BhGLM: Bayesian hierarchical GLMs and survival models, with applications to genomics and epidemiology

• Published in: Bioinformatics Vol. 35; no. 8; pp. 1419 - 1421
• Main Authors: Yi, Nengjun, Tang, Zaixiang, Zhang, Xinyan, Guo, Boyi
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 15.04.2019
• Subjects: Algorithms; Applications Notes; Bayes Theorem; Genomics; Linear Models; Proportional Hazards Models
• ISSN: 1367-4803; 1367-4811; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/bty803

Abstract Summary BhGLM is a freely available R package that implements Bayesian hierarchical modeling for high-dimensional clinical and genomic data. It consists of functions for setting up various Bayesian hierarchical models, including generalized linear models (GLMs) and Cox survival models, with four types of prior distributions for coefficients, i.e. double-exponential, Student-t, mixture double-exponential and mixture Student-t. These functions adapt fast and stable algorithms to estimate parameters. BhGLM also provides functions for summarizing results numerically and graphically and for evaluating predictive values. The package is particularly useful for analyzing large-scale molecular data, i.e. detecting disease-associated variables and predicting disease outcomes. We here describe the models, algorithms and associated features implemented in BhGLM. Availability and implementation The package is freely available from the public GitHub repository, https://github.com/nyiuab/BhGLM.