BMC Bioinformatics | |
Probabilistic principal component analysis for metabolomic data | |
Methodology Article | |
Lorraine Brennan1  Gift Nyamundanda2  Isobel Claire Gormley2  | |
[1] School of Agriculture, Food Science and Veterinary Medicine, Conway Institute, University College, Dublin, Ireland;School of Mathematical Sciences, University College, Dublin, Ireland; | |
关键词: Principal Component Analysis; Bayesian Information Criterion; Expectation Maximization Algorithm; Metabolomic Data; Loading Matrix; | |
DOI : 10.1186/1471-2105-11-571 | |
received in 2010-08-27, accepted in 2010-11-23, 发布年份 2010 | |
来源: Springer | |
【 摘 要 】
BackgroundData 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.ResultsHere, 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.ConclusionsThe 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.
【 授权许可】
Unknown
© Nyamundanda et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
【 预 览 】
Files | Size | Format | View |
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RO202311101571683ZK.pdf | 435KB | download |
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