2016/11/08 by Nilabja Guha, Veera Baladandayuthapani, Guha, Nilabja +3 · 2 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · #FOS: Computer and information sciences #Gene expression and cancer classification #Methodology (stat.ME) #Statistical Methods and Inference #Statistical Methods in Clinical Trials
paper · doi:10.48550/arxiv.1611.02480
openalex publication_date 2016/11/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Graphical models are ubiquitous tools to describe the interdependence between variables measured simultaneously such as large-scale gene or protein expression data. Gaussian graphical models (GGMs) are well-established tools for probabilistic exploration of dependence structures using precision matrices and they are generated under a multivariate normal joint distribution. However, they suffer from several shortcomings since they are based on Gaussian distribution assumptions. In this article, we propose a Bayesian quantile based approach for sparse estimation of graphs. We demonstrate that the resulting graph estimation is robust to outliers and applicable under general distributional assumptions. Furthermore, we develop efficient variational Bayes approximations to scale the methods for large data sets. Our methods are applied to a novel cancer proteomics data dataset where-in multiple proteomic antibodies are simultaneously assessed on tumor samples using reverse-phase protein arrays (RPPA) technology.