Mercurial > repos > tomnl > anticipated_purity_lcms

annotate anticipated_purity_dims.R @ 8:e28fc4fd51e9 draft

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planemo upload for repository https://github.com/computational-metabolomics/mspurity-galaxy commit 0aa10df0ec1ed71601f932cfb11d7d4d4f620d80-dirty
author tomnl
date Wed, 02 May 2018 13:21:00 -0400
parents 4d2e58566178
children 5969d72028f3
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1 library(msPurity)
dc00b7222a3c planemo upload for repository https://github.com/computational-metabolomics/mspurity-galaxy commit f9591483bef88aef71a9f0cc0ffc75cf6eab480c
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2 library(optparse)
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3 print(sessionInfo())
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5 option_list <- list(
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6 make_option(c("--mzML_file"), type="character"),
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7 make_option(c("--peaks_file"), type="character"),
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8 make_option(c("-o", "--out_dir"), type="character"),
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9 make_option("--minOffset", default=0.5),
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10 make_option("--maxOffset", default=0.5),
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11 make_option("--ilim", default=0.05),
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12 make_option("--ppm", default=4),
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13 make_option("--dimspy", action="store_true"),
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14 make_option("--sim", action="store_true"),
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15 make_option("--remove_nas", action="store_true"),
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16 make_option("--iwNorm", default="none", type="character"),
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17 make_option("--file_num_dimspy", default=1),
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18 make_option("--exclude_isotopes", action="store_true"),
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19 make_option("--isotope_matrix", type="character")
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20 )
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22 # store options
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23 opt<- parse_args(OptionParser(option_list=option_list))
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25 print(sessionInfo())
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26 print(opt)
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28 if (is.null(opt$dimspy)){
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30 df <- read.table(opt$peaks_file, header = TRUE, sep='\t')
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31 filename = NA
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32 }else{
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33 indf <- read.table(opt$peaks_file,
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34 header = TRUE, sep='\t', stringsAsFactors = FALSE)
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36 filename = colnames(indf)[8:ncol(indf)][opt$file_num_dimspy]
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37 # check if the data file is mzML or RAW (can only use mzML currently) so
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38 # we expect an mzML file of the same name in the same folder
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39 indf$i <- indf[,colnames(indf)==filename]
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40 indf[,colnames(indf)==filename] <- as.numeric(indf[,colnames(indf)==filename])
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42 filename = sub("raw", "mzML", filename, ignore.case = TRUE)
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44 df <- indf[4:nrow(indf),]
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45 if ('blank_flag' %in% colnames(df)){
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46 df <- df[df$blank_flag==1,]
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47 }
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49 colnames(df)[colnames(df)=='m.z'] <- 'mz'
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51 if ('nan' %in% df$mz){
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52 df[df$mz=='nan',]$mz <- NA
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53 }
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54 df$mz <- as.numeric(df$mz)
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56
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58
0
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59 }
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61 if (!is.null(opt$remove_nas)){
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62 df <- df[!is.na(df$mz),]
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63 }
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64
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65 if (is.null(opt$isotope_matrix)){
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66 im <- NULL
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67 }else{
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68 im <- read.table(opt$isotope_matrix,
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69 header = TRUE, sep='\t', stringsAsFactors = FALSE)
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70 }
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71
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72 if (is.null(opt$exclude_isotopes)){
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73 isotopes <- FALSE
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74 }else{
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75 isotopes <- TRUE
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76 }
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78
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79 if (dir.exists(opt$mzML_file)){
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80 # if directory then we need to add a file name
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81 print(filename)
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82 if (is.na(filename)){
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83 print('ERROR: If a directory is provided then a filename needs to be entered
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84 directory or automatically obtained by using a dimspy output')
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85 quit()
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86 }else{
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87 mzml_file <- file.path(opt$mzML_file, filename)
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88 }
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89 }else{
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90 mzml_file <- opt$mzML_file
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91 }
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92
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93 if (is.null(opt$sim)){
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94 sim=FALSE
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95 }else{
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96 sim=TRUE
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97 }
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98
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99 minOffset = as.numeric(opt$minOffset)
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100 maxOffset = as.numeric(opt$maxOffset)
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101
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102
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103
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104 if (opt$iwNorm=='none'){
dc00b7222a3c planemo upload for repository https://github.com/computational-metabolomics/mspurity-galaxy commit f9591483bef88aef71a9f0cc0ffc75cf6eab480c
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105 iwNorm = FALSE
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106 iwNormFun = NULL
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107 }else if (opt$iwNorm=='gauss'){
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108 iwNorm = TRUE
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109 iwNormFun = msPurity::iwNormGauss(minOff=-minOffset, maxOff=maxOffset)
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110 }else if (opt$iwNorm=='rcosine'){
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111 iwNorm = TRUE
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112 iwNormFun = msPurity::iwNormRcosine(minOff=-minOffset, maxOff=maxOffset)
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113 }else if (opt$iwNorm=='QE5'){
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114 iwNorm = TRUE
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115 iwNormFun = msPurity::iwNormQE.5()
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116 }
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117
7
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118 print('FIRST ROWS OF PEAK FILE')
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119 print(head(df))
0
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120
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121 predicted <- msPurity::dimsPredictPuritySingle(df$mz,
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122 filepth=mzml_file,
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123 minOffset=minOffset,
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124 maxOffset=maxOffset,
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125 ppm=opt$ppm,
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126 mzML=TRUE,
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127 sim = sim,
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128 ilim = opt$ilim,
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129 isotopes = isotopes,
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130 im = im,
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131 iwNorm = iwNorm,
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132 iwNormFun = iwNormFun
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133 )
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134 predicted <- cbind(df, predicted)
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135
7
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136 print(head(predicted))
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137 print(file.path(opt$out_dir, 'anticipated_purity_dims.tsv'))
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138 write.table(predicted, file.path(opt$out_dir, 'anticipated_purity_dims.tsv'), row.names=FALSE, sep='\t')