| springer-package {springer} | R Documentation |
Sparse Group Variable Selection for Gene-Environment Interactions in the Longitudinal Study
Description
In this package, we provide a set of regularized variable selection methods tailored for longitudinal studies of gene- environment interactions. The proposed method conducts sparse group variable selection by accounting for bi-level sparsity. Specifically, the individual and group level penalties have been simultaneously imposed to identify important main and interaction effects under three working correlation structures (exchangeable , AR-1 and independence), based on either the quadratic inference function (QIF) or generalized estimating equation (GEE). In addition, only the individual or group level selection in the longitudinal setting can also be conducted using springer. In total, springer provides 18 (=3\times3\times2) methods. Among them, sparse group variable selection for longitudinal studies have been developed for the first time. Please read the Details below for how to configure the method used.
Details
Users can flexibly choose the methods to fit the model by specifying the three arguments in the user interface springer():
| func: | the framework to obtain the score equation. Two choices are available: |
| "GEE" and "QIF". | |
| corr: | working correlation. Three choices are available: |
| "exchangeable", "AR-1" and "independence". | |
| structure: | structural identification. Three choices are available: |
| "bilevel", "group" and "individual". |
The function springer() returns a springer object that contains the estimated coefficients.
References
Zhou, F., Liu, Y., Ren, J., Wang, W., and Wu, C. (2023). Springer: An R package for bi-level variable selection of high-dimensional longitudinal data. Frontiers in Genetics, 14, 1088223 doi:10.3389/fgene.2023.1088223
Zhou, F., Lu, X., Ren, J., Fan, K., Ma, S. and Wu, C. (2022). Sparse Group Variable Selection for Gene-Environment Interactions in the Longitudinal Study. Genetic Epidemiology, 46(5-6), 317-340 doi:10.1002/gepi.22461
Zhou, F., Ren, J., Lu, X., Ma, S. and Wu, C. (2021). Gene-Environment Interaction: a Variable Selection Perspective. Epistasis: Methods and Protocols, Springer US doi:10.1007/978-1-0716-0947-7_13
Zhou, F., Ren, J., Li, G., Jiang, Y., Li, X., Wang, W. and Wu, C. (2019). Penalized Variable Selection for Lipid-Environment Interactions in a Longitudinal Lipidomics Study. Genes, 10(12), 1002 doi:10.3390/genes10121002
Zhou, F., Ren, J., Li, X., Wu, C. and Jiang, Y. (2019) interep: Interaction Analysis of Repeated Measure Data. R package version 0.3.1. https://CRAN.R-project.org/package=interep
Ren, J., Du, Y., Li, S., Ma, S., Jiang, Y. and Wu, C. (2019). Robust network-based regularization and variable selection for high-dimensional genomic data in cancer prognosis. Genetic epidemiology, 43(3), 276-291 doi:10.1002/gepi.22194
Wu, C., Zhang, Q., Jiang, Y. and Ma, S. (2018). Robust network-based analysis of the associations between (epi) genetic measurements. Journal of multivariate analysis, 168, 119-130 doi:10.1016/j.jmva.2018.06.009
Wu, C., Jiang, Y., Ren, J., Cui, Y. and Ma, S. (2018). Dissecting gene-environment interactions: A penalized robust approach accounting for hierarchical structures. Statistics in Medicine, 37:437-456 doi:10.1002/sim.7518
Wu, C., Zhong, P.S. and Cui, Y. (2018). Additive varying-coefficient model for nonlinear gene-environment interactions. Statistical Applications in Genetics and Molecular Biology, 17(2) doi:10.1515/sagmb-2017-0008
Ren, J., He, T., Li, Y., Liu, S., Du, Y., Jiang, Y. and Wu, C. (2017). Network-based regularization for high dimensional SNP data in the case-control study of Type 2 diabetes. BMC genetics, 18(1), 44 doi:10.1186/s12863-017-0495-5
Wu, C., Shi, X., Cui, Y. and Ma, S. (2015). A penalized robust semiparametric approach for gene-environment interactions. Statistics in Medicine, 34 (30): 4016-4030 doi:10.1002/sim.6609
Wu, C., Cui, Y., and Ma, S. (2014). Integrative analysis of gene-environment interactions under a multi-response partially linear varying coefficient model. Statistics in Medicine, 33(28), 4988-4998 doi:10.1002/sim.6287
Wu, C. and Cui, Y. (2014). Boosting signals in gene-based association studies via efficient SNP selection. Briefings in bioinformatics, 15(2), 279-291 doi:10.1093/bib/bbs087
Wu, C., Zhong, P.S. and Cui, Y. (2013). High dimensional variable selection for gene-environment interactions. Technical Report. Michigan State University.