Dataset Information
Sparse non-negative generalized PCA with applications to metabolomics.
Ontology highlight
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 both the non-negativity of the underlying spectra and dependencies at adjacent chemical shifts. Through the reanalysis of experimental NMR data on five purified neural cell types, we demonstrate the utility of our methods for dimension reduction, pattern recognition, sample exploration and feature selection. Our methods lead to the identification of novel metabolites that reflect the differences between these cell types.Availability
www.stat.rice.edu/~gallen/software.html.Contact
gallen@rice.edu.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Allen GI
PROVIDER: S-EPMC3198582 | biostudies-literature | 2011 Nov
REPOSITORIES: biostudies-literature
Publications
Sparse non-negative generalized PCA with applications to metabolomics.
Bioinformatics (Oxford, England) 20110919 21
<h4>Motivation</h4>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 adj ...[more]