A Bayesian integrative approach for multi-platform genomic data: A kidney cancer case study.
Integration of genomic data from multiple platforms has the capability to increase precision, accuracy, and statistical power in the identification of prognostic biomarkers. A fundamental problem faced in many multi-platform studies is unbalanced sample sizes due to the inability to obtain measurements from all the platforms for all the patients in the study. We have developed a novel Bayesian approach that integrates multi-regression models to identify a small set of biomarkers that can accurately predict time-to-event outcomes. This method fully exploits the amount of available information across platforms and does not exclude any of the subjects from the analysis. Through simulations, we demonstrate the utility of our method and compare its performance to that of methods that do not borrow information across regression models. Motivated by The Cancer Genome Atlas kidney renal cell carcinoma dataset, our methodology provides novel insights missed by non-integrative models.
Department of Mathematics and Statistics, University of Minnesota Duluth, Duluth, MN 55812, USA.
Thierry
T
Chekouo
Dipartimento di Statistica, Informatica, Applicazioni "G.Parenti", University of Florence, 50134 Florence, Italy.
Francesco C
FC
Stingo
CSIRO Health and Biosecurity/Australian e-Health Research Center Level 5, Queensland 4029, Australia.
James D
JD
Doecke
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Kim-Anh
KA
Do
D001499:Bayes Theorem; D002292:Carcinoma, Renal Cell; D023281:Genomics; D006801:Humans; D007680:Kidney Neoplasms
Bayesian variable selection; Integrating multi-regressions; Markov random field; Multiplatform genomic data; Non-local prior