MetaFIND: A feature analysis tool for metabolomics data
<p>Abstract</p> <p>Background</p> <p>Metabolomics, or metabonomics, refers to the quantitative analysis of all metabolites present within a biological sample and is generally carried out using NMR spectroscopy or Mass Spectrometry. Such analysis produces a set of peaks,...
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| Format: | Article |
| Language: | English |
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BMC
2008-11-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/9/470 |
| _version_ | 1818526503121453056 |
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| author | Cunningham Pádraig Brennan Lorraine Bryan Kenneth |
| author_facet | Cunningham Pádraig Brennan Lorraine Bryan Kenneth |
| author_sort | Cunningham Pádraig |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Metabolomics, or metabonomics, refers to the quantitative analysis of all metabolites present within a biological sample and is generally carried out using NMR spectroscopy or Mass Spectrometry. Such analysis produces a set of peaks, or <it>features</it>, indicative of the metabolic composition of the sample and may be used as a basis for sample classification. Feature selection may be employed to improve classification accuracy or aid model explanation by establishing a subset of class discriminating features. Factors such as experimental noise, choice of technique and threshold selection may adversely affect the set of selected features retrieved. Furthermore, the high dimensionality and multi-collinearity inherent within metabolomics data may exacerbate discrepancies between the set of features retrieved and those required to provide a complete explanation of metabolite signatures. Given these issues, the latter in particular, we present the MetaFIND application for 'post-feature selection' correlation analysis of metabolomics data.</p> <p>Results</p> <p>In our evaluation we show how MetaFIND may be used to elucidate metabolite signatures from the set of features selected by diverse techniques over two metabolomics datasets. Importantly, we also show how MetaFIND may augment standard feature selection and aid the discovery of additional significant features, including those which represent novel class discriminating metabolites. MetaFIND also supports the discovery of higher level metabolite correlations.</p> <p>Conclusion</p> <p>Standard feature selection techniques may fail to capture the full set of relevant features in the case of high dimensional, multi-collinear metabolomics data. We show that the MetaFIND 'post-feature selection' analysis tool may aid metabolite signature elucidation, feature discovery and inference of metabolic correlations.</p> |
| first_indexed | 2024-12-11T06:23:55Z |
| format | Article |
| id | doaj.art-4f6701d1835645a989922d67b724deff |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-11T06:23:55Z |
| publishDate | 2008-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-4f6701d1835645a989922d67b724deff2022-12-22T01:17:43ZengBMCBMC Bioinformatics1471-21052008-11-019147010.1186/1471-2105-9-470MetaFIND: A feature analysis tool for metabolomics dataCunningham PádraigBrennan LorraineBryan Kenneth<p>Abstract</p> <p>Background</p> <p>Metabolomics, or metabonomics, refers to the quantitative analysis of all metabolites present within a biological sample and is generally carried out using NMR spectroscopy or Mass Spectrometry. Such analysis produces a set of peaks, or <it>features</it>, indicative of the metabolic composition of the sample and may be used as a basis for sample classification. Feature selection may be employed to improve classification accuracy or aid model explanation by establishing a subset of class discriminating features. Factors such as experimental noise, choice of technique and threshold selection may adversely affect the set of selected features retrieved. Furthermore, the high dimensionality and multi-collinearity inherent within metabolomics data may exacerbate discrepancies between the set of features retrieved and those required to provide a complete explanation of metabolite signatures. Given these issues, the latter in particular, we present the MetaFIND application for 'post-feature selection' correlation analysis of metabolomics data.</p> <p>Results</p> <p>In our evaluation we show how MetaFIND may be used to elucidate metabolite signatures from the set of features selected by diverse techniques over two metabolomics datasets. Importantly, we also show how MetaFIND may augment standard feature selection and aid the discovery of additional significant features, including those which represent novel class discriminating metabolites. MetaFIND also supports the discovery of higher level metabolite correlations.</p> <p>Conclusion</p> <p>Standard feature selection techniques may fail to capture the full set of relevant features in the case of high dimensional, multi-collinear metabolomics data. We show that the MetaFIND 'post-feature selection' analysis tool may aid metabolite signature elucidation, feature discovery and inference of metabolic correlations.</p>http://www.biomedcentral.com/1471-2105/9/470 |
| spellingShingle | Cunningham Pádraig Brennan Lorraine Bryan Kenneth MetaFIND: A feature analysis tool for metabolomics data BMC Bioinformatics |
| title | MetaFIND: A feature analysis tool for metabolomics data |
| title_full | MetaFIND: A feature analysis tool for metabolomics data |
| title_fullStr | MetaFIND: A feature analysis tool for metabolomics data |
| title_full_unstemmed | MetaFIND: A feature analysis tool for metabolomics data |
| title_short | MetaFIND: A feature analysis tool for metabolomics data |
| title_sort | metafind a feature analysis tool for metabolomics data |
| url | http://www.biomedcentral.com/1471-2105/9/470 |
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