Repository 'w4mclassfilter' in Galaxy Toolshed
W4M Data Subset is Galaxy tool-wrapper to wrap the w4mclassfilter R package for use with the Workflow4Metabolomics flavor of Galaxy. This tool was developed and tested with planemo.
Arthur Eschenlauer (University of Minnesota, esch0041@umn.edu)
The w4mclassfilter package is available from the Hegeman lab github repository https://github.com/HegemanLab/w4mclassfilter/releases.
The "w4mclassfilter" Galaxy tool, built from this repository, is in the main Galaxy Toolshed at https://toolshed.g2.bx.psu.edu/repository?repository_id=5f24951d82ab40fa
See the NEWS section at the bottom of this page
LC-MS metabolomics experiments seek to resolve "features", i.e., species that have distinct chromatographic retention time ("rt") and (after ionization) mass-to-charge ratio ("m/z" or "mz"). (If a chemical is fragmented or may have a variety of adducts, several features will result.) Data for a sample are collected as mass-spectral intensities, each of which is associated with a position on a 2D plane with dimensions of rt and m/z. Ideally, features would be sufficiently reproducible among sample-runs to distinguish features that are similar among samples from those that differ.
For liquid chromatography, the retention time for a species can vary considerably from one chromatography run to the next. The Workflow4Metabolomics suite of Galaxy tools (W4M, [Giacomoni et al., 2014, Guitton et al. 2017]) uses the XCMS preprocessing tools [Smith et al., 2006] for "retention-time correction" to align features among samples. Features may be better aligned if pooled samples and blanks are included.
Multivariate statistical tools may be used to discover clusters of similar samples [Thévenot et al., 2015]. However, once retention-time alignment of features has been achieved among samples in LC-MS datasets:
- The presence of pools and blanks may confound identification and separation of sample clusters.
- Multivariate statistical algorithms may be impacted by missing values or dimensions that have zero variance.
The W4M Data Subset tool selects subsets of samples, features, or data values and conditions the data for further analysis.
- The tool takes as input the dataMatrix, sampleMetadata, and variableMetadata datasets produced by W4M's XCMS and CAMERA [Kuhl et al., 2012] tools.
- The tool produces the same trio of output datasets, modified as described below.
This tool can perform several operations to reduce the number samples or features to be analyzed (although this should be done only in a statistically sound manner consistent with the nature of the experiment):
- Sample filtering: Samples may be selected by designating a "sample class" column in sampleMetadata and specifying criteria to include or exclude samples based on the contents of this column.
- Feature filtering: Features may be selected by specifying minimum or maximum value (or both) allowable in columns of variableMetadata.
- Intensity filtering: To exclude minimal features from consideration, a lower bound may be specified for the maximum intensity for a feature across all samples (i.e., for a row in dataMatrix).
This tool also conditions data for statistical analysis:
- Samples that are missing from either sampleMetadata or dataMatrix are eliminated.
- Features that are missing from either variableMetadata or dataMatrix are eliminated.
- Features and samples that have zero variance are eliminated.
- Samples and features are ordered consistently in variableMetadata, sampleMetadata, and dataMatrix. (The columns for sorting variableMetadata or sampleMetadata may be specified.)
- The names of the first columns of variableMetadata and sampleMetadata are set respectively to "variableMetadata" and "sampleMetadata".
- If desired, the values in the dataMatrix may be log-transformed.
- Negative intensities become missing values (before missing-value replacement is performed).
- If desired, each missing value in dataMatrix may be replaced with zero or the median value observed for the corresponding feature.
- If desired, a "center" for each treatment can be computed in lieu of the samples for that treatment.
This tool may be applied several times sequentially, which may be useful for:
- analyzing subsets of samples for progressively smaller sets of treatment levels, or
- choosing subsets of samples or features, respectively based on criteria in columns of sampleMetadata or variableMetadata.
- Bug fix: "medoid computation aborts when trt is numeric or some trts have one replicate" HegemanLab/w4mclassfilter#8.
- Use v0.98.19 of the w4mclassfilter bioconda package which was built with R 4.0.3.
- Use v4.0.3 of the r-base conda-forge package.
- Enhancement: Added option "compute center for each treatment" HegemanLab/w4mclassfilter#6.
- Enhancement: Added option "enable sorting on multiple columns of metadata" HegemanLab/w4mclassfilter#7.
- Enhancement: Added option "always treat negative intensities as missing values" #7.
- Use v0.98.18 of the w4mclassfilter bioconda package.
- Enhancement #6 - "Provide sort options for features and samples".
- Use v0.98.14 of the w4mclassfilter bioconda package.
- Support enhancement HegemanLab/w4mclassfilter#4 - "add and test no-imputation and centering-imputation functions":
- Support no imputation.
- Support imputing missing feature-intensities as median intensity for the corresponding feature.
- Use v0.98.13 of the w4mclassfilter bioconda package.
- none
- Use v0.98.8 of the w4mclassfilter bioconda package.
- none
- Quality-assurance improvements - Changes for IUC conformance and automated Planemo testing on Travis CI.
- Forbid hyphens in sample and variable names because R does not permit them.
- none
- Added missing support for hyphen character in regular expressions
- The tool now appears in Galaxy with a new, more representative name: "W4M Data Subset". (Earlier versions of this tool appeared in Galaxy with the name "Sample Subset".)
- Option was added to log-transform data matrix values.
- Output datasets are named in conformance with the W4M convention of appending the name of each preprocessing tool to the input dataset name.
- Superfluous "Column that names the sample" input parameter was eliminated.
- Some documentation was updated or clarified.
- None
- First column of output variableMetadata (that has feature names) now is always named "variableMetadata"
- First column of output sampleMetadata now (that has sample names) is always named "sampleMetadata"
- Now uses w4mclassfilter R package v0.98.7.
- Added support for filtering out features whose attributes fall outside specified ranges. For more detail, see "Variable-range filters" above.
- Now uses w4mclassfilter R package v0.98.6.
- Now sorts sample names and feature names in output files because some statistical tools expect the same order in dataMatrix row and column names as in the corresponding metadata files.
- Improved reference-list.
- Improved input handling.
- Now uses w4mclassfilter R package v0.98.3, although that version has no functional implications for this tool.
- First release - Wrap the w4mclassfilter R package that implements filtering of W4M data matrix, variable metadata, and sample metadata by class of sample.
- dataMatrix is modified by the tool, so it does appear as an output file
- sampleMetadata is modified by the tool, so it does appear as an output file
- variableMetadata is modified by the tool, so it does appear as an output file
- none
Benjamini, Yoav and Hochberg, Yosef (1995) Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. In Journal of the royal statistical society. Series B (Methodological), 1 (57), pp. pp. 289-300. http://www.jstor.org/stable/2346101
Kuhl, Carsten and Tautenhahn, Ralf and Böttcher, Christoph and Larson, Tony R. and Neumann, Steffen (2011). CAMERA: An Integrated Strategy for Compound Spectra Extraction and Annotation of Liquid Chromatography/Mass Spectrometry Data Sets. In Analytical Chemistry, 84 (1), pp. 283-289. doi:10.1021/ac202450g
Giacomoni, F. and Le Corguille, G. and Monsoor, M. and Landi, M. and Pericard, P. and Petera, M. and Duperier, C. and Tremblay-Franco, M. and Martin, J.-F. and Jacob, D. and et al. (2014). Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics. In Bioinformatics, 31 (9), pp. 1493–1495. doi:10.1093/bioinformatics/btu813
Guitton, Yann and Tremblay-Franco, Marie and Le Corguillé, Gildas and Martin, Jean-François and Pétéra, Mélanie and Roger-Mele, Pierrick and Delabrière, Alexis and Goulitquer, Sophie and Monsoor, Misharl and Duperier, Christophe and et al. (2017). Create, run, share, publish, and reference your LC–MS, FIA–MS, GC–MS, and NMR data analysis workflows with the Workflow4Metabolomics 3.0 Galaxy online infrastructure for metabolomics. In The International Journal of Biochemistry & Cell Biology, pp. 89-101. doi:10.1016/j.biocel.2017.07.002
Smith, Colin A. and Want, Elizabeth J. and O'Maille, Grace and Abagyan, Ruben and Siuzdak, Gary (2006). XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification. In Analytical Chemistry, 78 (3), pp. 779–787. doi:10.1021/ac051437y
Thévenot, Etienne A. and Roux, Aurélie and Xu, Ying and Ezan, Eric and Junot, Christophe (2015). Analysis of the Human Adult Urinary Metabolome Variations with Age, Body Mass Index, and Gender by Implementing a Comprehensive Workflow for Univariate and OPLS Statistical Analyses. In Journal of Proteome Research, 14 (8), pp. 3322–3335. doi:10.1021/acs.jproteome.5b00354
Yekutieli, Daniel and Benjamini, Yoav (2001) The control of the false discovery rate in multiple testing under dependency. In The Annals of Statistics, 29 (4), pp. 1165-1188. doi:10.1214/aos/1013699998