Print  Close

Efficient Bayesian Additive Regression Models for Microbiome and Gene Expression Studies

Abstract Number:

2842 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Tinghua Chen (1), Michelle Nixon (1), Justin Silverman (1)

Institutions:

(1) Pennsylvania State University, State College, PA

Co-Author(s):

Michelle Nixon  
Pennsylvania State University
Justin Silverman  
Pennsylvania State University

First Author:

Tinghua Chen  
Pennsylvania State University

Presenting Author:

Tinghua Chen  
N/A

Abstract Text:

We propose a flexible family of Bayesian multinomial logistic-normal additive Gaussian process regression (MLN) models for estimating additive linear and non-linear effects in microbiome and gene expression studies. This family has a marginally latent matrix-t process (MLTP) form, facilitating efficient and accurate inference via a particle filter with marginal Laplace approximation. We also develop a maximum marginal likelihood estimation method for model hyperparameters. We demonstrate the efficiency and utility of these models for estimating linear and non-linear effects through analyses of real and simulated sequence count data.

Keywords:

Bayesian Statistics| Nonlinear Regression|Gaussian Processes|Microbiome Data|Gene Expression Data|

Can this be considered for alternate subtype?

Yes

Are you interested in volunteering to serve as a session chair?

Yes

I have read and understand that JSM participants must abide by the Participant Guidelines.

Yes

I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.

I understand