Sparse latent factor models with interactions: analysis of gene expression data

DOI10.1214/12-AOAS607zbMath1288.62164arXiv1312.1818WikidataQ105584260 ScholiaQ105584260MaRDI QIDQ2443144

Joseph E. Lucas, Vinícius Diniz Mayrink

Publication date: 4 April 2014

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1312.1818

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50) Biochemistry, molecular biology (92C40)

Related Items (5)

TPRM: tensor partition regression models with applications in imaging biomarker detection ⋮ Unnamed Item ⋮ Compressed spectral screening for large-scale differential correlation analysis with application in selecting glioblastoma gene modules ⋮ Sparse latent factor models with interactions: analysis of gene expression data ⋮ Clustering non-linear interactions in factor analysis

Uses Software

• BioHMM

Cites Work

• Ferguson distributions via Polya urn schemes
• Prior distributions on spaces of probability measures
• Hidden Markov models approach to the analysis of array CGH data
• A Bayesian approach to nonlinear latent variable models using the Gibbs sampler and the Metropolis-Hastings algorithm
• Sparse latent factor models with interactions: analysis of gene expression data
• A Bayesian analysis of some nonparametric problems
• High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics
• A Latent Class Model with Hidden Markov Dependence for Array CGH Data
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