{
  "abstract": "Recent advances in spatial multi-omics technologies have opened new avenues for characterizing tissue architecture and function in situ, by simultaneously providing multimodal and complementary information—such as spatially resolved transcriptomic, epigenomic, and proteomic features. Current computational approaches face substantial challenges, such as effective integration of multi-omics molecular information with spatial information and corresponding high-resolution histology images. To address this challenge, we proposed SpaMOAL (Spatially Multi-Omics graph contrAstive Learning), a graph-based contrastive learning approach for spatial domain identification. SpaMOAL learns clustering-friendly representations from spatial multi-omics data by integrating spatial coordinates, histological image features, and molecular profiles, enabling accurate delineation of spatial tissue domains. Benchmarking across multiple recent paired spatial multi-omics datasets from mouse and human demonstrated that SpaMOAL consistently outperforms existing methods. By enabling accurate spatial domain delineation, SpaMOAL provides a powerful framework for interpreting tissue organization and cellular microenvironments.\n\nSpatial multi-omics technologies reveal tissue organization through diverse molecular and spatial data, yet integrating these multi-omics and histology datasets remains challenging. This study develops SpaMOAL, a graph contrastive learning framework that enables accurate spatial domain identification by integrating diverse spatial multi-omics data.",
  "abstract_shown": true,
  "affiliations": [
    "School of Software Engineering, Beijing Jiaotong University, Beijing, China",
    "Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China",
    "School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China",
    "National Infrastructure for Translational Medicine, Institute of Clinical Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China",
    "College of Science, China Agricultural University, Beijing, China"
  ],
  "article_type": "Methods and Resources",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jinxia Wang"
    },
    {
      "affs": [
        1
      ],
      "name": "Yuying Huo"
    },
    {
      "affs": [
        2
      ],
      "name": "Rui Zhao"
    },
    {
      "affs": [
        3
      ],
      "name": "Yan Pan"
    },
    {
      "affs": [
        4
      ],
      "name": "Jianqiang Wu"
    },
    {
      "name": "Han Wang"
    },
    {
      "name": "Xiangyu Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/25tms6kwzxfta.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pbio.3003690",
  "fulltext_url": "https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3003690",
  "id": "25tms6kwzxfta",
  "issns": [
    "1545-7885"
  ],
  "landing_url": "https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3003690",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ],
    [
      "plos",
      "/Biology and life sciences/Anatomy/Brain"
    ],
    [
      "plos",
      "/Biology and life sciences/Anatomy/Histology"
    ],
    [
      "plos",
      "/Biology and life sciences/Biochemistry/Nucleic acids/RNA/RNA structure"
    ],
    [
      "plos",
      "/Biology and life sciences/Cell biology/Chromosome biology/Chromatin"
    ],
    [
      "plos",
      "/Biology and life sciences/Computational biology/Genome analysis/Transcriptome analysis"
    ],
    [
      "plos",
      "/Biology and life sciences/Genetics/Epigenetics/Chromatin"
    ],
    [
      "plos",
      "/Biology and life sciences/Genetics/Gene expression/Chromatin"
    ],
    [
      "plos",
      "/Biology and life sciences/Genetics/Genomics/Genome analysis/Transcriptome analysis"
    ],
    [
      "plos",
      "/Biology and life sciences/Molecular biology/Macromolecular structure analysis/RNA structure"
    ],
    [
      "plos",
      "/Biology and life sciences/Organisms/Eukaryota/Animals/Vertebrates/Amniotes/Mammals/Rodents/Mice"
    ],
    [
      "plos",
      "/Biology and life sciences/Psychology/Behavior/Antisocial behavior"
    ],
    [
      "plos",
      "/Biology and life sciences/Zoology/Animals/Vertebrates/Amniotes/Mammals/Rodents/Mice"
    ],
    [
      "plos",
      "/Medicine and health sciences/Anatomy/Brain"
    ],
    [
      "plos",
      "/Medicine and health sciences/Anatomy/Histology"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Breast tumors/Breast cancer"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Behavior/Antisocial behavior"
    ]
  ],
  "openalex": "W7213418050",
  "pdf_url": "https://journals.plos.org/plosbiology/article/file?id=10.1371/journal.pbio.3003690&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-16",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/25tms6kwzxfta.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:49Z",
      "native_id": "10.1371/journal.pbio.3003690",
      "source": "plos",
      "url": "https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3003690"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "plos:gene expression",
      "subfield": "molecular-biology"
    },
    {
      "field": "biology",
      "origin": "plos:genetics",
      "subfield": "genetics-genomics"
    },
    {
      "field": "medicine",
      "origin": "plos:anatomy",
      "subfield": "physiology"
    }
  ],
  "title": "SpaMOAL is a deep learning method that enables accurate spatial domain identification from multi-omics data",
  "title_text": "SpaMOAL is a deep learning method that enables accurate spatial domain identification from multi-omics data",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:49Z",
  "url": "https://research.draconitic.com/paper/25tms6kwzxfta",
  "venue": "PLOS Biology",
  "versions": []
}