Probabilistic Principal Component Analysis for Metabolomic Data.

Hdl Handle:
http://hdl.handle.net/10147/120516
Title:
Probabilistic Principal Component Analysis for Metabolomic Data.
Authors:
Nyamundanda, Gift; Brennan, Lorraine; Gormley, Isobel Claire
Citation:
BMC Bioinformatics. 2010 Nov 23;11(1):571
Issue Date:
23-Nov-2010
URI:
http://hdl.handle.net/10147/120516
Abstract:
Abstract Background Data from metabolomic studies are typically complex and high-dimensional. Principal component analysis (PCA) is currently the most widely used statistical technique for analyzing metabolomic data. However, PCA is limited by the fact that it is not based on a statistical model. Results Here, probabilistic principal component analysis (PPCA) which addresses some of the limitations of PCA, is reviewed and extended. A novel extension of PPCA, called probabilistic principal component and covariates analysis (PPCCA), is introduced which provides a flexible approach to jointly model metabolomic data and additional covariate information. The use of a mixture of PPCA models for discovering the number of inherent groups in metabolomic data is demonstrated. The jackknife technique is employed to construct confidence intervals for estimated model parameters throughout. The optimal number of principal components is determined through the use of the Bayesian Information Criterion model selection tool, which is modified to address the high dimensionality of the data. Conclusions The methods presented are illustrated through an application to metabolomic data sets. Jointly modeling metabolomic data and covariates was successfully achieved and has the potential to provide deeper insight to the underlying data structure. Examination of confidence intervals for the model parameters, such as loadings, allows for principled and clear interpretation of the underlying data structure. A software package called MetabolAnalyze, freely available through the R statistical software, has been developed to facilitate implementation of the presented methods in the metabolomics field.
Item Type:
Journal Article

Full metadata record

DC FieldValue Language
dc.contributor.authorNyamundanda, Gift-
dc.contributor.authorBrennan, Lorraine-
dc.contributor.authorGormley, Isobel Claire-
dc.date.accessioned2011-01-27T13:59:26Z-
dc.date.available2011-01-27T13:59:26Z-
dc.date.issued2010-11-23-
dc.identifierhttp://dx.doi.org/10.1186/1471-2105-11-571-
dc.identifier.citationBMC Bioinformatics. 2010 Nov 23;11(1):571-
dc.identifier.urihttp://hdl.handle.net/10147/120516-
dc.description.abstractAbstract Background Data from metabolomic studies are typically complex and high-dimensional. Principal component analysis (PCA) is currently the most widely used statistical technique for analyzing metabolomic data. However, PCA is limited by the fact that it is not based on a statistical model. Results Here, probabilistic principal component analysis (PPCA) which addresses some of the limitations of PCA, is reviewed and extended. A novel extension of PPCA, called probabilistic principal component and covariates analysis (PPCCA), is introduced which provides a flexible approach to jointly model metabolomic data and additional covariate information. The use of a mixture of PPCA models for discovering the number of inherent groups in metabolomic data is demonstrated. The jackknife technique is employed to construct confidence intervals for estimated model parameters throughout. The optimal number of principal components is determined through the use of the Bayesian Information Criterion model selection tool, which is modified to address the high dimensionality of the data. Conclusions The methods presented are illustrated through an application to metabolomic data sets. Jointly modeling metabolomic data and covariates was successfully achieved and has the potential to provide deeper insight to the underlying data structure. Examination of confidence intervals for the model parameters, such as loadings, allows for principled and clear interpretation of the underlying data structure. A software package called MetabolAnalyze, freely available through the R statistical software, has been developed to facilitate implementation of the presented methods in the metabolomics field.-
dc.titleProbabilistic Principal Component Analysis for Metabolomic Data.-
dc.typeJournal Article-
dc.language.rfc3066en-
dc.rights.holderNyamundanda et al.; licensee BioMed Central Ltd.-
dc.description.statusPeer Reviewed-
dc.date.updated2010-12-21T18:00:59Z-
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