Mercurial > repos > ethevenot > profia
changeset 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 |
| files | README.md profia_config.xml profia_wrapper.R runit/output/dataMatrix.tsv runit/output/figure.pdf runit/output/information.txt runit/output/sampleMetadata.tsv runit/output/variableMetadata.tsv runit/profia_runtests.R runit/profia_tests.R test-data/output-dataMatrix.tsv |
| diffstat | 11 files changed, 2610 insertions(+), 2493 deletions(-) [+] |
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--- a/README.md Wed May 03 10:49:08 2017 -0400 +++ b/README.md Mon Jan 22 11:32:41 2018 -0500 @@ -7,11 +7,11 @@ ### Description -**Version:** 3.0.4 -**Date:** 2017-05-02 +**Version:** 3.1.0 +**Date:** 2018-01-08 **Author:** Alexis Delabriere and Etienne A. Thevenot (CEA, LIST, MetaboHUB, W4M Core Development Team) **Email:** [etienne.thevenot(at)cea.fr](mailto:etienne.thevenot@cea.fr) -**Citation:** 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*. +**Citation:** 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). **Licence:** CeCILL **Reference history:** **Funding:** Agence Nationale de la Recherche ([MetaboHUB](http://www.metabohub.fr/index.php?lang=en&Itemid=473) national infrastructure for metabolomics and fluxomics, ANR-11-INBS-0010 grant) @@ -28,15 +28,16 @@ ```r install.packages("batch", dep=TRUE) - install.packages("FNN", dep=TRUE) - install.packages("maxLik", dep=TRUE) install.packages("minpack.lm", dep=TRUE) + install.packages("missForest", dep=TRUE) install.packages("pracma", dep=TRUE) ``` + **profia** from Bioconductor ```r source("http://www.bioconductor.org/biocLite.R") + biocLite("Biobase") + biocLite("BiocParallel") biocLite("xcms") biocLite("plasFIA") biocLite("proFIA") @@ -55,6 +56,18 @@ ### News +###### CHANGES IN VERSION 3.1.0 + +NEW FEATURE + +randomForest method implemented for imputation of missing values + +###### CHANGES IN VERSION 3.0.6 + +NEW FEATURES + + * New (advanced) parameters available + ###### CHANGES IN VERSION 3.0.4 MINOR MODIFICATION
--- 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>
--- a/profia_wrapper.R Wed May 03 10:49:08 2017 -0400 +++ b/profia_wrapper.R Mon Jan 22 11:32:41 2018 -0500 @@ -95,9 +95,43 @@ cat("\n", stpI, ") Peak detection step ('proFIAset'):\n", sep = "") -fiaset <- proFIAset(directory, - ppm = as.numeric(argVc["ppmN"]), - parallel = TRUE) +if("sizeMinN" %in% names(argVc) && argVc["sizeMinN"] != "none") { + if("scanMaxI" %in% names(argVc) && argVc["scanMaxI"] != "none") { + fiaset <- proFIAset(directory, + ppm = as.numeric(argVc["ppmN"]), + dmz = as.numeric(argVc["dmzN"]), + bandCoverage = as.numeric(ifelse("bandCoverageN" %in% names(argVc), argVc["bandCoverageN"], "0.3")), + sizeMin = as.numeric(argVc["sizeMinN"]), + scanmin = as.numeric(ifelse("scanMinI" %in% names(argVc), argVc["scanMinI"], "1")), + scanmax = as.numeric(argVc["scanMaxI"]), + parallel = TRUE) + } else { + fiaset <- proFIAset(directory, + ppm = as.numeric(argVc["ppmN"]), + dmz = as.numeric(argVc["dmzN"]), + bandCoverage = as.numeric(ifelse("bandCoverageN" %in% names(argVc), argVc["bandCoverageN"], "0.3")), + sizeMin = as.numeric(argVc["sizeMinN"]), + scanmin = as.numeric(ifelse("scanMinI" %in% names(argVc), argVc["scanMinI"], "1")), + parallel = TRUE) + } +} else { + if("scanMaxI" %in% names(argVc) && argVc["scanMaxI"] != "none") { + fiaset <- proFIAset(directory, + ppm = as.numeric(argVc["ppmN"]), + dmz = as.numeric(argVc["dmzN"]), + bandCoverage = as.numeric(ifelse("bandCoverageN" %in% names(argVc), argVc["bandCoverageN"], "0.3")), + scanmin = as.numeric(ifelse("scanMinI" %in% names(argVc), argVc["scanMinI"], "1")), + scanmax = as.numeric(argVc["scanMaxI"]), + parallel = TRUE) + } else { + fiaset <- proFIAset(directory, + ppm = as.numeric(argVc["ppmN"]), + dmz = as.numeric(argVc["dmzN"]), + bandCoverage = as.numeric(ifelse("bandCoverageN" %in% names(argVc), argVc["bandCoverageN"], "0.3")), + scanmin = as.numeric(ifelse("scanMinI" %in% names(argVc), argVc["scanMinI"], "1")), + parallel = TRUE) + } +} stpI <- stpI + 1 @@ -105,6 +139,7 @@ fiaset <- group.FIA(fiaset, ppmGroup = as.numeric(argVc["ppmGroupN"]), + dmz = as.numeric(argVc["dmzGroupN"]), fracGroup = as.numeric(argVc["fracGroupN"])) stpI <- stpI + 1 @@ -116,17 +151,23 @@ stpI <- stpI + 1 -kI <- as.integer(argVc["kI"]) -if(kI > 0) { +if(argVc["imputeC"] == "randomForest") { + + cat("\n", stpI, ") Imputing missing values ('impute.randomForest'):\n", sep = "") - cat("\n", stpI, ") Imputing missing values ('imputeMissingValues.WKNN_TN'):\n", sep = "") + set.seed(123) - fiaset <- imputeMissingValues.WKNN_TN(fiaset, - k = kI) + fiaset <- impute.randomForest(fiaset, + parallel = FALSE) + + set.seed(NULL) stpI <- stpI + 1 } +###TODO Fix the error message to set 'parallel' to TRUE: You must register a 'foreach' parallel backend to run 'missForest' in parallel. Set 'parallelize' to 'no' to compute serially. +###TODO add the impute.KNN_TN method + options(warn = optWrnN) @@ -205,6 +246,21 @@ cat("\nEnd of '", modNamC, "' Galaxy module call: ", as.character(Sys.time()), "\n", sep = "") +cat("\n\n\n============================================================================") +cat("\nAdditional information about the call:\n") +cat("\n1) Parameters:\n") +print(cbind(value = argVc)) + +cat("\n2) Session Info:\n") +sessioninfo <- sessionInfo() +cat(sessioninfo$R.version$version.string,"\n") +cat("Main packages:\n") +for (pkg in names(sessioninfo$otherPkgs)) { cat(paste(pkg,packageVersion(pkg)),"\t") }; cat("\n") +cat("Other loaded packages:\n") +for (pkg in names(sessioninfo$loadedOnly)) { cat(paste(pkg,packageVersion(pkg)),"\t") }; cat("\n") + +cat("============================================================================\n") + sink() rm(list = ls())
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--- a/runit/output/information.txt Wed May 03 10:49:08 2017 -0400 +++ b/runit/output/information.txt Mon Jan 22 11:32:41 2018 -0500 @@ -1,22 +1,31 @@ -Start of the 'proFIA' Galaxy module call: Wed 03 May 2017 01:18:28 PM +Start of the 'proFIA' Galaxy module call: Mon 08 Jan 2018 07:23:11 PM files_root_directory plasFIA 1) Peak detection step ('proFIAset'): 2) Peak alignment ('group.FIA'): - 722 groups have been done . + 707 groups have been done . 3) Creating the peak table ('makeDataMatrix'): -4) Imputing missing values ('imputeMissingValues.WKNN_TN'): +4) Imputing missing values ('impute.randomForest'): + missForest iteration 1 in progress...done! + missForest iteration 2 in progress...done! + missForest iteration 3 in progress...done! + missForest iteration 4 in progress...done! + missForest iteration 5 in progress...done! + missForest iteration 6 in progress...done! + missForest iteration 7 in progress...done! + missForest iteration 8 in progress...done! + missForest iteration 9 in progress...done! + missForest iteration 10 in progress...done! 5) Plotting ('plot'): PCA -3 samples x 644 variables +3 samples x 707 variables standard scaling of predictors -78 excluded variables (near zero variance) R2X(cum) pre ort Total 1 2 0 null device @@ -24,12 +33,41 @@ 6) Printing ('show'): A "proFIAset" object containing 1 classes. -Missing values have been imputed. -2101 peaks detected. -722 features have been grouped. +Data matrix has been created. +2089 peaks detected. +707 features have been grouped. The data matrix is avalaible. -Memory usage: 0.419 MB +Memory usage: 0.409 MB 7) Exporting ('exportDataMatrix', 'exportSampleMetadata', 'exportVariableMetadata'): -End of 'proFIA' Galaxy module call: 2017-05-03 13:27:09 +End of 'proFIA' Galaxy module call: 2018-01-08 19:27:06 + + + +============================================================================ +Additional information about the call: + +1) Parameters: + value +dataMatrix_out "./output/dataMatrix.tsv" +sampleMetadata_out "./output/sampleMetadata.tsv" +variableMetadata_out "./output/variableMetadata.tsv" +figure "./output/figure.pdf" +information "./output/information.txt" +zipfile "./plasfia/plasFIA.zip" +library "NULL" +ppmN "2" +dmzN "5e-04" +ppmGroupN "1" +dmzGroupN "5e-04" +fracGroupN "0.1" +imputeC "randomForest" + +2) Session Info: +R version 3.3.1 (2016-06-21) +Main packages: +proFIA 1.4.0 xcms 1.50.1 Biobase 2.34.0 ProtGenerics 1.6.0 BiocGenerics 0.20.0 mzR 2.8.1 Rcpp 0.12.10 batch 1.1.4 +Other loaded packages: +splines 3.3.1 itertools 0.1.3 MASS 7.3.45 BiocParallel 1.8.1 missForest 1.4 lattice 0.20.34 quadprog 1.5.5 foreach 1.4.4 plyr 1.8.4 grid 3.3.1 iterators 1.0.9 multtest 2.30.0 survival 2.41.2 randomForest 4.6.12 Matrix 1.2.8 RColorBrewer 1.1.2 S4Vectors 0.12.2 codetools 0.2.15 MassSpecWavelet 1.40.0 pracma 1.9.9 ropls 1.6.2 stats4 3.3.1 minpack.lm 1.2.1 RANN 2.5 +============================================================================
--- a/runit/output/sampleMetadata.tsv Wed May 03 10:49:08 2017 -0400 +++ b/runit/output/sampleMetadata.tsv Mon Jan 22 11:32:41 2018 -0500 @@ -1,4 +1,4 @@ -sampleMetadata samDF[, colnames(samDF) != "sampleID"] +sampleMetadata class C100a plasFIA C100b plasFIA C100c plasFIA
--- a/runit/output/variableMetadata.tsv Wed May 03 10:49:08 2017 -0400 +++ b/runit/output/variableMetadata.tsv Mon Jan 22 11:32:41 2018 -0500 @@ -1,723 +1,708 @@ -variableMetadata mzMed mzMin mzMax sizeSamp scanMin scanMax nPeaks meanSolvent timeShifted signalOverSolventPvalueMean corSampPeakMean corSampPeakSd signalOverSolventRatioMean imputationOk variableID -M86.0965 86.0965189329336 86.0962219238281 86.0969924926758 54 18 88 3 0 0 0 0.383752731062773 0.150199386574608 Inf 1 M86.0965 -M89.5728 89.57275471071 89.5722503662109 89.5733108520508 70 18 88 1 2018.93876953125 0 0 -0.205860140821627 NA 7.37171039239295 0 M89.5728 -M89.5775 89.577503377741 89.5766754150391 89.5781402587891 57 18 75 1 0 0 0 0.170383838659374 NA Inf 0 M89.5775 -M89.5944 89.5943771938108 89.5933990478516 89.5950241088867 70 18 88 1 0 0 0 0.347467448010905 NA Inf 0 M89.5944 -M89.6051 89.6051298777262 89.6045913696289 89.6056442260742 58 18 76 1 0 0 0 0.172024494887118 NA Inf 0 M89.6051 -M90.9767 90.9767066127749 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--- a/runit/profia_runtests.R Wed May 03 10:49:08 2017 -0400 +++ b/runit/profia_runtests.R Mon Jan 22 11:32:41 2018 -0500 @@ -49,11 +49,9 @@ ## Call wrapperCallC <- paste(c(wrapperPathC, argLs), collapse = " ") - print(wrapperCallC) - if(.Platform$OS.type == "windows") wrapperCallC <- paste("Rscript", wrapperCallC) - + wrapperCodeN <- system(wrapperCallC) if (wrapperCodeN != 0)
--- a/runit/profia_tests.R Wed May 03 10:49:08 2017 -0400 +++ b/runit/profia_tests.R Mon Jan 22 11:32:41 2018 -0500 @@ -4,13 +4,15 @@ argLs <- list(zipfile = "./plasfia/plasFIA.zip", library = "NULL", ppmN = "2", + dmzN = "0.0005", ppmGroupN = "1", + dmzGroupN = "0.0005", fracGroupN = "0.1", - kI = "2") + imputeC = "randomForest") argLs <- c(defaultArgF(testDirC), argLs) outLs <- wrapperCallF(argLs) - checkEqualsNumeric(outLs[["datMN"]]["C100a", "M86.0965"], 1313797.6184402, tolerance = 1e-7) + checkEqualsNumeric(outLs[["datMN"]]["C100a", "M86.0965"], 1365657.4687182, tolerance = 1e-7) }
--- a/test-data/output-dataMatrix.tsv Wed May 03 10:49:08 2017 -0400 +++ b/test-data/output-dataMatrix.tsv Mon Jan 22 11:32:41 2018 -0500 @@ -1,720 +1,708 @@ dataMatrix C100a C100b C100c -M86.0965 1313797.61844021 2346283.61756864 2199831.8940868 -M89.5775 NA NA NA -M89.5944 NA NA NA -M89.6051 NA NA NA -M90.9767 5106007.35482488 6564507.32105305 6447733.55546476 -M93.9829 NA NA NA -M93.9873 NA NA NA -M93.9950 NA NA NA -M94.0000 NA NA NA -M94.0078 NA NA NA -M94.0124 NA NA NA -M94.0234 NA NA NA -M94.0262 NA NA NA -M94.0321 NA NA NA -M94.0362 NA NA NA -M94.0401 NA NA NA -M94.0425 NA NA NA -M94.0437 NA NA NA -M94.0552 NA NA NA -M94.0991 NA NA NA -M94.1052 NA NA NA -M94.1167 NA NA NA -M94.1242 NA NA NA -M94.1339 NA NA NA -M94.1356 NA NA NA -M94.1397 NA NA NA -M94.1445 NA NA NA -M94.1538 NA NA NA -M95.0604 685629.13713184 993375.02882652 875626.486232038 -M96.9218 11555241.0234047 11798830.9597099 13629842.0824619 -M97.0761 1114447.34533038 1167650.73440278 987109.64945019 -M98.9189 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