Work

Bayesian matrix completion for hypothesis testing

Year: 2023

Type: article

Abstract: We aim to infer bioactivity of each chemical by assay endpoint combination, addressing sparsity of toxicology data. We propose a Bayesian hierarchical framework which borrows information across different chemicals and assay endpoints, facilitates out-of-sample prediction of activity for chemicals not yet assayed, quantifies uncertainty of predicted activity, and adjusts for multiplicity in hypothesis testing. Furthermore, this paper makes a novel attempt in toxicology to simultaneously model heteroscedastic errors and a nonparametric mean function, leading to a broader definition of activity whose need has been suggested by toxicologists. Real application identifies chemicals most likely active for neurodevelopmental disorders and obesity.

Source: Journal of the Royal Statistical Society Series C (Applied Statistics)

DOI: 10.1093/jrsssc/qlac005

Authors: Bora Jin, David B Dunson, Julia E Rager, David M Reif, Stephanie M Engel, Amy H Herring

Institutions: Duke University, University of North Carolina at Chapel Hill, North Carolina State University

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funder: National Institute of Environmental Health Sciences

Awards: Structured nonparametric methods for mixtures of exposures, Reproducibility and Robustness of Dimensionality Reduction, UNC Center for Environmental Health and Susceptibility