{
  "abstract": "Understanding disease-associated pathway coordination requires distinguishing phenotype-related co-expression changes from differences across cellular contexts while accounting for cells sampled from the same subject. Bulk gene-set co-expression methods do not directly address the sparse counts, single-cell distributional noise, subject-level dependence, and cell-type heterogeneity of single-cell RNA sequencing (scRNA-seq) data. We developed single-cell gene-set co-expression analysis (scGSCA), a framework for pathway-level differential co-expression analysis in scRNA-seq data. scGSCA fits gene-wise zero-inflated negative binomial mixed models with subject-level random effects and computes gene-set co-expression statistics from quantile residual correlations. It provides an overall differential gene-set co-expression analysis and an interaction test to assess whether the co-expression pattern between two cell types varies by sample phenotype. We applied scGSCA to tumor-normal gastric, breast-subtype, and lung-skin comparisons with matched bulk and scRNA-seq data. The overall test identified signals not captured in bulk analysis, including Axon guidance in gastric cancer and estrogen-response, IL6-JAK-STAT3, and TNF/NF-{kappa}B pathways in breast cancer. The interaction test further identified co-expression contrasts across cell types. scGSCA extends gene-set co-expression analysis to single-cell data while retaining subject-level structure and cell-type-specific context. scGSCA is freely available as an open-source R package.",
  "abstract_shown": true,
  "affiliations": [
    "University of Miami"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Z. Jin"
    },
    {
      "name": "R. Irlmeier"
    },
    {
      "name": "J. Ping"
    },
    {
      "name": "Y. Wang"
    },
    {
      "name": "Y. Guo"
    },
    {
      "affs": [
        0
      ],
      "name": "F. Ye"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1157bytk4csd7.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.05.756579",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.05.756579v1.full",
  "id": "1157bytk4csd7",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.05.756579v1",
  "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",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7221714220",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.05.756579v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1157bytk4csd7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:26Z",
      "native_id": "10.64898/2026.10.05.756579",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.05.756579v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "scGSCA: gene-set differential co-expression analysis for single-cell RNA sequencing data",
  "title_text": "scGSCA: gene-set differential co-expression analysis for single-cell RNA sequencing data",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:26Z",
  "url": "https://research.draconitic.com/paper/1157bytk4csd7",
  "venue": "bioRxiv",
  "versions": []
}