Nonparametric Bayesian sparse factor models with application to gene expression modeling

Details Nonparametric Bayesian sparse factor models with application to gene expression modeling Nonparametric Bayesian sparse factor models with application to gene expression modeling

2010-11-29

arxiv.org/abs/1011.6293v2

Annals of Applied Statistics 2011, Vol. 5, No. 2B, 1534-1552

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Published in at http://dx.doi.org/10.1214/10-AOAS435 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Ins

Scientific paper

10.1214/10-AOAS435

A nonparametric Bayesian extension of Factor Analysis (FA) is proposed where observed data $\mathbf{Y}$ is modeled as a linear superposition, $\mathbf{G}$, of a potentially infinite number of hidden factors, $\mathbf{X}$. The Indian Buffet Process (IBP) is used as a prior on $\mathbf{G}$ to incorporate sparsity and to allow the number of latent features to be inferred. The model's utility for modeling gene expression data is investigated using randomly generated data sets based on a known sparse connectivity matrix for E. Coli, and on three biological data sets of increasing complexity.

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