{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "University of Bordeaux, Inserm Bordeaux Population Health, Inria SISTM, Vaccine Research Institute, Institut de Mathématiques de Toulouse"
  ],
  "article_type": "Confirmatory results",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "S. Fallet"
    },
    {
      "name": "V. Godot"
    },
    {
      "name": "P. Neuvial"
    },
    {
      "name": "B. P. Hejblum"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0tmn4jfscwykw.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.13.758831",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.13.758831v1.full",
  "id": "0tmn4jfscwykw",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.13.758831v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "genomics"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7221820623",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.13.758831v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-13",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0tmn4jfscwykw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:47Z",
      "native_id": "10.64898/2026.10.13.758831",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.13.758831v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:genomics",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "cit_gsa: gene set analysis for scRNA-seq with covariate adjustment",
  "title_text": "cit_gsa: gene set analysis for scRNA-seq with covariate adjustment",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:47Z",
  "url": "https://research.draconitic.com/paper/0tmn4jfscwykw",
  "venue": "bioRxiv",
  "versions": []
}