Probabilistic principal component analysis for metabolomic data.

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 tec

SUBMITTER: Nyamundanda G 

PROVIDER: S-EPMC3006395 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature