  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.
