Construct Gene-Environment (G×E) Interaction Matrix

With high-dimensional omics features, repeated measure ANOVA leads to longitudinal gene-environment interaction studies that have intra-cluster correlations, outlying observations and structured sparsity arising from the ANOVA design. In this package, we have developed robust sparse Bayesian mixed effect models tailored for the above studies (Fan et al. (2025) <doi:10.1093/jrsssc/qlaf027>). 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++'. The development of this software package and the associated statistical methods have been partially supported by an Innovative Research Award from Johnson Cancer Research Center, Kansas State University.

Kun Fan

mixedBayes

Bayesian Longitudinal Regularized Quantile Mixed Model

Shejuty Devnath

Cen Wu

GE function

<dl><dt>g</dt>
<dd>the long-format matrix of genetic predictors.</dd>
<dt>e</dt>
<dd>the long-format design matrix for environment/treatment effects.</dd></dl>

Arguments

Construct Gene-Environment (G×E) Interaction Matrix — GE

<dl>

<dt>g</dt>
<dd>the long-format matrix of genetic predictors.</dd>


<dt>e</dt>
<dd>the long-format design matrix for environment/treatment effects.</dd>

</dl>

GE: Construct Gene-Environment (G×E) Interaction Matrix

Description

Usage

Value

Arguments