DEPower: approximate power analysis with DESeq2

Gorin, G.; Guruge, D.; Goodman, L.

doi:10.64898/2026.02.05.704084

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bioRxiv · 10.64898/2026.02.05.704084

DEPower: approximate power analysis with DESeq2

Gorin, G.·Guruge, D.·Goodman, L.

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Abstract

Rigorous experimental design, including formal power analysis, is a cornerstone of reproducible RNA sequencing (RNA-seq) research. The design of RNA-seq experiments requires computing the minimum sample number required to identify an effect of a particular size at a predefined significance level. Ideally, the statistical test used for the analysis of experimental data should match the test used for sample size determination; however, few tools use the assumptions of the popular differential expression testing framework DESeq2, and most opt for simulation-based rather than analytical approaches. Grounded in the DESeq2 model framework, we derive sample size requirements for both single-cell and bulk RNA-seq experiments delivered as a web-based tool for power analysis, DEPower, available at https://poweranalysis-fb.streamlit.app/ that makes rigorous RNA-seq study design accessible to all researchers.

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Gorin, G., Guruge, D., Goodman, L.. 2026-02-09. DEPower: approximate power analysis with DESeq2. https://doi.org/10.64898/2026.02.05.704084

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