---
title: "cit_gsa: one-step gene set analysis for single-cell RNA-seq with covariate adjustment"
id: "biorxiv-5-cit-gsa-gene-set-analysis-for-scrna-seq-with-covariate-adjustment"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-5-cit-gsa-gene-set-analysis-for-scrna-seq-with-covariate-adjustment"
content_type: "clinical_feed_article"
specialty: "General"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.10.13.758831v1?rss=1"
published_at: "2026-10-14T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# cit_gsa: one-step gene set analysis for single-cell RNA-seq with covariate adjustment
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-5-cit-gsa-gene-set-analysis-for-scrna-seq-with-covariate-adjustment
- **Specialty:** [General](https://medichelpline.com/clinical-feed/general.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.10.13.758831v1?rss=1)
- **Published At:** 2026-10-14T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The authors introduce **cit_gsa**, a new one-step, self-contained method for **single-cell RNA-seq** gene set differential expression analysis that can accommodate more than two conditions and adjust for covariates. - Existing single-cell DEA approaches typically perform a gene-level test followed by an enrichment step; those approaches often restrict comparisons to two groups and do not permit covariate adjustment, the team noted. - The paper reports benchmark comparisons in which investigators observed improved performance of **cit_gsa** versus state-of-the-art methods in complex simulated scenarios; the source described superior accuracy and robustness but did not provide numeric performance metrics in the abstract. - Real-data analyses were presented; data indicated that including covariate adjustment altered biological interpretation and yielded more interpretable gene-set associations in applied datasets. - The study authors declared no competing interests and listed funding sources; the preprint is available under a CC-BY 4.0 license. - The source did not report certain implementation details in the abstract, including exact model formulas, software runtime benchmarks, or command-line usage; readers must consult the full text or supplementary materials for those specifics.
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Sara Fallet 1 University of Bordeaux, Inserm Bordeaux Population Health, Inria SISTM, Vaccine Research Institute, Institut de Mathématiques de Toulouse; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Sara%2BFallet%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Fallet%20S&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ASara%2BFallet%2B) * For correspondence: sara.fallet@u-bordeaux.fr Véronique Godot 2 Vaccine Research Institute, INSERM-U955, team Accelerates, Faculté de Santé, Université Paris-Est, IHU SEPSIS - PROMETHEUS Comprehensive Sepsis Center; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=V%C3%A9ronique%2BGodot%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Godot%20V&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AV%25C3%25A9ronique%2BGodot%2B) Pierre Neuvial 3 Institut de Mathématiques de Toulouse, CNRS; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Pierre%2BNeuvial%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Neuvial%20P&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3APierre%2BNeuvial%2B) Boris P Hejblum 4 University of Bordeaux, Inserm Bordeaux Population Health, Inria SISTM, Vaccine Research Institute * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Boris%2BP%2BHejblum%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Hejblum%20BP&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ABoris%2BP%2BHejblum%2B) * [ORCID record for Boris P Hejblum](http://orcid.org/0000-0003-0646-452X "Open in new tab") * [Abstract](https://www.biorxiv.org/content/10.64898/2026.10.13.758831v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5867460/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.10.13.758831v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5867460/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.10.13.758831v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5867460/1) * [Supplementary material](https://www.biorxiv.org/content/10.64898/2026.10.13.758831v1.supplementary-material)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_data/node:5867460/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.10.13.758831v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5867460/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## Abstract Single-cell RNA-seq enables gene expression analysis at single-cell resolution. Differential Expression Analysis (DEA) performed at the gene set level for better power and interpretation, identifies gene sets associated with a factor of interest. Existing single-cell DEA methods rely on two-step approaches, a gene-level analysis followed by an enrichment step, are often limited to two-group comparisons and do not support covariate adjustment. We introduce cit_gsa, a novel one-step self-contained gene set DEA method for Single-cell RNA-seq that handles more than two conditions and allows covariate adjustment. A benchmark of cit_gsa against state-of-the-art methods shows that our method has better performance in complex scenarios, and real-data analysis confirms that covariate adjustment improves biological interpretation. ### Competing Interest Statement The authors have declared no competing interest. ## Funder Information Declared Agence Nationale de la Recherche, ANR-22-PESN-0002 University of Bordeaux's Digital Public Health Graduate School, 17‐EURE‐0019 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 4.0 International license](http://creativecommons.org/licenses/by/4.0/). bioRxiv and medRxiv thank the following for their generous financial support: > The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, The University of Edinburgh, University of Washington, and Vrije Universiteit Amsterdam. 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[ Download PDF](https://www.biorxiv.org/content/10.64898/2026.10.13.758831v1.full.pdf) Print/Save Options [Download PDF](https://www.biorxiv.org/content/biorxiv/early/2026/10/14/2026.10.13.758831.full.pdf)Full Text & In-line FiguresXML [More Info](https://www.biorxiv.org/about/FAQ#PrintOptions "More Information on Print/Save Options") [Supplementary Material ](https://www.biorxiv.org/content/10.64898/2026.10.13.758831v1.supplementary-material) [ Email](https://www.biorxiv.org/ "Email this Article") [ Share](https://www.biorxiv.org/) cit_gsa: gene set analysis for scRNA-seq with covariate adjustment Sara Fallet, Véronique Godot, Pierre Neuvial, Boris P Hejblum bioRxiv 2026.10.13.758831; doi: https://doi.org/10.64898/2026.10.13.758831 This article is a preprint and has not been certified by peer review [[what does this mean?](https://www.biorxiv.org/about/FAQ#unrefereed)]. 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