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Stable Variable Selection for High-Dimensional Genomic Data with Strong Correlations

Stable Variable Selection for High-Dimensional Genomic Data with Strong Correlations High-dimensional genomic data studies are often found to exhibit strong correlations, which results in instability and inconsistency in the estimates obtained using commonly used regularization approaches including the Lasso and MCP, etc. In this paper, we perform comparative study of regularization approaches for variable selection under different correlation structures and propose a two-stage procedure named rPGBS to address the issue of stable variable selection in various strong correlation settings. This approach involves repeatedly running a two-stage hierarchical approach consisting of a random pseudo-group clustering and bi-level variable selection. Extensive simulation studies and high-dimensional genomic data analysis on real datasets have demonstrated the advantage of the proposed rPGBS method over some of the most used regularization methods. In particular, rPGBS results in more stable selection of variables across a variety of correlation settings, as compared to some recent methods addressing variable selection with strong correlations: Precision Lasso (Wang et al. in Bioinformatics 35:1181–1187, 2019) and Whitening Lasso (Zhu et al. in Bioinformatics 37:2238–2244, 2021). Moreover, rPGBS has been shown to be computationally efficient across various settings. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Data Science Springer Journals

Stable Variable Selection for High-Dimensional Genomic Data with Strong Correlations

Sarkar, Reetika; Manage, Sithija; Gao, Xiaoli
Annals of Data Science , Volume OnlineFirst – Jun 29, 2023
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References (43)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
2198-5804
eISSN
2198-5812
DOI
10.1007/s40745-023-00481-5
Publisher site
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Abstract

High-dimensional genomic data studies are often found to exhibit strong correlations, which results in instability and inconsistency in the estimates obtained using commonly used regularization approaches including the Lasso and MCP, etc. In this paper, we perform comparative study of regularization approaches for variable selection under different correlation structures and propose a two-stage procedure named rPGBS to address the issue of stable variable selection in various strong correlation settings. This approach involves repeatedly running a two-stage hierarchical approach consisting of a random pseudo-group clustering and bi-level variable selection. Extensive simulation studies and high-dimensional genomic data analysis on real datasets have demonstrated the advantage of the proposed rPGBS method over some of the most used regularization methods. In particular, rPGBS results in more stable selection of variables across a variety of correlation settings, as compared to some recent methods addressing variable selection with strong correlations: Precision Lasso (Wang et al. in Bioinformatics 35:1181–1187, 2019) and Whitening Lasso (Zhu et al. in Bioinformatics 37:2238–2244, 2021). Moreover, rPGBS has been shown to be computationally efficient across various settings.

Journal

Annals of Data Science – Springer Journals

Published: Jun 29, 2023

Keywords: Bi-level sparsity; Minimax concave penalty; Stability; Strong correlation; Variable selection

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