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June 2013 Sparse latent factor models with interactions: Analysis of gene expression data
Vinicius Diniz Mayrink, Joseph Edward Lucas
Ann. Appl. Stat. 7(2): 799-822 (June 2013). DOI: 10.1214/12-AOAS607

Abstract

Sparse latent multi-factor models have been used in many exploratory and predictive problems with high-dimensional multivariate observations. Because of concerns with identifiability, the latent factors are almost always assumed to be linearly related to measured feature variables. Here we explore the analysis of multi-factor models with different structures of interactions between latent factors, including multiplicative effects as well as a more general framework for nonlinear interactions introduced via the Gaussian Process. We utilize sparsity priors to test whether the factors and interaction terms have significant effect. The performance of the models is evaluated through simulated and real data applications in genomics. Variation in the number of copies of regions of the genome is a well-known and important feature of most cancers. We examine interactions between factors directly associated with different chromosomal regions detected with copy number alteration in breast cancer data. In this context, significant interaction effects for specific genes suggest synergies between duplications and deletions in different regions of the chromosome.

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Vinicius Diniz Mayrink. Joseph Edward Lucas. "Sparse latent factor models with interactions: Analysis of gene expression data." Ann. Appl. Stat. 7 (2) 799 - 822, June 2013. https://doi.org/10.1214/12-AOAS607

Information

Published: June 2013
First available in Project Euclid: 27 June 2013

zbMATH: 1288.62164
MathSciNet: MR3112918
Digital Object Identifier: 10.1214/12-AOAS607

Rights: Copyright © 2013 Institute of Mathematical Statistics

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Vol.7 • No. 2 • June 2013
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