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Zhou F, Liu Y, Ren J, Wang W, Wu C. Springer: An R package for bi-level variable selection of high-dimensional longitudinal data. Front Genet 2023; 14:1088223. [PMID: 37091810 PMCID: PMC10117642 DOI: 10.3389/fgene.2023.1088223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 02/28/2023] [Indexed: 04/09/2023] Open
Abstract
In high-dimensional data analysis, the bi-level (or the sparse group) variable selection can simultaneously conduct penalization on the group level and within groups, which has been developed for continuous, binary, and survival responses in the literature. Zhou et al. (2022) (PMID: 35766061) has further extended it under the longitudinal response by proposing a quadratic inference function-based penalization method in gene–environment interaction studies. This study introduces “springer,” an R package implementing the bi-level variable selection within the QIF framework developed in Zhou et al. (2022). In addition, R package “springer” has also implemented the generalized estimating equation-based sparse group penalization method. Alternative methods focusing only on the group level or individual level have also been provided by the package. In this study, we have systematically introduced the longitudinal penalization methods implemented in the “springer” package. We demonstrate the usage of the core and supporting functions, which is followed by the numerical examples and discussions. R package “springer” is available at https://cran.r-project.org/package=springer.
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Affiliation(s)
- Fei Zhou
- Department of Statistics, Kansas State University, Manhattan, KS, United States
| | - Yuwen Liu
- Department of Statistics, Kansas State University, Manhattan, KS, United States
| | - Jie Ren
- Department of Biostatistics and Health Data Sciences, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Weiqun Wang
- Department of Food, Nutrition, Dietetics and Health, Kansas State University, Manhattan, KS, United States
| | - Cen Wu
- Department of Statistics, Kansas State University, Manhattan, KS, United States
- *Correspondence: Cen Wu,
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Zhou F, Ren J, Liu Y, Li X, Wang W, Wu C. Interep: An R Package for High-Dimensional Interaction Analysis of the Repeated Measurement Data. Genes (Basel) 2022; 13:genes13030544. [PMID: 35328097 PMCID: PMC8950762 DOI: 10.3390/genes13030544] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 03/12/2022] [Accepted: 03/13/2022] [Indexed: 02/05/2023] Open
Abstract
We introduce interep, an R package for interaction analysis of repeated measurement data with high-dimensional main and interaction effects. In G × E interaction studies, the forms of environmental factors play a critical role in determining how structured sparsity should be imposed in the high-dimensional scenario to identify important effects. Zhou et al. (2019) (PMID: 31816972) proposed a longitudinal penalization method to select main and interaction effects corresponding to the individual and group structure, respectively, which requires a mixture of individual and group level penalties. The R package interep implements generalized estimating equation (GEE)-based penalization methods with this sparsity assumption. Moreover, alternative methods have also been implemented in the package. These alternative methods merely select effects on an individual level and ignore the group-level interaction structure. In this software article, we first introduce the statistical methodology corresponding to the penalized GEE methods implemented in the package. Next, we present the usage of the core and supporting functions, which is followed by a simulation example with R codes and annotations. The R package interep is available at The Comprehensive R Archive Network (CRAN).
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Affiliation(s)
- Fei Zhou
- Department of Statistics, Kansas State University, Manhattan, KS 66506, USA; (F.Z.); (Y.L.); (X.L.)
| | - Jie Ren
- Department of Biostatistics and Health Data Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA;
| | - Yuwen Liu
- Department of Statistics, Kansas State University, Manhattan, KS 66506, USA; (F.Z.); (Y.L.); (X.L.)
| | - Xiaoxi Li
- Department of Statistics, Kansas State University, Manhattan, KS 66506, USA; (F.Z.); (Y.L.); (X.L.)
| | - Weiqun Wang
- Department of Food, Nutrition, Dietetics and Health, Kansas State University, Manhattan, KS 66506, USA;
| | - Cen Wu
- Department of Statistics, Kansas State University, Manhattan, KS 66506, USA; (F.Z.); (Y.L.); (X.L.)
- Correspondence: ; Tel.: +1-7855322231
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