Bayesian group latent factor analysis with structured sparsity

1 code implementation • 11 Nov 2014 • Shiwen Zhao, Chuan Gao, Sayan Mukherjee, Barbara E. Engelhardt

Latent factor models are the canonical statistical tool for exploratory analyses of low-dimensional linear structure for an observation matrix with p features across n samples.

Differential gene co-expression networks via Bayesian biclustering models

no code implementations • 7 Nov 2014 • Chuan Gao, Shiwen Zhao, Ian C. McDowell, Christopher D. Brown, Barbara E. Engelhardt

Further, we develop a method to recover gene co-expression networks from the estimated sparse biclustering matrices.

A latent factor model with a mixture of sparse and dense factors to model gene expression data with confounding effects

1 code implementation • 17 Oct 2013 • Chuan Gao, Christopher D. Brown, Barbara E. Engelhardt

To address this problem, we developed a Bayesian sparse latent factor model that uses a three parameter beta prior to flexibly model shrinkage in the loading matrix.

Applications Genomics