regnet - Network-Based Regularization for Generalized Linear Models

Network-based regularization has achieved success in variable selection for high-dimensional biological data due to its ability to incorporate correlations among genomic features. This package provides procedures of network-based variable selection for generalized linear models (Ren et al. (2017) <doi:10.1186/s12863-017-0495-5> and Ren et al.(2019) <doi:10.1002/gepi.22194>). Continuous, binary, and survival response are supported. Robust network-based methods are available for continuous and survival responses.

Last updated 9 months ago

cancer-prognosislogistic-regressionnetworkpenalized-regressionregularizationsurvival

3.30 score 4 stars 6 scripts 162 downloads

roben - Robust Bayesian Variable Selection for Gene-Environment Interactions

Gene-environment (G×E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of G×E studies have been commonly encountered, leading to the development of a broad spectrum of robust penalization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a robust Bayesian variable selection method for G×E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects. An efficient Gibbs sampler has been developed to facilitate fast computation. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in C++.

Last updated 8 months ago

3.00 score 3 scripts 120 downloads

spinBayes - Semi-Parametric Gene-Environment Interaction via Bayesian Variable Selection

Many complex diseases are known to be affected by the interactions between genetic variants and environmental exposures beyond the main genetic and environmental effects. Existing Bayesian methods for gene-environment (G×E) interaction studies are challenged by the high-dimensional nature of the study and the complexity of environmental influences. We have developed a novel and powerful semi-parametric Bayesian variable selection method that can accommodate linear and nonlinear G×E interactions simultaneously (Ren et al. (2020) <doi:10.1002/sim.8434>). Furthermore, the proposed method can conduct structural identification by distinguishing nonlinear interactions from main effects only case within Bayesian framework. Spike-and-slab priors are incorporated on both individual and group level to shrink coefficients corresponding to irrelevant main and interaction effects to zero exactly. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in C++.

Last updated 8 months ago

bayesian-variable-selectiongene-environment-interactionshigh-dimensional-datasemi-parametric-modeling

3.00 score 1 stars 3 scripts 139 downloads