Efficient Bayesian Additive Regression Models For Microbiome Studies

Tinghua Chen, Michelle Pistner Nixon, Justin D Silverman

Keywords: Additive Gaussian Process, Multinomial Logistic-Normal(MLN), Sequence count data

Abstract:

Bayesian multinomial logistic-normal (MLN) models have gained popularity due
to their ability to account for the count compositional nature of microbiome
data. Recently, we developed a computationally efficient and accurate approach
to inferring MLN models with a Marginally Latent Matrix-T Process (MLTP)
form: MLN-MLTPs. However, previous research on MLTPs has been restricted
to linear models or a single non-linear process. This article addresses this
deficiency by introducing a new class of MLN Additive Gaussian Process models
(MultiAddGPs) for deconvolution of overlapping linear and non-linear processes.
We show that MultiAddGPs are examples of MLN-MLTPs and derive an efficient
Collapse-Uncollapse (CU) sampler for this model class. Through simulation
studies, we show that MultiAddGPs accurately and efficiently decompose over-
lapping effects in microbiota data, which provides a powerful tool for analyzing
complex count compositional datasets

Submission Number: 37