Bernoulli-Gaussian model for gene expression analysis - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2011

Bernoulli-Gaussian model for gene expression analysis

Résumé

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.
Fichier principal
Vignette du fichier
Bazot_IEEE_ICASSP_v9.pdf (310.43 Ko) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-04241309 , version 1 (13-10-2023)

Identifiants

Citer

Cécile Bazot, Nicolas Dobigeon, Jean-Yves Tourneret, Alfred O. Hero. Bernoulli-Gaussian model for gene expression analysis. International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011), IEEE, May 2011, Prague, Czech Republic. pp.5996--5999, ⟨10.1109/ICASSP.2011.5947728⟩. ⟨hal-04241309⟩
33 Consultations
8 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More