{
  "abstract": "Most single-cell studies measure either gene expression or chromatin accessibility, and connecting the two usually requires fitting a model to each study. Here we present MUTUAL (MUltiome Transfer in a Unified Aligned Latent space), which keeps the single-cell foundation models scFoundation and EpiAgent frozen, combines their embeddings with linear summaries of each modality and learns a shared RNA--ATAC space from 3.0 million paired cells of 400 public multiome datasets. Applied without refitting to ten datasets not used for training, MUTUAL matched cells' RNA and ATAC profiles more accurately than methods fitted on other donors of the same dataset, annotated ATAC-only cells more accurately and transferred transcription-factor motif activity to RNA-only cells beyond what cell-type assignment provides. In a maturation series of stem-cell-derived islets, motif activity predicted from RNA largely reproduced the measured stage-wise changes when other libraries of the series served as the reference. MUTUAL makes public multiome data directly usable by single-modality studies.",
  "abstract_shown": true,
  "affiliations": [
    "University of Michigan"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 14,
  "authors": [
    {
      "name": "X. Bao"
    },
    {
      "name": "J. Zhu"
    },
    {
      "name": "X. Song"
    },
    {
      "name": "X. Zhang"
    },
    {
      "name": "H. Zeng"
    },
    {
      "name": "H. T. H. Vu"
    },
    {
      "name": "H. Zhang"
    },
    {
      "name": "Z. Zhang"
    },
    {
      "name": "M. Yao"
    },
    {
      "name": "M. Liang"
    },
    {
      "affs": [
        0
      ],
      "name": "K. Liu"
    },
    {
      "name": "S. C. Parker"
    },
    {
      "name": "S. Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "J. Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/by42dy42s8rss.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.28.762297",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.28.762297v1.full",
  "id": "by42dy42s8rss",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.28.762297v1",
  "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": "W7225114073",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.28.762297v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/by42dy42s8rss.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:20:51Z",
      "native_id": "10.64898/2026.10.28.762297",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.28.762297v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "A single-cell multimodal foundation model enables cross modality analysis between transcriptome and chromatin",
  "title_text": "A single-cell multimodal foundation model enables cross modality analysis between transcriptome and chromatin",
  "type": "preprint",
  "updated_at": "2026-11-04T00:21:14Z",
  "url": "https://research.draconitic.com/paper/by42dy42s8rss",
  "venue": "bioRxiv",
  "versions": []
}