Bayesian Kernel Machine Regression Model for Analysing Sequence Count Data

1608 

Contributed Abstract 

Poster 

Mosammat Sonia Khatun (1), Ander Wilson (2)

(1) N/A, N/A, (2) Colorado State University, N/A

Ander Wilson  
Colorado State University

Mosammat Sonia Khatun  
N/A

Mosammat Sonia Khatun  
N/A

RNA sequencing (RNA-Seq) is a powerful technology for quantifying gene expression and identifying genes influenced by environmental exposures or other treatments. There is currently interest in how mixtures of multiple environmental chemicals affect gene expression using RNA-Seq data. There are many popular methods for RNA-Seq analysis; however, none focus on correlated environmental exposures. The existing Bayesian kernel machine regression (BKMR) effectively analyses mixture effects for continuous outcomes but cannot handle the unique challenges of RNA-Seq count data. We develop BKMRSeq, a novel BKMR model tailored for RNA-Seq count data to address this gap. BKMRSeq uses Polya-Gamma augmentation within a Markov chain Monte Carlo (MCMC) framework to estimate a complex non-linear association between exposures and gene expression using a Gaussian kernel matrix and select genes that are differentially expressed. Through simulation studies, BKMRSeq demonstrates superior performance compared to the existing methods to analyze gene expression when there is a complex exposure-response relation. We further validate its utility by applying it to real-world RNA-Seq data.

RNA-Seq; Correlated environmental exposures; BKMR; Polya-gamma distribution; Data augmentation| | | | |

Section on Bayesian Statistical Science

Bayesian Computation