Optimized murine sample sizes for RNA sequencing studies revealed from large scale comparative analysis

Gabor Halasz, Jennifer Schmahl, Nicole Negron, Min Ni, Wei Keat Lim, Gurinder S. Atwal, Yu Bai and David J. Glass ()
Additional contact information
Gabor Halasz: Molecular Profiling & Data Science, Regeneron Pharmaceuticals
Jennifer Schmahl: Aging and Age-Related Disorders, Regeneron Pharmaceuticals
Nicole Negron: Molecular Profiling & Data Science, Regeneron Pharmaceuticals
Min Ni: Molecular Profiling & Data Science, Regeneron Pharmaceuticals
Wei Keat Lim: Molecular Profiling & Data Science, Regeneron Pharmaceuticals
Gurinder S. Atwal: Molecular Profiling & Data Science, Regeneron Pharmaceuticals
Yu Bai: Molecular Profiling & Data Science, Regeneron Pharmaceuticals
David J. Glass: Aging and Age-Related Disorders, Regeneron Pharmaceuticals

Nature Communications, 2025, vol. 16, issue 1, 1-9

Abstract: Abstract Determining the appropriate sample size (N) for bulk RNA sequencing experiments is critical for obtaining reliable results. We show in two N = 30 profiling studies, comparing wild-type mice and mice in which one copy of a gene has been deleted, the N required to minimize false positives and maximize true discoveries found in the N = 30 experiment. Results from experiments with N = 4 or less are shown to be highly misleading, given the high false positive rate and the lack of discovery of genes later found with higher N. For a cut-off of 2-fold expression differences, we find an N of 6-7 mice is required to consistently decrease the false positive rate to below 50%, and the detection sensitivity to above 50%. More is always better for both metrics – and an N of 8-12 is significantly better in recapitulating the full experiment.A common way to reduce the false discovery rate in underpowered experiments is to raise the fold cutoff. We show that this strategy is no substitute for increasing the N of the experiment: it results in consistently inflated effect sizes and causes a substantial drop in sensitivity of detection. These data should be helpful to scientists in choosing their Ns.

Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-65022-5 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-65022-5

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-65022-5

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().