Global FDR control across multiple RNAseq experiments.

Autor:	Liou L; Merck Research Laboratories, Merck & Co., Kenilworth, NJ 07033, USA., Hornburg M; Merck Research Laboratories, Merck & Co., Kenilworth, NJ 07033, USA., Robertson DS; MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK.
Jazyk:	angličtina
Zdroj:	Bioinformatics (Oxford, England) [Bioinformatics] 2023 Jan 01; Vol. 39 (1).
DOI:	10.1093/bioinformatics/btac718
Abstrakt:	Motivation: While classical approaches for controlling the false discovery rate (FDR) of RNA sequencing (RNAseq) experiments have been well described, modern research workflows and growing databases enable a new paradigm of controlling the FDR globally across RNAseq experiments in the past, present and future. The simplest analysis strategy that analyses each RNAseq experiment separately and applies an FDR correction method can lead to inflation of the overall FDR. We propose applying recently developed methodology for online multiple hypothesis testing to control the global FDR in a principled way across multiple RNAseq experiments. Results: We show that repeated application of classical repeated offline approaches has variable control of global FDR of RNAseq experiments over time. We demonstrate that the online FDR algorithms are a principled way to control FDR. Furthermore, in certain simulation scenarios, we observe empirically that online approaches have comparable power to repeated offline approaches. Availability and Implementation: The onlineFDR package is freely available at http://www.bioconductor.org/packages/onlineFDR. Additional code used for the simulation studies can be found at https://github.com/latlio/onlinefdr_rnaseq_simulation. Supplementary Information: Supplementary data are available at Bioinformatics online. (© The Author(s) 2022. Published by Oxford University Press.)
Databáze:	MEDLINE
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