Document Type

Article

Publication Date

2025

DOI

10.3390/math13183047

Publication Title

Mathematics

Volume

13

Issue

18

Pages

3047

Abstract

In RNA-seq data analysis, a primary objective is the identification of differentially expressed genes, which are genes that exhibit varying expression levels across different conditions of interest. It is widely known that hidden factors, such as batch effects, can substantially influence the differential expression analysis. Furthermore, apart from the primary factor of interest and unforeseen artifacts, an RNA-seq experiment typically contains multiple measured covariates, some of which may significantly affect gene expression levels, while others may not. Existing methods either address the covariate selection or the unknown artifacts separately. In this study, we investigate two integrated strategies, FSR_sva and SVAall_FSR, for jointly addressing covariate selection and hidden factors through simulations based on a real RNA-seq dataset. Our results show that when no available relevant covariates are strongly associated with the main factor of interest, FSR_sva performs comparably to existing methods. However, when some available relevant covariates are strongly correlated with the primary factor of interest–SVAall_FSR achieves the best performance among the compared methods.

Rights

© 2025 by the authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data Availability

Article states: "The R codes are available in the GitHub repository folder https://github.com/Farzana001-Noorzahan/covariate-selection-hidden-factor-rnaseq, accessed on 18 September 2025."

Original Publication Citation

Noorzahan, F., Jeon, H., & Nguyen, Y. (2025). Covariate selection for RNA-seq differential expression analysis with hidden factor adjustment. Mathematics, 13(8), Article 3047. https://doi.org/10.3390/math13183047

ORCID

0000-0003-4881-3476 (Nguyen)

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