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A Bernoulli-Gaussian model for gene factor analysis

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4 Author(s)
Bazor, C. ; IRIT/INP-ENSEEIHT, Univ. of Toulouse, Toulouse, France ; Dobigeon, N. ; Tourneret, J.-Y. ; Hero, A.O.

This paper investigates a Bayesian model and a Markov chain Monte Carlo (MCMC) algorithm for gene factor analysis. Each sample in the dataset is decomposed as a linear combination of characteristic gene signatures (also referred to as factors) following a linear mixing model. To enforce the sparsity of the relative contribution (called factor score) of each gene signature to a specific sample, constrained Bernoulli-Gaussian distributions are elected as prior distributions for these factor scores. This distribution allows one to ensure non-negativity and full-additivity constraints for the scores that are interpreted as concentrations. The complexity of the resulting Bayesian estimators is alleviated by using a Gibbs sampler which generates samples distributed according to the posterior distribution of interest. These samples are then used to approximate the standard maximum a posteriori (MAP) or minimum mean square error (MMSE) estimators. The accuracy of the proposed Bayesian method is illustrated by simulations conducted on synthetic and real data.

Published in:

Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on

Date of Conference:

22-27 May 2011