False discovery rate control for sparse correlation matrix.
5 affiliations
- 1Department of Statistics, University of Leeds, Leeds, LS2 9JT, United Kingdom.
- 2Department of Mathematics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia.
- 3Mathematics Division, Centre for Foundation Studies in Science, Universiti Malaya, Kuala Lumpur 50603, Malaysia.
- 4Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam 40450, Malaysia.
- 5Leeds Institute of Medical Research at St. James's, University of Leeds, Leeds, LS9 7TF, United Kingdom.
Abstract
In genomic data analysis, high-dimensional correlation matrices pose challenges in interpreting complex relationships among genomic features, particularly due to overfitting. Traditional approaches generate sparse correlation matrices by setting correlations below a predetermined threshold to zero, with the threshold typically determined through Frobenius norm minimization via cross-validation. In this study, we propose a novel alternative thresholding strategy based on an empirical Bayes framework that controls the false discovery rate among nonzero correlations. This method offers two key advantages: first, it is compatible with any correlation measure and, second, it provides an estimation of the expected false positives among nonzero correlations. By formulating the construction of a sparse correlation matrix as both an estimation and an inference problem, our approach enhances interpretability and demonstrates greater sensitivity in retaining true biological correlations than threshold selection based on the minimization of Frobenius norm. The relevant R package called scfdr is available from github.com/ariefgusnanto/scfdr.
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BibTeX
@article{gusnanto2026false,
title = {{False discovery rate control for sparse correlation matrix.}},
author = {Arief Gusnanto and Basuki Widodo and Norli Anida Abdullah and Ahmad Zia Ul-Saufie and Lucy F Stead},
journal = {Bioinformatics advances},
year = {2026},
month = oct,
doi = {10.1093/bioadv/vbag314},
url = {https://europepmc.org/article/MED/42999928},
note = {CC BY},
}
RIS
TY - JOUR
TI - False discovery rate control for sparse correlation matrix.
AU - Arief Gusnanto
AU - Basuki Widodo
AU - Norli Anida Abdullah
AU - Ahmad Zia Ul-Saufie
AU - Lucy F Stead
PY - 2026
DA - 2026/10/19
JO - Bioinformatics advances
DO - 10.1093/bioadv/vbag314
UR - https://europepmc.org/article/MED/42999928
AB - In genomic data analysis, high-dimensional correlation matrices pose challenges in interpreting complex relationships among genomic features, particularly due to overfitting. Traditional approaches generate sparse correlation matrices by setting correlations below a predetermined threshold to zero, with the threshold typically determined through Frobenius norm minimization via cross-validation. In this study, we propose a novel alternative thresholding strategy based on an empirical Bayes framework that controls the false discovery rate among nonzero correlations. This method offers two key advantages: first, it is compatible with any correlation measure and, second, it provides an estimation of the expected false positives among nonzero correlations. By formulating the construction of a sparse correlation matrix as both an estimation and an inference problem, our approach enhances interpretability and demonstrates greater sensitivity in retaining true biological correlations than threshold selection based on the minimization of Frobenius norm. The relevant R package called scfdr is available from github.com/ariefgusnanto/scfdr.
SN - 2635-0041
ER -