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Springer: An R package for bi-level variable selection of high-dimensional longitudinal data.

Fei Zhou1, Yuwen Liu1, Jie Ren2

  • 1Department of Statistics, Kansas State University, Manhattan, KS, United States.

Frontiers in Genetics
|April 24, 2023
PubMed
Summary

This study introduces "springer," an R package for bi-level variable selection in longitudinal data analysis. It implements advanced penalization methods for gene-environment interaction studies, enhancing high-dimensional data analysis.

Keywords:
bi-level variable selectiongeneralized estimating equationgene–environment interactionquadratic inference functionrepeated measurements

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Area of Science:

  • Statistics
  • Bioinformatics
  • Computational Biology

Background:

  • Bi-level variable selection is crucial for high-dimensional data, with existing methods for various response types.
  • Previous work extended bi-level selection to longitudinal data using quadratic inference functions (QIF) for gene-environment interactions.
  • The QIF framework offers simultaneous penalization at group and within-group levels.

Purpose of the Study:

  • Introduce the R package "springer" for implementing advanced longitudinal penalization methods.
  • Demonstrate the utility of "springer" for bi-level variable selection within the QIF framework.
  • Provide a comprehensive resource for researchers in gene-environment interaction studies.

Main Methods:

  • The "springer" R package implements bi-level variable selection using a quadratic inference function (QIF) framework.
  • It also incorporates generalized estimating equation-based sparse group penalization.
  • The package offers alternative methods for group-level and individual-level selection.

Main Results:

  • The study systematically introduces longitudinal penalization methods available in the "springer" package.
  • Demonstrates the practical application and usage of the package's core and supporting functions.
  • Includes numerical examples and discussions to illustrate the methods' effectiveness.

Conclusions:

  • The "springer" R package provides a robust implementation of advanced bi-level variable selection methods for longitudinal data.
  • It facilitates gene-environment interaction studies by offering flexible penalization strategies.
  • The package is readily available for researchers to enhance their high-dimensional data analysis.