 return(NULL)
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395 }
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396
1
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397 # validation of input parameters is complete; it is now justifiable to
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398 # install LaTeX tools to render markdown as PDF; this involves a big
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399 # download from GitHub
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400 if (!tinytex::is_tinytex()) tinytex::install_tinytex()
0
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401
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402 rmarkdown_params <- list(
1
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403
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diff changeset
404 # files
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diff changeset
405 alphaFile = alpha_file
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406 , anovaKseaMetadata = anova_ksea_metadata_file
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407 , imputedDataFilename = imputed_data_file
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408 , imputedQNLTDataFile = imp_qn_lt_data_file
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409 , inputFile = input_file
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410 , kseaAppPrepDb = ksea_sqlite_file
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411 , preprocDb = preproc_sqlite_file
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412
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413 # parameters
0
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414 , firstDataColumn = first_data_column
1
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415 , groupFilter = group_filter
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416 , groupFilterMode = group_filter_mode # arg sampleGroupFilterMode
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417 , groupFilterPatterns = group_filter_patterns # arg sampleGroupFilterPatterns
0
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418 , imputationMethod = imputation_method
1
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419 , intensityMinValuesPerGroup = intensity_min_values_per_class
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420 , kseaCutoffStatistic = ksea_cutoff_statistic
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421 , kseaCutoffThreshold = ksea_cutoff_threshold
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422 , kseaMinSubstrateCount = ksea_min_substrate_count
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423 , kseaUseAbsoluteLog2FC = ksea_use_absolute_log2_fc # add
0
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424 , meanPercentile = mean_percentile
1
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425 , minQuality = min_quality # add
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426 , regexSampleGrouping = regex_sample_grouping
0
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427 , regexSampleNames = regex_sample_names
1
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428 , sdPercentile = sd_percentile
0
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429 )
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430
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431 print("rmarkdown_params")
1
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432 print(rmarkdown_params)
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433 print(
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434 lapply(
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435 X = rmarkdown_params,
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436 FUN = function(x) {
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437 paste0(
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438 nchar(as.character(x)),
b76c75521d91 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 43e7a43b545c24b2dc33d039198551c032aa79be
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439 ": '",
b76c75521d91 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 43e7a43b545c24b2dc33d039198551c032aa79be
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440 as.character(x),
b76c75521d91 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 43e7a43b545c24b2dc33d039198551c032aa79be
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441 "'"
b76c75521d91 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 43e7a43b545c24b2dc33d039198551c032aa79be
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442 )
b76c75521d91 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 43e7a43b545c24b2dc33d039198551c032aa79be
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443 }
b76c75521d91 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 43e7a43b545c24b2dc33d039198551c032aa79be
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444 )
b76c75521d91 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 43e7a43b545c24b2dc33d039198551c032aa79be
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445 )
b76c75521d91 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 43e7a43b545c24b2dc33d039198551c032aa79be
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446
0
8dfd5d2b5903 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 3a7b3609d6e514c9e8f980ecb684960c6b2252fe
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447
8dfd5d2b5903 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 3a7b3609d6e514c9e8f980ecb684960c6b2252fe
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448 # freeze the random number generator so the same results will be produced
8dfd5d2b5903 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 3a7b3609d6e514c9e8f980ecb684960c6b2252fe
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449 # from run to run
8dfd5d2b5903 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 3a7b3609d6e514c9e8f980ecb684960c6b2252fe
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450 set.seed(28571)
8dfd5d2b5903 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 3a7b3609d6e514c9e8f980ecb684960c6b2252fe
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451
1
b76c75521d91 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 43e7a43b545c24b2dc33d039198551c032aa79be
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452 script_dir <- location_of_this_script()
0
8dfd5d2b5903 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 3a7b3609d6e514c9e8f980ecb684960c6b2252fe
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453
8dfd5d2b5903 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 3a7b3609d6e514c9e8f980ecb684960c6b2252fe
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454 rmarkdown::render(
8dfd5d2b5903 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 3a7b3609d6e514c9e8f980ecb684960c6b2252fe
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455 input = paste(script_dir, "mqppep_anova_script.Rmd", sep = "/")
8dfd5d2b5903 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 3a7b3609d6e514c9e8f980ecb684960c6b2252fe
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456 , output_file = report_file_name
8dfd5d2b5903 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 3a7b3609d6e514c9e8f980ecb684960c6b2252fe
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457 , params = rmarkdown_params
1
b76c75521d91 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 43e7a43b545c24b2dc33d039198551c032aa79be
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458 , output_format = rmarkdown::pdf_document(
b76c75521d91 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 43e7a43b545c24b2dc33d039198551c032aa79be
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459 includes = rmarkdown::includes(in_header = "mqppep_anova_preamble.tex")
b76c75521d91 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 43e7a43b545c24b2dc33d039198551c032aa79be
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460 , dev = "pdf"
b76c75521d91 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 43e7a43b545c24b2dc33d039198551c032aa79be
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461 , toc = TRUE
b76c75521d91 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 43e7a43b545c24b2dc33d039198551c032aa79be
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462 , toc_depth = 2
b76c75521d91 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 43e7a43b545c24b2dc33d039198551c032aa79be
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463 , number_sections = FALSE
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464 )
0
8dfd5d2b5903 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 3a7b3609d6e514c9e8f980ecb684960c6b2252fe
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465 )