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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 28, 2019
- Source ID
- 10.1093/bioinformatics/btz636
Entities
People
- Christine B Peterson
- Kim-anh Do
- Priyam Das
- Rehan Akbani
- Veerabhadran Baladandayuthapani
Organizations
- Cancer Prevention and Research Institute of Texas
- Congressionally Directed Medical Research Programs
- National Institutes of Health
- National Science Foundation
- University of Michigan
- University of Texas at Austin