{
  "abstract": "Spatial technologies that jointly profile RNA and additional molecular layers provide a comprehensive view of tissue organization, yet their limited resolution results in measurements that reflect mixtures of multiple cell types. While RNA-based deconvolution methods are well established, incorporating co-profiled non-transcriptomic modalities without compromising cell-type identifiability remains an open challenge, as reliable references are largely restricted to transcriptomic data. Here we present SMODER, an RNA-anchored framework for spatial multi-omics deconvolution that treats transcriptomes as the primary determinant of cell identity while incorporating complementary molecular modalities in a constrained manner. By adopting an asymmetric integration strategy, SMODER preserves RNA-driven cell-type resolution and enables robust estimation of cell-type compositions across spatial locations. Across simulations and diverse spatial multi-omics datasets spanning protein, chromatin accessibility and histone modification assays, SMODER consistently outperforms existing methods in both accuracy and spatial coherence. Beyond deconvolution, SMODER denoises multi-omic signals and enables cell-type-aware analysis of regulatory programs. Application to spatial CUT\\&Tag--RNA-seq and {RNA--ATAC-seq} data reveals spatially resolved epigenetic repression and cis-regulatory relationships linked to gene expression.",
  "abstract_shown": true,
  "affiliations": [
    "School of Mathematics, Sichuan University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "name": "Y. Bai"
    },
    {
      "name": "X. Guan"
    },
    {
      "name": "S. Xiong"
    },
    {
      "name": "X. Guo"
    },
    {
      "name": "X. Wang"
    },
    {
      "name": "S. Yi"
    },
    {
      "name": "W. Ju"
    },
    {
      "name": "S. Ma"
    },
    {
      "name": "D. Zhou"
    },
    {
      "name": "Q. Dong"
    },
    {
      "affs": [
        0
      ],
      "name": "w. liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5xhxa4d2yap9x.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.09.757940",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.09.757940v1.full",
  "id": "5xhxa4d2yap9x",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.09.757940v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7221949053",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.09.757940v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5xhxa4d2yap9x.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:28:00Z",
      "native_id": "10.64898/2026.10.09.757940",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.09.757940v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Deciphering Spatial Cellular Organization through RNA-Anchored Multi-Omics Deconvolution",
  "title_text": "Deciphering Spatial Cellular Organization through RNA-Anchored Multi-Omics Deconvolution",
  "type": "preprint",
  "updated_at": "2026-10-30T12:28:00Z",
  "url": "https://research.draconitic.com/paper/5xhxa4d2yap9x",
  "venue": "bioRxiv",
  "versions": []
}