Document Type

Article

Publication Date

2023

DOI

10.1093/bioinformatics/btad498

Publication Title

Bioinformatics

Volume

39

Issue

8

Pages

btad498 (1-9)

Abstract

Summary

This paper suggests a novel positive false discovery rate (pFDR) controlling method for testing gene-specific hypotheses using a gene-specific covariate variable, such as gene length. We suppose the null probability depends on the covariate variable. In this context, we propose a rejection rule that accounts for heterogeneity among tests by employing two distinct types of null probabilities. We establish a pFDR estimator for a given rejection rule by following Storey's q-value framework. A condition on a type 1 error posterior probability is provided that equivalently characterizes our rejection rule. We also present a suitable procedure for selecting a tuning parameter through cross-validation that maximizes the expected number of hypotheses declared significant. A simulation study demonstrates that our method is comparable to or better than existing methods across realistic scenarios. In data analysis, we find support for our method's premise that the null probability varies with a gene-specific covariate variable.

Rights

© The Authors 2023.

This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

Data Availability

Article states: The source code repository is publicly available at https://github.com/hsjeon1217/conditional_method

Original Publication Citation

Jeon, H., Lim, K. S., Nguyen, Y., & Nettleton, D. (2023). Adjusting for gene-specific covariates to improve RNA-seq analysis. Bioinformatics, 39(8), 1-7, Article btad498. https://doi.org/10.1093/bioinformatics/btad498

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