Sparse non-negative generalized PCA with applications to metabolomics.

ABSTRACT:

Motivation

Nuclear magnetic resonance (NMR) spectroscopy has been used to study mixtures of metabolites in biological samples. This technology produces a spectrum for each sample depicting the chemical shifts at which an unknown number of latent metabolites resonate. The interpretation of this data with common multivariate exploratory methods such as principal components analysis (PCA) is limited due to high-dimensionality, non-negativity of the underlying spectra and dependencies at adjacent chemical shifts.

Results

We develop a novel modification of PCA that is appropriate for analysis of NMR data, entitled Sparse Non-Negative Generalized PCA. This method yields interpretable principal components and loading vectors that select important features and directly account for b

SUBMITTER: Allen GI 

PROVIDER: S-EPMC3198582 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature