ArticleNature communications2025
Optimized murine sample sizes for RNA sequencing studies revealed from large scale comparative analysis.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Article
- Distinct immunometabolic signatures in peripheral blood mononuclear cells are linked to systemic inflammation in type 2 diabetes and diabetic kidney disease.Molecular biomedicine · 2026Article
- Multi-organ transcriptome profiling in an isogenic cachexia-inducing xenograft model to dissect host responses across heart, liver, kidney, and muscle.Human cell · 2026Article
- Multitissue, multi-time point transcriptomic atlas of aging in mice and rats.Science advances · 2026Article
- Article
- Gene screening and immune phenotype shifts of osteoporosis.Scientific reports · 2026Article
- A protocol for the impact of Tongmai Jiangtang capsules on the risk of cardiovascular and cerebrovascular events in metabolic syndrome.Frontiers in cardiovascular medicine · 2026Article
- Article
- Commentary: a review of technical considerations for planning an RNA-Sequencing experiment.BMC genomics · 2025Review
- Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq.Nature communications · 2025Article
- Sex-specific differential gene expression during stolonization in the branching syllid Ramisyllis kingghidorahi (Annelida, Syllidae).BMC genomics · 2025Article
- Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq.bioRxiv : the preprint server for biology · 2025Article
- A practical guide to sequencing in neuropsychiatric research.NPP - digital psychiatry and neuroscience · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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