---
title: "Single-cell study designs lack power to detect small-effect genes in brain cell types"
id: "biorxiv-14-single-cell-study-designs-are-systematically-underpowered-for-small-effect-genes"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-14-single-cell-study-designs-are-systematically-underpowered-for-small-effect-genes"
content_type: "clinical_feed_article"
specialty: "Neurology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.09.17.752063v1?rss=1"
published_at: "2026-09-19T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Single-cell study designs lack power to detect small-effect genes in brain cell types
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-14-single-cell-study-designs-are-systematically-underpowered-for-small-effect-genes
- **Specialty:** [Neurology](https://medichelpline.com/clinical-feed/neurology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.09.17.752063v1?rss=1)
- **Published At:** 2026-09-19T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The authors used **sex-biased differential expression** across 1,494 donors in three brain cell types to produce empirically grounded power estimates for single-cell study designs. - They report a substantial lack of statistical **power** to detect small-effect genes, even in experiments containing up to 600 donors. - When astrocyte data were downsampled to match the poorer sequencing characteristics of **microglia**, more than half of the previously detected **DEGs** were lost, showing the sensitivity of results to sequencing depth and cell counts. - Standard multiple-testing corrected adjusted p-value thresholds yielded many nonreproducible findings; only the top quartile of significant genes were reproducible in empirical comparisons. - A predictive model fitted to empirical outcomes identified **cell count** and **gene expression level** as strong determinants of power. - The authors recommend cell type enrichment and **deeper sequencing**, particularly for rarer populations such as microglia, and argue for stricter significance thresholds when seeking robust differential-expression discoveries. - Data and code are available from the authors’ repository (GitHub link provided in the source). - The preprint was posted September 19, 2026, and the authors declared no competing interests.
## Clinical Analysis & Structured Key Points
Single-Cell Study Designs Are Systematically Underpowered for Small-Effect Genes | bioRxiv Skip to main content New Results Single-Cell Study Designs Are Systematically Underpowered for Small-Effect Genes View ORCID Profile Chris Crampton , View ORCID Profile Salman Fawad , View ORCID Profile Toby Clark , View ORCID Profile Hiru Dash , View ORCID Profile Bence Kövér , View ORCID Profile Garry Cotton , View ORCID Profile Donghoon Lee , View ORCID Profile Eugene Duff , View ORCID Profile Leonardo Bottolo , View ORCID Profile Paul M Matthews , View ORCID Profile Nathan Skene doi: https://doi.org/10.64898/2026.09.17.752063 Chris Crampton 1 Imperial College London; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Chris Crampton Salman Fawad 1 Imperial College London; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Salman Fawad Toby Clark 1 Imperial College London; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Toby Clark Hiru Dash 1 Imperial College London; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Hiru Dash Bence Kövér 2 King's College London; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Bence Kövér Garry Cotton 1 Imperial College London; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Garry Cotton Donghoon Lee 3 Icahn School of Medicine at Mount Sinai; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Donghoon Lee Eugene Duff 1 Imperial College London; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Eugene Duff Leonardo Bottolo 4 University of Cambridge; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Leonardo Bottolo Paul M Matthews 5 Imperial College, London Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Paul M Matthews Nathan Skene 1 Imperial College London; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nathan Skene For correspondence: n.skene{at}imperial.ac.uk Abstract Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Single cell differential expression analysis enables biologists to make statistical conclusions about which genes are up or downregulated in a particular cell type, between two conditions, such as those with or without a disease. However, due to biological and technical noise, these differences are hard to detect reliably. Determining if an experiment has sufficient power is often derived from simulations or small pilot samples, if done at all. Here, we use sex-biased differential expression on 1,494 donors in three brain cell types to derive empirically grounded power estimates for a variety of experimental setups. Our work reveals a substantial lack of power for reliably detecting the small effects in the range that many studies report, even in experiments containing 600 donors. When reducing the astrocyte data to the poor sequencing characteristics of microglia, over half the differentially expressed genes (DEGs) detected in the full set were lost, highlighting the damage caused by insufficient sequencing depth and cell counts. Despite standard thresholds of adjusted p-value with multiple testing correction, only the top quartile of significant genes were reproducible. Furthermore, from fitting a predictive model to the empirical outcomes, we find cell count and expression levels of genes to be a strong determinant of power. As such, we advocate for future studies to employ cell type enrichment and deeper sequencing, especially for rarer populations like microglia, emphasise the importance of powering experiments of this type when seeking robust findings, and suggest stricter significance thresholds for future discoveries. Competing Interest Statement The authors have declared no competing interest. Footnotes https://github.com/neurogenomics/Empirical_Power_Analysis/ Funder Information Declared UK Dementia Research Institute , UK DRI-5008 UK Research and Innovation , MR/T04327X/1 , MR/W029790/1 , UKRI2755 Wellcome Trust , 218461/Z/19/Z NIHR Cambridge Biomedical Research Centre , NIHR203312 Edmond J. Safra Philanthropic Foundation, https://ror.org/032j40h62 Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license . Back to top Previous Next Posted September 19, 2026. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Single-Cell Study Designs Are Systematically Underpowered for Small-Effect Genes Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Single-Cell Study Designs Are Systematically Underpowered for Small-Effect Genes Chris Crampton , Salman Fawad , Toby Clark , Hiru Dash , Bence Kövér , Garry Cotton , Donghoon Lee , Eugene Duff , Leonardo Bottolo , Paul M Matthews , Nathan Skene bioRxiv 2026.09.17.752063; doi: https://doi.org/10.64898/2026.09.17.752063 Share This Article: Copy Citation Tools Single-Cell Study Designs Are Systematically Underpowered for Small-Effect Genes Chris Crampton , Salman Fawad , Toby Clark , Hiru Dash , Bence Kövér , Garry Cotton , Donghoon Lee , Eugene Duff , Leonardo Bottolo , Paul M Matthews , Nathan Skene bioRxiv 2026.09.17.752063; doi: https://doi.org/10.64898/2026.09.17.752063 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Genomics Subject Areas All Articles Animal Behavior and Cognition (8013) Biochemistry (18737) Bioengineering (14888) Bioinformatics (44415) Biophysics (22597) Cancer Biology (19722) Cell Biology (26899) Clinical Trials (138) Developmental Biology (13964) Ecology (21005) Epidemiology (2067) Evolutionary Biology (25454) Genetics (16165) Genomics (23505) Immunology (18703) Microbiology (42483) Molecular Biology (18058) Neuroscience (93440) Paleontology (700) Pathology (2977) Pharmacology and Toxicology (5094) Physiology (8114) Plant Biology (15999) Scientific Communication and Education (2095) Synthetic Biology (4560) Systems Biology (10235) Zoology (2391)
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