Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Genomic Data

TL;DR

This paper introduces fiBAG, a Bayesian framework that integrates multi-platform genomic data with functional evidence to enhance detection of disease markers and understand cellular mechanisms in cancer.

Contribution

The paper presents a novel Bayesian method that combines Gaussian process models and functional calibration to improve multi-omic data integration and biomarker discovery.

Findings

Higher power in detecting disease markers compared to non-integrative methods

Effective identification of proteogenomic markers linked to cancer stemness

Application to pan-cancer data reveals insights into cellular mechanisms

Abstract

Rapid advancements in collection and dissemination of multi-platform molecular and genomics data has resulted in enormous opportunities to aggregate such data in order to understand, prevent, and treat human diseases. While significant improvements have been made in multi-omic data integration methods to discover biological markers and mechanisms underlying both prognosis and treatment, the precise cellular functions governing these complex mechanisms still need detailed and data-driven de-novo evaluations. We propose a framework called Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Genomic Data (fiBAG), that allows simultaneous identification of upstream functional evidence of proteogenomic biomarkers and the incorporation of such knowledge in Bayesian variable selection models to improve signal detection. fiBAG employs a conflation of Gaussian process models to quantify (possibly non-linear) functional evidence via Bayes factors, which are then mapped to a novel calibrated spike-and-slab prior, thus guiding selection and providing functional relevance to the associations with patient outcomes. Using simulations, we illustrate how integrative methods with functional calibration have higher power to detect disease related markers than non-integrative approaches. We demonstrate the profitability of fiBAG via a pan-cancer analysis of 14 cancer types to identify and assess the cellular mechanisms of proteogenomic markers associated with cancer stemness and patient survival.