NExUS: Bayesian simultaneous network estimation across unequal sample sizes

Abstract

Network-based analyses of high-throughput genomics data provide a holistic, systems-level understanding of various biological mechanisms for a common population. However, when estimating multiple networks across heterogeneous sub-populations, varying sample sizes pose a challenge in the estimation and inference, as network differences may be driven by differences in power. We are particularly interested in addressing this challenge in the context of proteomic networks for related cancers, as the number of subjects available for rare cancer (sub-)types is often limited. We develop NExUS (Network Estimation across Unequal Sample sizes), a Bayesian method that enables joint learning of multiple networks while avoiding artefactual relationship between sample size and network sparsity. We demonstrate through simulations that NExUS outperforms existing network estimation methods in this context, and apply it to learn network similarity and shared pathway activity for groups of cancers with related origins represented in The Cancer Genome Atlas (TCGA) proteomic data.

8 pages, 8 figues

References in corpus (5)

• Network exploration via the adaptive LASSO and SCAD penalties
• Predictive genomics: A cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data
• Scaling It Up: Stochastic Search Structure Learning in Graphical Models
• Bayesian inference for multiple Gaussian graphical models with application to metabolic association networks
• Bayesian sparse graphical models for classification with application to protein expression data