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annotate profia_config.xml @ 0:39ccace77270 draft

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planemo upload for repository https://github.com/workflow4metabolomics/profia.git commit 2757590af8c7ba9833ba3bebd7da7f96b20d1128-dirty
author ethevenot
date Sun, 26 Mar 2017 17:37:12 -0400
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children 4753e64cf694
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39ccace77270 planemo upload for repository https://github.com/workflow4metabolomics/profia.git commit 2757590af8c7ba9833ba3bebd7da7f96b20d1128-dirty
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1 <tool id="profia" name="proFIA" version="3.0.0">
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2 <description>Preprocessing of FIA-HRMS data</description>
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3
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4 <requirements>
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5 <requirement type="package">r-batch</requirement>
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6 <requirement type="package">r-FNN</requirement>
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7 <requirement type="package">r-maxLik</requirement>
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8 <requirement type="package">r-minpack.lm</requirement>
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9 <requirement type="package">r-pracma</requirement>
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10 <requirement type="package">bioconductor-proFIA</requirement>
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11 </requirements>
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13 <stdio>
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14 <exit_code range="1:" level="fatal" />
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15 </stdio>
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17 <command><![CDATA[
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18 Rscript $__tool_directory__/profia_wrapper.R
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20 #if $inputs.input == "lib":
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21 library $__app__.config.user_library_import_dir/$__user_email__/$inputs.library
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22 #elif $inputs.input == "zip_file":
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23 zipfile $inputs.zip_file
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24 #end if
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26 ppmN "$ppmN"
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27 ppmGroupN "$ppmGroupN"
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28 fracGroupN "$fracGroupN"
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29 kI "$kI"
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31 dataMatrix_out "$dataMatrix_out"
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32 sampleMetadata_out "$sampleMetadata_out"
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33 variableMetadata_out "$variableMetadata_out"
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34 figure "$figure"
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35 information "$information"
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36 ]]></command>
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38 <inputs>
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39 <conditional name="inputs">
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40 <param name="input" type="select" label="Choose your input method" >
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41 <option value="zip_file" selected="true">Zip file from your history containing your raw files</option>
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42 <option value="lib" >Library directory name</option>
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43 </param>
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44 <when value="zip_file">
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45 <param name="zip_file" type="data" format="no_unzip.zip,zip" label="Zip file" />
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46 </when>
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47 <when value="lib">
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48 <param name="library" type="text" size="40" label="Library directory name" help="The name of your directory containing all your data" >
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49 <validator type="empty_field"/>
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50 </param>
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51 </when>
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52 </conditional>
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53
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54 <param name="ppmN" label="Maximum deviation between centroids during band detection (in ppm)" type="text" value = "5" help="[ppm]" />
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55 <param name="ppmGroupN" label="Accuracy of the mass spectrometer to be used during feature alignment (in ppm)" type="text" value = "5" help="[ppmGroup] Should be inferior or equal to the deviation parameter above." />
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56 <param name="fracGroupN" label=" Minimum fraction of samples in which a peak should be detected in at least one class to be kept during feature alignment" type="text" value = "0.5" help="[fracGroup]" />
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57 <param name="kI" label="Number of neighbour features to be used for imputation (select 0 to skip the imputation step)" type="text" value = "5" help="[k]" />
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58 </inputs>
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59
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60 <outputs>
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61 <data name="dataMatrix_out" label="${tool.name}_dataMatrix.tsv" format="tabular" ></data>
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62 <data name="sampleMetadata_out" label="${tool.name}_sampleMetadata.tsv" format="tabular" ></data>
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63 <data name="variableMetadata_out" label="${tool.name}_variableMetadata.tsv" format="tabular" ></data>
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64 <data name="figure" label="${tool.name}_figure.pdf" format="pdf"/>
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65 <data name="information" label="${tool.name}_information.txt" format="txt"/>
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66 </outputs>
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67
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68 <tests>
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69 <test>
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70 <param name="inputs|input" value="zip_file" />
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71 <param name="inputs|zip_file" value="input-plasFIA.zip" ftype="zip" />
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72 <param name="ppmN" value="2"/>
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73 <param name="ppmGroupN" value="1"/>
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74 <param name="fracGroupN" value="0.1"/>
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75 <param name="kI" value="2"/>
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76 <output name="dataMatrix_out" file="output-dataMatrix.tsv"/>
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77 </test>
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78 </tests>
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80 <help>
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82 .. class:: infomark
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83
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84 **Author** Alexis Delabriere and Etienne Thevenot (CEA, LIST, MetaboHUB Paris, etienne.thevenot@cea.fr)
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86 ---------------------------------------------------
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88 .. class:: infomark
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90 **Please cite**
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92 Delabriere A., Hohenester U., Junot C. and Thevenot E.A. *proFIA*: A data preprocessing workflow for Flow Injection Analysis coupled to High-Resolution Mass Spectrometry. *submitted*.
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94 ---------------------------------------------------
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95
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96 .. class:: infomark
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97
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98 **R package**
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99
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100 The **proFIA** package is available from the bioconductor repository `http://bioconductor.org/packages/proFIA &lt;http://bioconductor.org/packages/proFIA&gt;`_
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101
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102 ---------------------------------------------------
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103
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104 .. class:: infomark
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105
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106 **Tool updates**
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107
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108 See the **NEWS** section at the bottom of this page
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109
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110 ---------------------------------------------------
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111
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112 ==========================================================
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113 *proFIA*: Preprocessing workflow for FIA-HRMS data
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114 ==========================================================
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115
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116 -----------
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117 Description
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118 -----------
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119
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120 **Flow Injection Analysis coupled to High-Resolution Mass Spectrometry (FIA-HRMS)** is a promising approach for **high-throughput metabolomics** (Madalinski *et al.*, 2008; Fuhrer *et al.*, 2011; Draper *et al.*, 2013). FIA- HRMS data, however, cannot be preprocessed with current software tools which rely on liquid chromatography separation, or handle low resolution data only.
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121
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122 The **proFIA module is a workflow** allowing to preprocess FIA-HRMS raw data in **centroid** mode and open format (netCDF, mzData, mzXML, and mzML), and generates the table of peak intensities (**peak table**). The workflow consists in **peak detection and quantification** within individual sample files, followed by **alignment** between files in the m/z dimension, and **imputation** of the missing values in the final peak table (Delabriere *et al.*, submitted). For each ion, the graph representing the intensity as a function of time is called a **flowgram**. A flowgram can be modeled as I = kP + ME(P) + B + e, where k is the response factor (corresponding to the ionization properties of the analyte), P is the **sample peak** (normalized profile which is common for all analytes from a sample and depends on the flow injection conditions only), ME is the **matrix effect**, B is the **solvent baseline**, and e is the heteroscedastic noise.
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123
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124 The generated peak table is available in the '3 table' W4M tabular format (**dataMatrix**, **sampleMetadata**, and **variableMetadata**) for downstream statistical analysis and annotation with W4M modules.
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125
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126 A figure provides **diagnostics** and visualization of the preprocessed data set.
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127
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128 ---------------------------------------------------
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129
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130 .. class:: infomark
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131
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132 **References**
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133
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134 | Delabriere A., Hohenester U., Junot C. and Thevenot E.A. proFIA: A data preprocessing workflow for Flow Injection Analysis coupled to High-Resolution Mass Spectrometry. *submitted*.
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135 | Draper J., Lloyd A., Goodacre R. and Beckmann M. (2013). Flow infusion electrospray ionisation mass spectrometry for high throughput, non-targeted metabolite fingerprinting: a review. *Metabolomics* 9, 4-29.
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136 | Fuhrer T., Dominik H., Boris B. and Zamboni N. (2011). High-throughput, accurate mass metabolome profiling of cellular extracts by flow injection-time-of-flight mass spectrometry. *Analytical Chemistry* 83, 7074-7080.
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137 | Madalinski G., Godat E., Alves S., Lesage D., Genin E., Levi P., Labarre J., Tabet J., Ezan E. and Junot, C. (2008). Direct introduction of biological samples into a LTQ-orbitrap hybrid mass spectrometer as a tool for fast metabolome analysis. *Analytical Chemistry* 80, 3291-3303.
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138
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139 ---------------------------------------------------
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140
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141 -----------------
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142 Workflow position
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143 -----------------
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144
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145 .. image:: profia_workflowPositionImage.png
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146 :width: 600
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147
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148 -----------
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149 Input files
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150 -----------
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151
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152 +---------------------------+------------+
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153 | Parameter : num + label | Format |
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154 +===========================+============+
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155 | 1 : Choose your inputs | zip |
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156 +---------------------------+------------+
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157
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158
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159 You have two methods for your inputs:
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160 | Zip file (recommended): You can put a zip file containing your inputs: myinputs.zip (containing all your conditions as sub-directories).
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161 | library folder: You must specify the name of your "library" (folder) created within your space project (for example: /projet/externe/institut/login/galaxylibrary/yourlibrary). Your library must contain all your conditions as sub-directories.
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162
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163 **Steps for creating the zip file**
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164
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165 **Step1: Creating your directory and hierarchize the subdirectories**
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166
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167 .. class:: warningmark
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168
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169 VERY IMPORTANT: If you zip your files under Windows, you must use the **7Zip** software (http://www.7-zip.org/), otherwise your zip will not be well unzipped on the platform W4M (zip corrupted bug).
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170 Your zip should contain all your conditions as sub-directories. For example, two conditions (mutant and wild):
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171 arabidopsis/wild/01.raw
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172 arabidopsis/mutant/01.raw
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173
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174 **Step2: Creating a zip file**
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175 Create your zip file (e.g.: arabidopsis.zip).
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176
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177 **Step 3 : Uploading it to our Galaxy server**
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178 If your zip file is less than 2Gb, you get use the Get Data tool to upload it.
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179 Otherwise if your zip file is larger than 2Gb, please refer to the HOWTO on workflow4metabolomics.org (http://application.sb-roscoff.fr/download/w4m/howto/galaxy_upload_up_2Go.pdf).
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180 For more informations, don't hesitate to send us an email at supportATworkflow4metabolomics.org).
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181
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182 **Advices for converting your files for the XCMS input**
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183
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184 .. class:: warningmark
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185
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186 VERY IMPORTANT: your data must be in **centroid** mode. In addition, we recommend you to convert your raw files to mzXML.
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187
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188 We recommend the following parameters:
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189
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190 Use Filtering: **True**
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191 Use Peak Picking: **True**
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192 Peak Peaking -Apply to MS Levels: **All Levels (1-)** : Centroid Mode
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193 Use zlib: **64**
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194 Binary Encoding: **64**
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195 m/z Encoding: **64**
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196 Intensity Encoding: **64**
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197
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198 ----------
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199 Parameters
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200 ----------
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201
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202 Maximum deviation between centroids during band detection; in ppm (default = 5)
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203 | m/z tolerance of centroids corresponding to the same ion from one scan to the other.
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204 |
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205
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206 Accuracy of the mass spectrometer to be used during feature alignment; in ppm (default = 5)
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207 | Should be inferior or equal to the deviation parameter above.
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208 |
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209
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210 Minimum fraction of samples in which a peak should be detected in at least one class to be kept during feature alignment (default = 0.5)
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211 | Identical to the corresponding parameter in XCMS.
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212 |
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213
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214 Number of neighbour features to be used for imputation (default = 5)
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215 | Select 0 to skip the imputation step.
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216 |
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217
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218
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219 ------------
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220 Output files
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221 ------------
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222
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223 dataMatrix.tabular
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224 | **dataMatrix** tabular separated file with the variables as rows and samples as columns. Missing values are indicated as 'NA' (i.e. when the signal was not significantly different from noise).
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225 |
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226
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227 sampleMetadata.tabular
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228 | **sampleMetadata** tabular separated file containing the sample metadata as columns.
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229 |
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230
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231 variableMetadata.tabular
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232 | **variableMetadata** tabular separated file containing the variable metadata as columns. The **timeShifted** flag is set to 1 when the flowgram is time shifted compared to the sample peak (probably due to liquid retention in the FI tube). The **corSampPeakMean** metric is the correlation between the feature flowgram and the sample peak (values are in [-1, 1]). A value below 0.2 suggests that the feature signal is affected by a strong matrix effect. The **meanSolvent** is the mean baseline signal in the feature flowgrams. The **signalOverSolventPvalueMean** is the mean p-value of the tests discriminating between signal and baseline solvent.
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233 |
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234
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235 figure.pdf
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236 | Visualization and diagnostics about the preprocessed data set; **Feature quality**: Number of detected features per sample for each of the three categories: 'Well-behaved' features have a peak shape close to the sample peak (optimal FIA acquisition is achieved when the majority of the features fall into this category); 'Shifted' indicates a time shift compared to the sample peak, and probably results from retention in the FI tube; 'Significant Matrix Effect' corresponds to a correlation between the feature and the samples peaks of less than 0.2, which is usually caused by a strong matrix effect; **Sample peaks**: Visualization of the peak model for each sample; should have close shapes in case of similar FIA conditions; **m/z density**: may allow to detect a missing m/z value, and in turn, suggest that the *ppm* parameter should be modified; **PCA score plot** of the log10 intensities to detect sample outliers.
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237 |
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238
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239 information.txt
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240 | Text file with all messages and warnings generated during the computation.
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241 |
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242
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243 ---------------------------------------------------
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244
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245 ---------------
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246 Working example
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247 ---------------
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248
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249 Figure output
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250 =============
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251
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252 .. image:: profia_workingExampleImage.png
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253 :width: 600
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254
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255 ---------------------------------------------------
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256
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257 ----
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258 NEWS
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259 ----
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260
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261 CHANGES IN VERSION 3.0.0
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262 ========================
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263
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264 NEW FEATURE
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265
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266 Creation of the tool
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267
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268 </help>
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269
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270 <citations>
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271 <citation type="bibtex">@Article{DelabriereSubmitted,
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272 Title = {proFIA: A data preprocessing workflow for Flow Injection Analysis coupled to High-Resolution Mass Spectrometry},
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273 Author = {Delabriere, Alexis and Hohenester, Ulli and Junot, Christophe and Thevenot, Etienne A},
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274 Journal = {submitted},
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275 Year = {submitted},
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276 Pages = {--},
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277 Volume = {},
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278 Doi = {}
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279 }</citation>
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280 <citation type="doi">10.1093/bioinformatics/btu813</citation>
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281 </citations>
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282
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283 </tool>