---
title: "celltypeEnrich: consensus-based scRNA-seq cluster annotation tool"
id: "biorxiv-15-celltypeenrich-a-consensus-based-scrna-seq-cluster-annotation-tool"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-15-celltypeenrich-a-consensus-based-scrna-seq-cluster-annotation-tool"
content_type: "clinical_feed_article"
specialty: "General"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.09.15.751735v1?rss=1"
published_at: "2026-09-21T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# celltypeEnrich: consensus-based scRNA-seq cluster annotation tool
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-15-celltypeenrich-a-consensus-based-scrna-seq-cluster-annotation-tool
- **Specialty:** [General](https://medichelpline.com/clinical-feed/general.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.09.15.751735v1?rss=1)
- **Published At:** 2026-09-21T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The preprint introduces **celltypeEnrich**, a cluster-level annotation tool for single-cell RNA sequencing (**scRNA-seq**) that generates consensus cell-type labels from enrichment results across multiple reference databases. - Annotation is based on a **hypergeometric test** performed on input gene lists to assess enrichment of cell-type–specific genes from reference datasets. - The tool aggregates enrichment results from up to 26 reference datasets to determine a consensus annotation for each cluster. - Benchmarking used scRNA-seq datasets from three tissues spanning two species; reported annotation accuracy for celltypeEnrich ranged from **62–72%**, generally outperforming other evaluated tools. - Compared tools suffered from lower accuracy, incomplete tissue coverage, or required parameter optimization; celltypeEnrich was presented as faster to use and more reproducible. - Performance remained stable when input gene lists were down-sampled to **25%** of their original size, indicating robustness to sparser marker lists. - Implementation: celltypeEnrich is available as an **R Shiny** web application under the MIT license for non-profit academic use at https://celltypeenrich.gdcb.iastate.edu and source code at https://github.com/Tuteja-Lab/celltypeEnrich. - The authors declare no competing interests and acknowledge funding from Eunice Kennedy Shriver National Institute of Child Health and Human Development (grant numbers reported in the source). - The preprint was posted on bioRxiv on September 21, 2026. Details of datasets, parameter settings, and full benchmarking procedures are referenced in the preprint and supplementary material available with the source.
## Clinical Analysis & Structured Key Points
celltypeEnrich: a consensus-based scRNA-seq cluster annotation tool | bioRxiv Skip to main content New Results celltypeEnrich: a consensus-based scRNA-seq cluster annotation tool Sabrena Rutledge , Geetu Tuteja doi: https://doi.org/10.64898/2026.09.15.751735 Sabrena Rutledge Iowa State University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Geetu Tuteja Iowa State University Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: geetu{at}iastate.edu Abstract Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Motivation Single-cell RNA sequencing (scRNA-seq) cluster annotation is a critical step in data analysis. Current methods are time-consuming, difficult to reproduce, or limited in tissue or species coverage. Results We developed celltypeEnrich, a cluster-level annotation tool that uses a hypergeometric test to identify enrichment of cell-type-specific genes from input gene lists. Enrichment results from up to 26 reference datasets are used to determine a consensus annotation. Benchmarking using scRNA-seq datasets from three tissues spanning two species showed 62-72% annotation accuracy for celltypeEnrich, generally outperforming other tools, which had either lower accuracy, incomplete tissue coverage, or the need for parameter optimization. The performance of celltypeEnrich remained stable when input gene lists were down-sampled to 25% of their original size. Availability and Implementation celltypeEnrich is freely available at (https://celltypeenrich.gdcb.iastate.edu) as an R Shiny web application under the MIT license for non-profit academic use. Competing Interest Statement The authors have declared no competing interest. Footnotes https://celltypeenrich.gdcb.iastate.edu https://github.com/Tuteja-Lab/celltypeEnrich Funder Information Declared Eunice Kennedy Shriver National Institute of Child Health and Human Development , R01HD112559 , R01HD105734 , R01HD094937 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-ND 4.0 International license . Back to top Previous Next Posted September 21, 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 celltypeEnrich: a consensus-based scRNA-seq cluster annotation tool 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 celltypeEnrich: a consensus-based scRNA-seq cluster annotation tool Sabrena Rutledge , Geetu Tuteja bioRxiv 2026.09.15.751735; doi: https://doi.org/10.64898/2026.09.15.751735 Share This Article: Copy Citation Tools celltypeEnrich: a consensus-based scRNA-seq cluster annotation tool Sabrena Rutledge , Geetu Tuteja bioRxiv 2026.09.15.751735; doi: https://doi.org/10.64898/2026.09.15.751735 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 Areas All Articles Animal Behavior and Cognition (8014) Biochemistry (18743) Bioengineering (14890) Bioinformatics (44443) Biophysics (22609) Cancer Biology (19728) Cell Biology (26906) Clinical Trials (138) Developmental Biology (13969) Ecology (21008) Epidemiology (2067) Evolutionary Biology (25457) Genetics (16169) Genomics (23512) Immunology (18714) Microbiology (42520) Molecular Biology (18063) Neuroscience (93474) Paleontology (700) Pathology (2981) Pharmacology and Toxicology (5098) Physiology (8115) Plant Biology (16000) Scientific Communication and Education (2095) Synthetic Biology (4560) Systems Biology (10236) Zoology (2391)
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