Type

Journal Article

Authors

Padraig Cunningham
Lorraine Brennan
Kenneth Bryan

Subjects

Biochemistry

Topics
metabolomics least squares analysis mass spectrometry statistics nonparametric computational biology databases protein user computer interface reproducibility of results internet nuclear magnetic resonance biomolecular software discriminant analysis

MetaFIND: a feature analysis tool for metabolomics data. (2008)

Abstract 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. 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. 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.
Collections Ireland -> University College Dublin -> PubMed

Full list of authors on original publication

Padraig Cunningham, Lorraine Brennan, Kenneth Bryan

Experts in our system

1
Padraig Cunningham
University College Dublin
Total Publications: 165
 
2
Lorraine Brennan
University College Dublin
Total Publications: 166
 
3
Kenneth Bryan
Royal College of Surgeons in Ireland
Total Publications: 36