Conference Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 70 - Novel Approaches for Omics and Multi-Omics Analysis
Type: Contributed
Date/Time: Sunday, August 7, 2022 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #322868
Title: FiBAG: Functional Integrative Bayesian Analysis of High-Dimensional Multiplatform Genomic Data
Author(s): Rupam Bhattacharyya* and Nicholas Henderson and Veera Baladandayuthapani
Companies: University of Michigan and University of Michigan and University of Michigan
Keywords: driver genes; driver proteins; cascading proteins; pan-platform integration; spike-and-slab prior; hierarchical models
Abstract:

Large-scale multi-omics datasets offer complementary, partly independent, high-resolution views of the human genome. Inference using such data poses challenges like high-dimensionality and structured dependencies but offers potential for understanding the complex biological processes characterizing a disease. We propose fiBAG, an integrative hierarchical Bayesian framework for modeling the fundamental biological relationships underlying such cross-platform molecular features. Using Gaussian processes, fiBAG identifies mechanistic evidence for covariates from upstream information. Such evidence, mapped to prior inclusion probabilities, informs a calibrated Bayesian variable selection (cBVS) model identifying genes/proteins associated with the outcome. Simulation studies illustrate that cBVS has higher power to detect disease-related markers than non-integrative approaches. A pan-cancer analysis of 14 TCGA cancer datasets is performed to identify markers associated with cancer stemness and patient survival. Our findings include both known associations like the role of RPS6KA1/p90RSK in gynecological cancers and interesting novelties like EGFR in gastrointestinal cancers.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2022 program