Mercurial > repos > galaxyp > cardinal_data_exporter

annotate data_exporter.xml @ 5:90d2e00e1304 draft

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"planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit 1df4591de435d232862f20669aea529ceb23b12a"
author galaxyp
date Fri, 13 Dec 2019 13:56:29 -0500
parents e521b5767819
children 350a84ea795c
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1 <tool id="cardinal_data_exporter" name="MSI data exporter" version="@VERSION@.2">
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2 <description>
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3 exports imzML and Analyze7.5 to tabular files
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4 </description>
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5 <macros>
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6 <import>macros.xml</import>
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7 </macros>
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8 <expand macro="requirements"/>
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9 <command detect_errors="exit_code">
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10 <![CDATA[
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12 @INPUT_LINKING@
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13 cat '${cardinal_imzml_exporter}' &&
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14 Rscript '${cardinal_imzml_exporter}'
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16 ]]>
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17 </command>
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18 <configfiles>
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19 <configfile name="cardinal_imzml_exporter"><![CDATA[
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21 ################################# load libraries and read file #################
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23 library(Cardinal)
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25 @READING_MSIDATA_INRAM@
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28 ###################### Intensity matrix output ################################
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30 #if "int_matrix" in str($output_options).split(","):
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31 print("intensity matrix output")
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33 mz_names = gsub(" = ", "_", names(features(msidata)))
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34 mz_names = gsub("/", "", mz_names)
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35 pixel_names = gsub(", y = ", "_", names(pixels(msidata)))
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36 pixel_names = gsub(" = ", "y_", pixel_names)
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37
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38 ##spectramatrix = cbind(mz_names,spectra(msidata))
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39 newmatrix = rbind(c("mz_name", pixel_names), cbind(mz_names,spectra(msidata)))
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40 write.table(newmatrix, file="$intensity_matrix", quote = FALSE, row.names = FALSE, col.names=FALSE, sep = "\t")
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41 ## free up RAM space in case furhter steps will be run:
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42 rm(newmatrix)
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43 gc()
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45 #end if
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48 ############################## m/z feature output ##########################
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49 #if "mz_tabular" in str($output_options).split(","):
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50 print("mz feature output")
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52 mz_names = gsub(" = ", "_", names(features(msidata)))
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53 mz_names = gsub("/", "", mz_names)
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55 ## mean, median, sd and SEM intensity per file and mz
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56 full_sample_mean = rowMeans(spectra(msidata), na.rm=TRUE)
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57 full_sample_median = apply(spectra(msidata),1,median, na.rm=TRUE)
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58 full_sample_sd = apply(spectra(msidata),1,sd, na.rm=TRUE)
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59 full_sample_sem = full_sample_sd/full_sample_mean*100
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60 ## npeaks and sum of all intensities per spectrum and mz
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61 npeaks= sum(spectra(msidata)>0, na.rm=TRUE)
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62 mzTIC = rowSums(spectra(msidata), na.rm=TRUE) ## calculate intensity sum for each m/z
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63 peakspermz = rowSums(spectra(msidata) > 0, na.rm=TRUE) ## calculate number of intensities > 0 for each m/z (max = number of spectra)
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65 ## combine into dataframe, order is the same for all vectors
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66 mz_df = data.frame(mz_names, mz(msidata), full_sample_mean, full_sample_median, full_sample_sd, full_sample_sem, mzTIC, peakspermz)
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67 colnames(mz_df) = c("mz_names", "mz", "sample_mean", "sample_median", "sample_sd", "sample_sem", "intensity_sum", "number_peaks")
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68 write.table(mz_df, file="$feature_output", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
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69 ## free up RAM space in case furhter steps will be run:
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70 rm(mz_df)
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71 gc()
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72 #end if
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74 ###################### summarized m/z feature output #######################
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76 #if str($tabular_annotation.load_annotation) == 'yes_annotation':
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77 print("summarized annotation output")
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78
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79 ## read and extract x,y,annotation information
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80 input_tabular = read.delim("$tabular_annotation.annotation_file", header = $tabular_annotation.tabular_header, stringsAsFactors = FALSE)
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81 annotation_input = input_tabular[,c($tabular_annotation.column_x, $tabular_annotation.column_y, $tabular_annotation.column_names)]
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82 colnames(annotation_input) = c("x", "y", "annotation")
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84 ## merge with coordinate information of msidata
28ba52c9548c planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit 0825a4ccd3ebf4ca8a298326d14f3e7b25ae8415
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85 msidata_coordinates = cbind(coord(msidata)[,1:2], c(1:ncol(msidata)))
28ba52c9548c planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit 0825a4ccd3ebf4ca8a298326d14f3e7b25ae8415
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86 colnames(msidata_coordinates)[3] = "pixel_index"
28ba52c9548c planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit 0825a4ccd3ebf4ca8a298326d14f3e7b25ae8415
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87 merged_annotation = merge(msidata_coordinates, annotation_input, by=c("x", "y"), all.x=TRUE)
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88 merged_annotation[is.na(merged_annotation)] = "NA"
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89 merged_annotation = merged_annotation[order(merged_annotation\$pixel_index),]
28ba52c9548c planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit 0825a4ccd3ebf4ca8a298326d14f3e7b25ae8415
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90 msidata\$annotation = as.factor(merged_annotation[,4])
28ba52c9548c planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit 0825a4ccd3ebf4ca8a298326d14f3e7b25ae8415
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91
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92 ## create m/z feature name
28ba52c9548c planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit 0825a4ccd3ebf4ca8a298326d14f3e7b25ae8415
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93 mz_names = gsub(" = ", "_", names(features(msidata)))
28ba52c9548c planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit 0825a4ccd3ebf4ca8a298326d14f3e7b25ae8415
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94 mz_names = gsub("/", "", mz_names)
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95
28ba52c9548c planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit 0825a4ccd3ebf4ca8a298326d14f3e7b25ae8415
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96 #if "mean" in str($tabular_annotation.summary_type).split(","):
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97 print("summarized mean")
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98
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99 ## calculate mean per annotation group
28ba52c9548c planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit 0825a4ccd3ebf4ca8a298326d14f3e7b25ae8415
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100 sample_matrix = matrix(,ncol=0, nrow=nrow(msidata))
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101 count = 1
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102 for (subsample in levels(msidata\$annotation)){
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103 subsample_pixels = msidata[,msidata\$annotation == subsample]
4
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104 subsample_calc = rowMeans(spectra(subsample_pixels), na.rm=TRUE)
0
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105 sample_matrix = cbind(sample_matrix, subsample_calc)
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106 count = count+1}
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107 sample_matrix_mean = cbind(mz_names,sample_matrix)
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108 sample_matrix_mean = rbind(c("mz_name", levels(msidata\$annotation)), sample_matrix_mean)
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109 write.table(sample_matrix_mean, file="$summarized_mean", quote = FALSE, row.names = FALSE, col.names=FALSE, sep = "\t")
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110 #end if
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111
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112 #if "median" in str($tabular_annotation.summary_type).split(","):
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113 print("summarized median")
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114
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115 sample_matrix = matrix(,ncol=0, nrow=nrow(msidata))
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116 count = 1
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117 for (subsample in levels(msidata\$annotation)){
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118 subsample_pixels = msidata[,msidata\$annotation == subsample]
4
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119 subsample_calc = apply(spectra(subsample_pixels),1,median, na.rm=TRUE)
0
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120 sample_matrix = cbind(sample_matrix, subsample_calc)
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121 count = count+1}
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122 sample_matrix_median = cbind(mz_names,sample_matrix)
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123 sample_matrix_median = rbind(c("mz name", levels(msidata\$annotation)), sample_matrix_median)
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124 write.table(sample_matrix_median, file="$summarized_median", quote = FALSE, row.names = FALSE, col.names=FALSE, sep = "\t")
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125 #end if
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126
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127 #if "sd" in str($tabular_annotation.summary_type).split(","):
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128 print("summarized sd")
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129
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130 sample_matrix = matrix(,ncol=0, nrow=nrow(msidata))
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131 count = 1
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132 for (subsample in levels(msidata\$annotation)){
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133 subsample_pixels = msidata[,msidata\$annotation == subsample]
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134 subsample_calc = apply(spectra(subsample_pixels),1,sd, na.rm=TRUE)
0
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135 sample_matrix = cbind(sample_matrix, subsample_calc)
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136 count = count+1}
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137 sample_matrix_sd = cbind(mz_names,sample_matrix)
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138 sample_matrix_sd = rbind(c("mz name", levels(msidata\$annotation)), sample_matrix_sd)
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139 write.table(sample_matrix_sd, file="$summarized_sd", quote = FALSE, row.names = FALSE, col.names=FALSE, sep = "\t")
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140 #end if
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141
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142 #end if
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143
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144
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145 ############################ spectra (pixel) output ############################
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146 #if "pixel_tabular" in str($output_options).split(","):
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147 print("pixel output")
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148
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149 ## coordinates
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150 xycoordinates = coord(msidata)[,1:2]
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151
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152 ## pixel name
2
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153 pixel_names = paste0("xy_", xycoordinates\$x, "_", xycoordinates\$y)
0
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154
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155 ## pixel order
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156 pixelxyarray=1:length(pixels(msidata))
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157
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158 ## number of pixels per spectrum: every intensity value > 0 counts as peak
4
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159 peaksperpixel = colSums(spectra(msidata)>0, na.rm=TRUE)
0
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160
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161 ## Total ion chromatogram per spectrum
4
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162 TICs = round(colSums(spectra(msidata), na.rm=TRUE), digits = 2)
2
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163
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164 ## Median ion intensity per spectrum
4
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165 med_int = round(apply(spectra(msidata), 2, median, na.rm=TRUE), digits = 2)
2
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166
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167 ## Maximum ion intensity per spectrum
4
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168 max_int = round(apply(spectra(msidata), 2, max, na.rm=TRUE), digits = 2)
0
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169
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170 ## Highest m/z per spectrum
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171 highestmz = apply(spectra(msidata),2,which.max)
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172 highestmz_data = mz(msidata)[highestmz]
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173
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174 ## Combine into dataframe; order is the same for all vectors
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175 spectra_df = data.frame(pixel_names, xycoordinates, pixelxyarray, peaksperpixel, med_int, TICs, max_int, highestmz_data)
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176 colnames(spectra_df) = c("spectra_names", "x_values", "y_values","pixel_order", "peaks_per_spectrum", "median_intensity", "spectrum_TIC", "maximum_intensity", "most_abundant_mz")
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177
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178 #if str($counting_calibrants.pixel_with_calibrants) == "yes_calibrants":
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179
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180 calibrant_list = read.delim("$counting_calibrants.mz_tabular", header = $counting_calibrants.feature_header, na.strings=c("","NA"), stringsAsFactors = FALSE)
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181 calibrant_list = calibrant_list[,$counting_calibrants.feature_column, drop=FALSE]
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182 ### calculate how many input calibrant m/z are valid:
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183 inputcalibrants = calibrant_list[calibrant_list[,1]>min(mz(msidata)) & calibrant_list[,1]<max(mz(msidata)),,drop = FALSE]
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184 inputcalibrantmasses = inputcalibrants[,1]
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185
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186 ##QC plot number 2) Number of calibrants per spectrum
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187
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188 ## matrix with calibrants in columns and in rows if there is peak intensity in range or not
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189 pixelmatrix = matrix(ncol=ncol(msidata), nrow = 0)
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190
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191 if (length(inputcalibrantmasses) != 0){
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192
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193 ## calculate plusminus values in m/z for each calibrant
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194 plusminusvalues = rep($counting_calibrants.plusminus_ppm/1000000, length(inputcalibrantmasses))*inputcalibrantmasses
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195
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196 ## filter for m/z window of each calibrant and calculate if sum of peak intensities > 0
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197
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198 for (mass in 1:length(inputcalibrantmasses)){
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199 filtered_data = msidata[mz(msidata) >= inputcalibrantmasses[mass]-plusminusvalues[mass] & mz(msidata) <= inputcalibrantmasses[mass]+plusminusvalues[mass],]
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200 if (nrow(filtered_data) > 1 & sum(spectra(filtered_data),na.rm=TRUE) > 0){
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201 ## intensity of all m/z > 0
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202 intensity_sum = colSums(spectra(filtered_data), na.rm=TRUE) > 0
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203
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204 }else if(nrow(filtered_data) == 1 & sum(spectra(filtered_data), na.rm=TRUE) > 0){
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205 ## intensity of only m/z > 0
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206 intensity_sum = spectra(filtered_data) > 0
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207 }else{
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208 intensity_sum = rep(FALSE, ncol(filtered_data))}
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209 ## for each pixel add sum of intensities > 0 in the given m/z range
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210 pixelmatrix = rbind(pixelmatrix, intensity_sum)
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211 }
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212 ## for each pixel count TRUE (each calibrant m/z range with intensity > 0 is TRUE)
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213 countvector= as.factor(apply(pixelmatrix, 2,sum,na.rm=TRUE))
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214
0
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215 }else{countvector = rep(0,ncol(msidata))}
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216 countdf= cbind(coord(msidata)[,1:2], countvector) ## add pixel coordinates to counts
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217 colnames(countdf) = c("x_values", "y_values", "m/z count")
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218 spectra_df = merge(spectra_df, countdf, by=c("x_values", "y_values"))
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219
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220 ## sort columns to have spectra_names as rowname in first column
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221 spectra_df = spectra_df[c("spectra_names", "x_values", "y_values","pixel_order", "peaks_per_spectrum", "median_intensity", "spectrum_TIC", "maximum_intensity", "most_abundant_mz", "m/z count")]
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222
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223 #end if
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224 #if str($tabular_annotation.load_annotation) == 'yes_annotation':
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225
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226 colnames(annotation_input) = c("x_values", "y_values", "annotation")
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227 spectra_df = merge(spectra_df,annotation_input, by=c("x_values", "y_values"), all.x=TRUE)
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228
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229 ## sort columns to have spectra_names as rowname in first column
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230 #if str($counting_calibrants.pixel_with_calibrants) == "yes_calibrants":
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231 spectra_df = spectra_df[c("spectra_names", "x_values", "y_values","pixel_order", "peaks_per_spectrum", "median_intensity", "spectrum_TIC", "maximum_intensity", "most_abundant_mz", "m/z count", "annotation")]
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232 #else
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233 spectra_df = spectra_df[c("spectra_names", "x_values", "y_values","pixel_order", "peaks_per_spectrum", "median_intensity", "spectrum_TIC", "maximum_intensity", "most_abundant_mz", "annotation")]
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234 #end if
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235
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236 #end if
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237 ## sort rows according to original pixel order
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238 spectra_df = spectra_df[match(pixel_names, spectra_df\$spectra_names),]
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239
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240 ## Create list and output tabular
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241 write.table(spectra_df, file="$pixel_output", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
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242 #end if
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243
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244
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245 ]]></configfile>
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246 </configfiles>
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247 <inputs>
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248 <expand macro="reading_msidata"/>
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249 <param name="output_options" type="select" display="checkboxes" optional="False" multiple="true" label="Multiple output files can be selected">
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250 <option value="int_matrix" selected="True" >intensity matrix</option>
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251 <option value="mz_tabular">mz feature output</option>
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252 <option value="pixel_tabular">pixel output</option>
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253 </param>
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254 <conditional name="counting_calibrants">
2
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255 <param name="pixel_with_calibrants" type="select" label="Use file with m/z of interest to calculate their occurrence in each spectrum">
0
28ba52c9548c planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit 0825a4ccd3ebf4ca8a298326d14f3e7b25ae8415
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256 <option value="no_calibrants" selected="True">no</option>
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257 <option value="yes_calibrants">yes</option>
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258 </param>
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259 <when value="no_calibrants"/>
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260 <when value="yes_calibrants">
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261 <expand macro="reading_1_column_mz_tabular" label="For each spectrum the occurrence of the provided m/z values is counted"/>
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262 <param name="plusminus_ppm" value="200" type="float" label="ppm range will be added in both directions to input m/z" help="The m/z window is used to search for peaks, if intensity > 0 found in the window the m/z is considered present, if all intensities are 0 the m/z is considered not present"/>
28ba52c9548c planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit 0825a4ccd3ebf4ca8a298326d14f3e7b25ae8415
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263 </when>
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264 </conditional>
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265 <conditional name="tabular_annotation">
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266 <param name="load_annotation" type="select" label="Pixel annotation can be used to summarize intensities per annotation group">
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267 <option value="no_annotation" selected="True">no</option>
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268 <option value="yes_annotation">yes</option>
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269 </param>
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270 <when value="no_annotation"/>
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271 <when value="yes_annotation">
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272 <expand macro="reading_pixel_annotations"/>
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273 <param name="summary_type" type="select" display="checkboxes" optional="False" multiple="true" label="Calculation for each m/z and all pixels of a annotation group" help="This step will only work if pixel annotations are provided">
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274 <option value="mean">mean</option>
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275 <option value="median">median</option>
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276 <option value="sd">standard deviation</option>
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277 </param>
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278 </when>
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279 </conditional>
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280 </inputs>
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281 <outputs>
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282 <data format="tabular" name="intensity_matrix" label="${tool.name} on ${on_string}: intensity_matrix">
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283 <filter>"int_matrix" in output_options</filter>
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284 </data>
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285 <data format="tabular" name="pixel_output" label="${tool.name} on ${on_string}: spectra">
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286 <filter>"pixel_tabular" in output_options</filter>
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287 </data>
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288 <data format="tabular" name="feature_output" label="${tool.name} on ${on_string}: features">
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289 <filter>"mz_tabular" in output_options</filter>
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290 </data>
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291 <data format="tabular" name="summarized_mean" label="${tool.name} on ${on_string}: group_mean">
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292 <filter>tabular_annotation['load_annotation'] == 'yes_annotation' and 'mean' in tabular_annotation['summary_type']</filter>
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293 </data>
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294 <data format="tabular" name="summarized_median" label="${tool.name} on ${on_string}: group_median">
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295 <filter>tabular_annotation['load_annotation'] == 'yes_annotation' and 'median' in tabular_annotation['summary_type']</filter>
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296 </data>
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297 <data format="tabular" name="summarized_sd" label="${tool.name} on ${on_string}: group_sd">
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298 <filter>tabular_annotation['load_annotation'] == 'yes_annotation' and 'sd' in tabular_annotation['summary_type']</filter>
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299 </data>
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300 </outputs>
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301 <tests>
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302 <test expect_num_outputs="2">
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303 <expand macro="infile_imzml"/>
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304 <param name="output_options" value="int_matrix,mz_tabular"/>
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305 <output name="intensity_matrix" file="int_matrix1.tabular"/>
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306 <output name="feature_output" file="features_out1.tabular"/>
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307 </test>
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308 <test expect_num_outputs="3">
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309 <expand macro="infile_analyze75"/>
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310 <param name="output_options" value="pixel_tabular"/>
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311 <conditional name="tabular_annotation">
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312 <param name="load_annotation" value="yes_annotation"/>
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313 <param name="annotation_file" value="annotations.tabular"/>
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314 <param name="column_x" value="1"/>
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315 <param name="column_y" value="2"/>
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316 <param name="column_names" value="4"/>
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317 <param name="tabular_header" value="True"/>
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318 <param name="summary_type" value="mean,sd"/>
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319 </conditional>
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320 <output name="pixel_output" file="pixel_out2.tabular"/>
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321 <output name="summarized_mean" file="mean_out2.tabular"/>
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322 <output name="summarized_sd" file="sd_out2.tabular"/>
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323 </test>
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324 <test expect_num_outputs="3">
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325 <expand macro="infile_imzml"/>
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326 <param name="output_options" value="int_matrix,pixel_tabular,mz_tabular"/>
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327 <conditional name="counting_calibrants">
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328 <param name="pixel_with_calibrants" value="yes_calibrants"/>
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329 <param name="mz_tabular" value="inputcalibrantfile2.txt"/>
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330 <param name="feature_column" value="1"/>
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331 <param name="feature_header" value="False"/>
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332 <param name="plusminus_ppm" value="200"/>
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333 </conditional>
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334 <output name="intensity_matrix" file="int_matrix3.tabular"/>
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335 <output name="feature_output" file="features_out3.tabular"/>
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336 <output name="pixel_output" file="pixel_out3.tabular"/>
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337 </test>
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338 </tests>
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339 <help>
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340 <![CDATA[
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341
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342 @CARDINAL_DESCRIPTION@
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343
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344 -----
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345
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346 This tool provides multiple tabular output options for mass spectrometry imaging data files.
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347
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348 @MSIDATA_INPUT_DESCRIPTION@
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349
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350 @SPECTRA_TABULAR_INPUT_DESCRIPTION@
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351
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352 @MZ_TABULAR_INPUT_DESCRIPTION@
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353
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354 **Output options**
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355
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356 - intensity matrix: m/z in rows, spectra in columns, filled with intensity values
2
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357 - spectra output: spectra in rows - for each spectrum: name, x and y coordinates,order, number of peaks (intensities > 0), total ion chromatogram (TIC), median intensity, maximum intensity, highest m/z feature per spectrum, optional count of m/z per spectrum, optional spectrum annotation
0
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358 - mz feature output: m/z in rows - for each m/z: name, m/z, mean, median, standard deviation (sd), standard error of the mean (sem), sum of all intensities per m/z, number of peaks (intensity > 0) per m/z
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359 - summarized intensities: pixel annotations will be used to group spectra into annotation groups and calculate mean, median and sd of the intensities per group
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360
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361 ]]>
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362 </help>
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363 <expand macro="citations"/>
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364 </tool>