Mercurial > repos > ethevenot > profia
diff profia_config.xml @ 2:3f8ae071bdda draft
planemo upload for repository https://github.com/workflow4metabolomics/profia.git commit 19ed25c048232776369a392ddb8c1860471acd29
| author | ethevenot |
|---|---|
| date | Mon, 22 Jan 2018 11:32:41 -0500 |
| parents | 4753e64cf694 |
| children | de9d1270a9ae |
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--- a/profia_config.xml Wed May 03 10:49:08 2017 -0400 +++ b/profia_config.xml Mon Jan 22 11:32:41 2018 -0500 @@ -1,315 +1,367 @@ -<tool id="profia" name="proFIA" version="3.0.4"> - <description>Preprocessing of FIA-HRMS data</description> - - <requirements> - <requirement type="package">r-batch</requirement> - <requirement type="package">r-FNN</requirement> - <requirement type="package">r-maxLik</requirement> - <requirement type="package">r-minpack.lm</requirement> - <requirement type="package">r-pracma</requirement> - <requirement type="package">bioconductor-xcms</requirement> - <requirement type="package">bioconductor-plasFIA</requirement> - <requirement type="package">bioconductor-proFIA</requirement> - </requirements> - - <stdio> - <exit_code range="1:" level="fatal" /> - </stdio> - - <command> - Rscript $__tool_directory__/profia_wrapper.R - - #if $inputs.input == "lib": - library $__app__.config.user_library_import_dir/$__user_email__/$inputs.library - #elif $inputs.input == "zip_file": - zipfile $inputs.zip_file - #end if - - ppmN "$ppmN" - ppmGroupN "$ppmGroupN" - fracGroupN "$fracGroupN" - kI "$kI" - - dataMatrix_out "$dataMatrix_out" - sampleMetadata_out "$sampleMetadata_out" - variableMetadata_out "$variableMetadata_out" - figure "$figure" - information "$information" - </command> - - <inputs> - <conditional name="inputs"> - <param name="input" type="select" label="Choose your input method" > - <option value="zip_file" selected="true">Zip file from your history containing your raw files</option> - <option value="lib" >Library directory name</option> - </param> - <when value="zip_file"> - <param name="zip_file" type="data" format="no_unzip.zip,zip" label="Zip file (see the details for file upload in the help section below)" /> - </when> - <when value="lib"> - <param name="library" type="text" size="40" label="Library directory name" help="The name of your directory containing all your data" > - <validator type="empty_field"/> - </param> - </when> - </conditional> - - <param name="ppmN" label="Maximum deviation between centroids during band detection (in ppm)" type="text" value = "5" help="[ppm]" /> - <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." /> - <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]" /> - <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]" /> - </inputs> - - <outputs> - <data name="dataMatrix_out" label="${tool.name}_dataMatrix.tsv" format="tabular" ></data> - <data name="sampleMetadata_out" label="${tool.name}_sampleMetadata.tsv" format="tabular" ></data> - <data name="variableMetadata_out" label="${tool.name}_variableMetadata.tsv" format="tabular" ></data> - <data name="figure" label="${tool.name}_figure.pdf" format="pdf"/> - <data name="information" label="${tool.name}_information.txt" format="txt"/> - </outputs> - - <tests> - <test> - <param name="inputs|input" value="zip_file" /> - <param name="inputs|zip_file" value="input-plasFIA.zip" ftype="zip" /> - <param name="ppmN" value="2"/> - <param name="ppmGroupN" value="1"/> - <param name="fracGroupN" value="0.1"/> - <param name="kI" value="2"/> - <output name="dataMatrix_out" file="output-dataMatrix.tsv" /> - <output name="information"> - <assert_contents> - <has_text text="722 groups have been done" /> - <has_text text="3 samples x 644 variables" /> - <has_text text="78 excluded variables (near zero variance)" /> - <has_text text="2101 peaks detected" /> - </assert_contents> - </output> - </test> - </tests> - - <help> - -.. class:: infomark - -**Author** Alexis Delabriere and Etienne Thevenot (CEA, LIST, MetaboHUB Paris, etienne.thevenot@cea.fr) - ---------------------------------------------------- - -.. class:: infomark - -**Please cite** - -Delabriere A., Hohenester U., Colsch B., Junot C., Fenaille F. and Thevenot E.A. *proFIA*: A data preprocessing workflow for Flow Injection Analysis coupled to High-Resolution Mass Spectrometry. *submitted*. - ---------------------------------------------------- - -.. class:: infomark - -**R package** - -The **proFIA** package is available from the bioconductor repository `http://bioconductor.org/packages/proFIA <http://bioconductor.org/packages/proFIA>`_ - ---------------------------------------------------- - -.. class:: infomark - -**Tool updates** - -See the **NEWS** section at the bottom of this page - ---------------------------------------------------- - -========================================================== -*proFIA*: A preprocessing workflow for FIA-HRMS data -========================================================== - ------------ -Description ------------ - -**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. - -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. - -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. - -A figure provides **diagnostics** and visualization of the preprocessed data set. - ---------------------------------------------------- - -.. class:: infomark - -**References** - -| 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*. -| 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. (http://dx.doi.org/10.1007/s11306-012-0449-x) -| 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. (http://dx.doi.org/10.1021/ac201267k) -| 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. (http://dx.doi.org/10.1021/ac7024915) - ---------------------------------------------------- - ------------------ -Workflow position ------------------ - -.. image:: profia_workflowPositionImage.png - :width: 600 - ------------ -Input files ------------ - -+---------------------------+------------+ -| Parameter : num + label | Format | -+===========================+============+ -| 1 : Choose your inputs | zip | -+---------------------------+------------+ - ---------------------------------------------------- - -.. class:: warningmark - -VERY IMPORTANT: Your data must be in **centroid** mode (centroidization of raw files and conversion to an open format can be achieved with the proteowizard software: http://proteowizard.sourceforge.net/). - - -You have two methods for your inputs: - | Zip file (recommended): You can put a zip file containing your inputs: myinputs.zip (containing all your conditions as sub-directories; see below). - | 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. - -**Steps for creating the zip file** - -**Step1: Creating your directory and hierarchize the subdirectories** - -.. class:: warningmark - -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). - -1a) Prepare a parent folder with the name of your data set (e.g., 'arabidopsis') containing your files: - | 'arabidopsis/w1.raw' - | 'arabidopsis/w2.raw' - | ... - | 'arabidopsis/m1.raw' - | 'arabidopsis/m2.raw' - | ... - | - -1b) If you have several experimental conditions resulting in distinct profiles of your samples (e.g. 'wild-type' and 'mutant' genotypes), create subfolders for your files (e.g., 'wild' and 'mutant') into your parent folder: - | 'arabidopsis/wild/w1.raw' - | 'arabidopsis/wild/w2.raw' - | ... - | 'arabidopsis/mutant/m1.raw' - | 'arabidopsis/mutant/m2.raw' - | ... - | - -**Step2: Creating a zip file** - | Zip your **parent** folder (here the 'arabidopsis' folder) containing all the subfolders and files with **7Zip**. - | - -**Step 3 : Uploading it to our Galaxy server** - | If your zip file is less than 2Gb, you get use the **Upload File** tool and the **no_unzip.zip** type to upload it. - | 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). - | For more informations, don't hesitate to send us an email at supportATworkflow4metabolomics.org). - | - ----------- -Parameters ----------- - -Maximum deviation between centroids during band detection; in ppm (default = 5) - | m/z tolerance of centroids corresponding to the same ion from one scan to the other. - | - -Accuracy of the mass spectrometer to be used during feature alignment; in ppm (default = 5) - | Should be inferior or equal to the deviation parameter above. - | - -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) - | Identical to the corresponding parameter in XCMS. - | - -Number of neighbour features to be used for imputation (default = 5) - | Select 0 to skip the imputation step. - | - - ------------- -Output files ------------- - -dataMatrix.tabular - | **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). - | - -sampleMetadata.tabular - | **sampleMetadata** tabular separated file containing the sample metadata as columns. - | - -variableMetadata.tabular - | **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. - | - -figure.pdf - | 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. - | - -information.txt - | Text file with all messages and warnings generated during the computation. - | - ---------------------------------------------------- - ---------------- -Working example ---------------- - -Figure output -============= - -.. image:: profia_workingExampleImage.png - :width: 600 - ---------------------------------------------------- - ----- -NEWS ----- - -CHANGES IN VERSION 3.0.4 -======================== - -MINOR MODIFICATION - -Details added in the documentation - -CHANGES IN VERSION 3.0.2 -======================== - -NEW FEATURE - -Parallel processing - - -CHANGES IN VERSION 3.0.0 -======================== - -NEW FEATURE - -Creation of the tool - -</help> - -<citations> - <citation type="bibtex">@Article{DelabriereSubmitted, - Title = {proFIA: A data preprocessing workflow for Flow Injection Analysis coupled to High-Resolution Mass Spectrometry}, - Author = {Delabriere, Alexis and Hohenester, Ulli and Colsch, Benoit and Junot, Christophe and Fenaille, Francois and Thevenot, Etienne A}, - Journal = {submitted}, - Year = {submitted}, - Pages = {--}, - Volume = {}, - Doi = {} - }</citation> - <citation type="doi">10.1093/bioinformatics/btu813</citation> -</citations> - -</tool> +<tool id="profia" name="proFIA" version="3.1.0"> + <description>Preprocessing of FIA-HRMS data</description> + + <requirements> + <requirement type="package">r-batch</requirement> + <requirement type="package">bioconductor-proFIA</requirement> + </requirements> + + <stdio> + <exit_code range="1:" level="fatal" /> + </stdio> + + <command> + Rscript $__tool_directory__/profia_wrapper.R + + #if $inputs.input == "lib": + library $__app__.config.user_library_import_dir/$__user_email__/$inputs.library + #elif $inputs.input == "zip_file": + zipfile $inputs.zip_file + #end if + + ppmN "$ppmN" + dmzN "$dmzN" + ppmGroupN "$ppmGroupN" + dmzGroupN "$dmzGroupN" + fracGroupN "$fracGroupN" + imputeC "$imputeC" + + #if $advCpt.opcC == "full" + bandCoverageN "$advCpt.bandCoverageN" + sizeMinN "$advCpt.sizeMinN" + scanMinI "$advCpt.scanMinI" + scanMaxI "$advCpt.scanMaxI" + #end if + + dataMatrix_out "$dataMatrix_out" + sampleMetadata_out "$sampleMetadata_out" + variableMetadata_out "$variableMetadata_out" + figure "$figure" + information "$information" + </command> + + <inputs> + <conditional name="inputs"> + <param name="input" type="select" label="Choose your input method" > + <option value="zip_file" selected="true">Zip file from your history containing your raw files</option> + <option value="lib" >Library directory name</option> + </param> + <when value="zip_file"> + <param name="zip_file" type="data" format="no_unzip.zip,zip" label="Zip file (see the details for file upload in the help section below)" /> + </when> + <when value="lib"> + <param name="library" type="text" size="40" label="Library directory name" help="The name of your directory containing all your data" > + <validator type="empty_field"/> + </param> + </when> + </conditional> + + <param name="ppmN" label="Maximum deviation between centroids during band detection (in ppm)" type="text" value = "7" help="[ppm]" /> + <param name="dmzN" label="Minimal maximum deviation between centroids during band detection (in Da)" type="text" value = "0.001" help="[dmz] shloud be at most 0.002 for high resolution" /> + <param name="ppmGroupN" label="Accuracy of the mass spectrometer to be used during feature alignment (in ppm)" type="text" value = "3" help="[ppmGroup] Should be inferior to the ppm parameter above." /> + <param name="dmzGroupN" label="Minimal accuracy of the mass spectrometer to be used during feature alignment (in Da)" type="text" value = "0.0005" help="[dmzGroup] shloud be at most 0.002 for high resolution" /> + <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]" /> + <param name="imputeC" label="Imputation method" type="select" help="[imputation]"> + <option value="None">None</option> + <option value="randomForest" selected="true">randomForest</option> + </param> + + + + <conditional name="advCpt"> + <param name="opcC" type="select" label="Advanced parameters" > + <option value="default" selected="true">Use default</option> + <option value="full">Full parameter list</option> + </param> + <when value="default"/> + <when value="full"> + <param name="bandCoverageN" type="float" value="0.3" label="Minimum fraction of centroids in the estimated injection window for a band to be built" help="[bandCoverage] Must be between 0 and 1"/> + <param name="sizeMinN" type="text" value="none" label="Minimum number of consecutive centroids for a band to be built" help="[sizeMin] If set to 'none', the half of the estimated injection window will be used"/> + <param name="scanMinI" type="integer" value="1" label="First scan to be preprocessed" help="[scanMin]"/> + <param name="scanMaxI" type="text" value="none" label="Last scan to be preprocessed" help="[scanMax] Set to 'none' to preprocess up to the last acquired scan"/> + </when> + </conditional> + + + + </inputs> + + <outputs> + <data name="dataMatrix_out" label="${tool.name}_dataMatrix.tsv" format="tabular" ></data> + <data name="sampleMetadata_out" label="${tool.name}_sampleMetadata.tsv" format="tabular" ></data> + <data name="variableMetadata_out" label="${tool.name}_variableMetadata.tsv" format="tabular" ></data> + <data name="figure" label="${tool.name}_figure.pdf" format="pdf"/> + <data name="information" label="${tool.name}_information.txt" format="txt"/> + </outputs> + + <tests> + <test> + <param name="inputs|input" value="zip_file" /> + <param name="inputs|zip_file" value="input-plasFIA.zip" ftype="zip" /> + <param name="ppmN" value="2"/> + <param name="dmzN" value="0.0005"/> + <param name="ppmGroupN" value="1"/> + <param name="dmzGroupN" value="0.0005"/> + <param name="fracGroupN" value="0.1"/> + <param name="imputeC" value="randomForest"/> + <output name="dataMatrix_out" file="output-dataMatrix.tsv" /> + <output name="information"> + <assert_contents> + <has_text text="707 groups have been done" /> + <has_text text="3 samples x 707 variables" /> + <has_text text="2089 peaks detected" /> + </assert_contents> + </output> + </test> + </tests> + + <help> + +.. class:: infomark + +**Author** Alexis Delabriere and Etienne Thevenot (CEA, LIST, MetaboHUB Paris, etienne.thevenot@cea.fr) + +--------------------------------------------------- + +.. class:: infomark + +**Please cite** + +Delabriere A., Hohenester U., Colsch B., Junot C., Fenaille F. and Thevenot E.A. (2017). *proFIA*: A data preprocessing workflow for Flow Injection Analysis coupled to High-Resolution Mass Spectrometry. *Bioinformatics*, **33**:3767-3775. `https://doi.org/10.1093/bioinformatics/btx458 <https://doi.org/10.1093/bioinformatics/btx458>`_ + +--------------------------------------------------- + +.. class:: infomark + +**R package** + +The **proFIA** package is available from the bioconductor repository `http://bioconductor.org/packages/proFIA <http://bioconductor.org/packages/proFIA>`_ + +--------------------------------------------------- + +.. class:: infomark + +**Tool updates** + +See the **NEWS** section at the bottom of this page + +--------------------------------------------------- + +========================================================== +*proFIA*: A preprocessing workflow for FIA-HRMS data +========================================================== + +----------- +Description +----------- + +**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. + +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. + +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. + +A figure provides **diagnostics** and visualization of the preprocessed data set. + +--------------------------------------------------- + +.. class:: infomark + +**References** + +| Delabriere A., Hohenester U., Junot C. and Thevenot E.A. (2017). proFIA: A data preprocessing workflow for Flow Injection Analysis coupled to High-Resolution Mass Spectrometry. *Bioinformatics*, **33**:3767-3775. (https://doi.org/10.1093/bioinformatics/btx458) +| 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. (https://doi.org/10.1007/s11306-012-0449-x) +| 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. (https://doi.org/10.1021/ac201267k) +| 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. (https://doi.org/10.1021/ac7024915) + +--------------------------------------------------- + +----------------- +Workflow position +----------------- + +.. image:: profia_workflowPositionImage.png + :width: 600 + +----------- +Input files +----------- + ++---------------------------+------------+ +| Parameter : num + label | Format | ++===========================+============+ +| 1 : Choose your inputs | zip | ++---------------------------+------------+ + +--------------------------------------------------- + +.. class:: warningmark + +VERY IMPORTANT: Your data must be in **centroid** mode (centroidization of raw files and conversion to an open format can be achieved with the proteowizard software: http://proteowizard.sourceforge.net/). + + +You have two methods for your inputs: + | Zip file (recommended): You can put a zip file containing your inputs: myinputs.zip (containing all your conditions as sub-directories; see below). + | 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. + +**Steps for creating the zip file** + +**Step1: Creating your directory and hierarchize the subdirectories** + +.. class:: warningmark + +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). + +1a) Prepare a parent folder with the name of your data set (e.g., 'arabidopsis') containing your files: + | 'arabidopsis/w1.raw' + | 'arabidopsis/w2.raw' + | ... + | 'arabidopsis/m1.raw' + | 'arabidopsis/m2.raw' + | ... + | + +1b) If you have several experimental conditions resulting in distinct profiles of your samples (e.g. 'wild-type' and 'mutant' genotypes), create subfolders for your files (e.g., 'wild' and 'mutant') into your parent folder: + | 'arabidopsis/wild/w1.raw' + | 'arabidopsis/wild/w2.raw' + | ... + | 'arabidopsis/mutant/m1.raw' + | 'arabidopsis/mutant/m2.raw' + | ... + | + +**Step2: Creating a zip file** + | Zip your **parent** folder (here the 'arabidopsis' folder) containing all the subfolders and files with **7Zip**. + | + +**Step 3 : Uploading it to our Galaxy server** + | If your zip file is less than 2Gb, you get use the **Upload File** tool and the **no_unzip.zip** type to upload it. + | 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). + | For more informations, don't hesitate to send us an email at supportATworkflow4metabolomics.org). + | + +---------- +Parameters +---------- + +Maximum deviation between centroids during band detection; in ppm (default = 7) + | m/z tolerance of centroids corresponding to the same ion from one scan to the other. + | + +Minimal maximum deviation between centroids during band detection; in Da (default = 0.001); to avoid bias at low mass values, the deviation is the maximum between this quantity and the deviation in ppm + | minimum m/z tolerance of centroids corresponding to the same ion from one scan to the other. + | + +Accuracy of the mass spectrometer to be used during feature alignment; in ppm (default = 3); should be less than the ppm parameter used for detection + | Should be inferior or equal to the ppm deviation parameter above. + | + +Minimal accuracy of the mass spectrometer to be used during feature alignment; in Da (default = 0.0005); to avoid bias at low mass values; the deviation is the maximum between this quantity and the deviation in ppm + | + +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) + | Identical to the corresponding parameter in XCMS. + | + +Imputation method for missing values (default = 'randomForest') + | + +Minimum fraction of centroids in the estimated injection window for a band to be built (advanced; default = 0.3) + | + +Minimum number of consecutive centroids for a band to be built (advanced; default = half of the size of the estimated injection window) + | + +First scan to be preprocessed (advanced; default = 1) + | + +Last scan to be preprocessed (advanced; default = last acquisition scan) + | + +------------ +Output files +------------ + +dataMatrix.tabular + | **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). + | + +sampleMetadata.tabular + | **sampleMetadata** tabular separated file containing the sample metadata as columns. + | + +variableMetadata.tabular + | **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. + | + +figure.pdf + | 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. + | + +information.txt + | Text file with all messages and warnings generated during the computation. + | + +--------------------------------------------------- + +--------------- +Working example +--------------- + +Figure output +============= + +.. image:: profia_workingExampleImage.png + :width: 600 + +--------------------------------------------------- + +---- +NEWS +---- + +CHANGES IN VERSION 3.1.0 +======================== + +NEW FEATURE + +randomForest method implemented for imputation of missing values + +CHANGES IN VERSION 3.0.6 +======================== + +NEW FEATURE + +dmz (and dmzGroup) parameters added for the peak detection and grouping steps; bandCoverage, sizeMin, scanMin, and scanMax added as advanced parameters for peak detection + + +CHANGES IN VERSION 3.0.4 +======================== + +MINOR MODIFICATION + +Details added in the documentation + +CHANGES IN VERSION 3.0.2 +======================== + +NEW FEATURE + +Parallel processing + + +CHANGES IN VERSION 3.0.0 +======================== + +NEW FEATURE + +Creation of the tool + +</help> + +<citations> + <citation type="doi">10.1093/bioinformatics/btx458</citation> + <citation type="doi">10.1093/bioinformatics/btu813</citation> +</citations> + +</tool>