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MetaFIND: a feature analysis tool for metabolomics data.

Bryan K, Brennan L, Cunningham P - BMC Bioinformatics (2008)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Complex & Adaptive Systems Laboratory (CASL), University College Dublin, Ireland. kenneth.bryan@ucd.ie

ABSTRACT

Background: 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 features, 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.

Results: 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.

Conclusion: 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.

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The feature analysis pipeline. The Metabolomics feature analysis pipeline incorporating MetaFIND as an additional post-feature selection step.
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Figure 2: The feature analysis pipeline. The Metabolomics feature analysis pipeline incorporating MetaFIND as an additional post-feature selection step.

Mentions: To further aid the retrieval of all peaks and metabolites relevant to both class discrimination and subsequent explanation, a novel metabolomics feature analysis tool called MetaFIND (Metabolomics Feature IN terrogation and Discovery) has been developed. The MetaFIND application addresses the multi-collinear aspect of metabolomics data by providing an adjunct to standard feature selection techniques. This takes the form of a 'post-feature selection' correlation analysis step, as illustrated in Figure 2.


MetaFIND: a feature analysis tool for metabolomics data.

Bryan K, Brennan L, Cunningham P - BMC Bioinformatics (2008)

The feature analysis pipeline. The Metabolomics feature analysis pipeline incorporating MetaFIND as an additional post-feature selection step.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2655093&req=5

Figure 2: The feature analysis pipeline. The Metabolomics feature analysis pipeline incorporating MetaFIND as an additional post-feature selection step.
Mentions: To further aid the retrieval of all peaks and metabolites relevant to both class discrimination and subsequent explanation, a novel metabolomics feature analysis tool called MetaFIND (Metabolomics Feature IN terrogation and Discovery) has been developed. The MetaFIND application addresses the multi-collinear aspect of metabolomics data by providing an adjunct to standard feature selection techniques. This takes the form of a 'post-feature selection' correlation analysis step, as illustrated in Figure 2.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Complex & Adaptive Systems Laboratory (CASL), University College Dublin, Ireland. kenneth.bryan@ucd.ie

ABSTRACT

Background: 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 features, 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.

Results: 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.

Conclusion: 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.

Show MeSH