A Penalized Regression-Based Biclustering Approach in Gene Expression Data

Clustering serves as a pivotal instrument in the realm of gene expression data analysis. This paper proposes a Biclustering Coefficient Estimation (BCE) method to identify groups in the individuals and genes. An alternating direction method of multipliers (ADMM) algorithm with a double fusion penalt...

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Bibliographic Details
Published inJournal of systems science and complexity Vol. 38; no. 4; pp. 1766 - 1783
Main Authors Wei, Mengxi, Zheng, Zhi, Zhang, Weiping
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2025
Springer Nature B.V
Subjects
Clustering
Complex Systems
Control
Data analysis
Gene expression
Identification methods
Mathematics
Mathematics and Statistics
Mathematics of Computing
Operations Research/Decision Theory
Statistics
Systems Theory
biclustering analysis
penalized fusion
ADMM algorithm
lung adenocarcinoma
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Summary:Clustering serves as a pivotal instrument in the realm of gene expression data analysis. This paper proposes a Biclustering Coefficient Estimation (BCE) method to identify groups in the individuals and genes. An alternating direction method of multipliers (ADMM) algorithm with a double fusion penalty is developed to solve the problem. The authors rigorously establish the oracle properties for the proposed penalized estimator. Numerical studies, including simulations and analysis of a lung adenocarcinoma dataset, suggest that the proposed method is expected to simultaneously recover reasonable potential groups of samples and covariates and provide satisfactory estimates of group coefficients.
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ISSN:1009-6124
1559-7067
DOI:10.1007/s11424-024-4025-z