Probabilistic Principal Component Analysis for Metabolomic Data

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...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Gift Nyamundanda, Lorraine Brennan, Isobel Claire Gormley
• Keywords: BMCBI 2007, BMCBI 2010, Metabolomic Data, Principal Component Analysis, Principal Components