2013/10/17 by Chuan Gao, Gao, Chuan, Christopher D Brown +5
Biochemistry, Genetics and Molecular Biology · Mathematics · #Applications (stat.AP) #Bioinformatics and Genomic Networks #FOS: Biological sciences #FOS: Computer and information sciences #Gene expression and cancer classification #Genomics (q-bio.GN) #Statistical Methods and Inference #q-bio.GN #stat.AP
paper · pdf · doi:10.48550/arxiv.1310.4792
arxiv created 2013/10/17 · openalex publication_date 2013/10/17 · arxiv updated 2013/10/18 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
One important problem in genome science is to determine sets of co-regulated genes based on measurements of gene expression levels across samples, where the quantification of expression levels includes substantial technical and biological noise. 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. By applying three layers of shrinkage to the loading matrix (global, factor-specific, and element-wise), this model has non-parametric properties in that it estimates the appropriate number of factors from the data. We added a two-component mixture to model each factor loading as being generated from either a sparse or a dense mixture component; this allows dense factors that capture confounding noise, and sparse factors that capture local gene interactions. We developed two statistics to quantify the stability of the recovered matrices for both sparse and dense matrices. We tested our model on simulated data and found that we successfully recovered the true latent structure as compared to related models. We applied our model to a large gene expression study and found that we recovered known covariates and small groups of co-regulated genes. We validated these gene subsets by testing for associations between genotype data and these latent factors, and we found a substantial number of biologically important genetic regulators for the recovered gene subsets.