Document Type

Article

Publication Date

2022

DOI

10.1186/s12874-022-01530-y

Publication Title

BMC Medical Research Methodology

Volume

22

Pages

43 (1-12)

Abstract

BACKGROUND: Recent high-throughput technologies have opened avenues for simultaneous analyses of thousands of genes. With the availability of a multitude of public databases, one can easily access multiple genomic study results where each study comprises of significance testing results of thousands of genes. Researchers currently tend to combine this genomic information from these multiple studies in the form of a meta-analysis. As the number of genes involved is very large, the classical meta-analysis approaches need to be updated to acknowledge this large-scale aspect of the data.

METHODS: In this article, we discuss how application of standard theoretical null distributional assumptions of the classical meta-analysis methods, such as Fisher's p-value combination and Stouffer's Z, can lead to incorrect significant testing results, and we propose a robust meta-analysis method that empirically modifies the individual test statistics and p-values before combining them.

RESULTS: Our proposed meta-analysis method performs best in significance testing among several meta-analysis approaches, especially in presence of hidden confounders, as shown through a wide variety of simulation studies and real genomic data analysis.

CONCLUSION: The proposed meta-analysis method produces superior meta-analysis results compared to the standard p-value combination approaches for large-scale simultaneous testing in genomic experiments. This is particularly useful in studies with large number of genes where the standard meta-analysis approaches can result in gross false discoveries due to the presence of unobserved confounding variables.

Comments

© The Authors 2022

This article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The Creative Commons Public Domain Dedication (CC0 1.0) waiver applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Original Publication Citation

Sikdar, S. (2022). Robust meta-analysis for large-scale genomic experiments based on an empirical approach. BMC Medical Research Methodology, 22, 1-12, Article 43. https://doi.org/10.1186/s12874-022-01530-y

ORCID

0000-0003-1230-5162 (Sikdar)

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