NExUS: Bayesian simultaneous network estimation across unequal sample sizes.

ABSTRACT:

Motivation

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.

Results

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 s

SUBMITTER: Das P 

PROVIDER: S-EPMC8215919 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature