{
  "abstract": "Integrating single-cell transcriptomic datasets across laboratories, conditions, and technologies remains challenging because of cell type-specific batch effects and heterogeneous biological compositions. Current methods often fail to separate genuine biological variation from technical noise, misaligning distinct cell populations or leaving batch effects uncorrected. To address these challenges, we present CellDiffusion, an unsupervised graph-based model that propagates cellular features through a diffusion process to strengthen meaningful cell-cell associations in data integration. By learning a cell-cell attention network, CellDiffusion adaptively identifies corresponding populations across batches. Our approach applies cell-specific correction in data integration, generating cell embeddings that align cells across batches while preserving intrinsic cell types and developmental trajectories. We validate CellDiffusion across various datasets, including the human endometrium, one of the most dynamic and complex tissues featuring extensive cellular and molecular changes with the menstrual cycle. This framework also improves other essential analyses such as label transfer, clustering, and trajectory inference. CellDiffusion represents a powerful paradigm for accurate single-cell transcriptomic integrative analysis.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.",
    "Beijing Key Laboratory of Collaborative Innovation in Frontier Technologies for Population Quality, Beijing, China.",
    "State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China.",
    "Department of Electrical Engineering and Computer Science, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.",
    "Department of Computer Science and Faculty of Computing & Data Sciences, Boston University, Boston, MA, USA.",
    "Department of Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University Indianapolis, Indianapolis, IN, USA."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 12,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yu-Chen Liu",
      "orcid": "0000-0002-7445-7666"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Ziying Huang",
      "orcid": "0009-0004-7897-6676"
    },
    {
      "affs": [
        0
      ],
      "name": "Simon Liang Lu",
      "orcid": "0000-0001-7239-1037"
    },
    {
      "affs": [
        3
      ],
      "name": "Lei Jiang",
      "orcid": "0000-0002-6365-1965"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Zedong Lin",
      "orcid": "0009-0004-1108-418X"
    },
    {
      "affs": [
        0
      ],
      "name": "Jou-Hsuan Lee"
    },
    {
      "affs": [
        0,
        4
      ],
      "name": "Vijaya B. Kolachalama",
      "orcid": "0000-0002-5312-8644"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Rong Li",
      "orcid": "0000-0003-0305-5579"
    },
    {
      "affs": [
        3
      ],
      "name": "Dong Xu",
      "orcid": "0000-0002-4809-0514"
    },
    {
      "affs": [
        5
      ],
      "name": "Juexin Wang",
      "orcid": "0000-0002-2260-4310"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Heng Pan",
      "orcid": "0000-0002-5407-5687"
    },
    {
      "affs": [
        0
      ],
      "name": "Chao Zhang",
      "orcid": "0000-0001-8019-7998"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/73b7q2tfbkv4w.bib",
  "cited_by": 0,
  "doi": "10.1126/sciadv.aed4155",
  "fulltext_url": "https://europepmc.org/articles/PMC13674473",
  "id": "73b7q2tfbkv4w",
  "issns": [
    "2375-2548"
  ],
  "landing_url": "https://www.science.org/doi/10.1126/sciadv.aed4155",
  "language": "eng",
  "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": [
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7222567585",
  "pdf_url": "https://europepmc.org/articles/PMC13674473?pdf=render",
  "pmcid": "PMC13674473",
  "pmid": "42886809",
  "published": "2026-10-16",
  "publisher": "American Association for the Advancement of Science (AAAS)",
  "ris": "https://research.draconitic.com/paper/73b7q2tfbkv4w.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:41Z",
      "native_id": "10.1126/sciadv.aed4155",
      "source": "crossref",
      "url": "https://www.science.org/doi/10.1126/sciadv.aed4155"
    },
    {
      "first_seen": "2026-10-30T12:18:46Z",
      "native_id": "MED:42886809",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42886809"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Single-cell and spatial transcriptomics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "CellDiffusion: Enhancing scRNA-seq data integration with accurate inference of cell-cell association",
  "title_text": "CellDiffusion: Enhancing scRNA-seq data integration with accurate inference of cell-cell association",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:46Z",
  "url": "https://research.draconitic.com/paper/73b7q2tfbkv4w",
  "venue": "Science Advances",
  "versions": []
}