{
  "abstract": "Recent foundation models for single-cell transcriptomic data generate informative, context-aware gene and cell representations. Spatial transcriptomic (ST) data offer extra positional insights but were not considered by these single-cell models. We introduce stFormer, a transformer model tailored for ST data, which employs the cross-attention module to incorporate spatial ligand genes. To unify different ST technologies with a trade-off between resolution and gene coverage, we propose a biased cross-attention method that enables the model to do learning with single-cell resolution on whole-transcriptome but low-resolution Visium data. We assembled a pretraining corpus comprising ∼4.1 million spatial samples from public human Visium datasets. After pretraining, stFormer improved upon the state-of-the-art single-cell foundation model, scFoundation, across diverse tasks, including cell clustering, batch effect correction, cell type prediction, and gene function prediction. stFormer also revealed intercellular ligand-receptor signaling responses via in silico perturbation. Finally, we demonstrated stFormer's utility in yielding biological findings on two case studies.",
  "abstract_shown": true,
  "affiliations": [
    "Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China",
    "State Key Laboratory of Biopharmaceutical Preparation and Delivery, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China",
    "Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China; State Key Laboratory of Biopharmaceutical Preparation and Delivery, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China. Electronic address: yyuan@ipe.ac.cn"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "Shenghao Cao",
      "orcid": "0000-0002-2203-1460"
    },
    {
      "affs": [
        0
      ],
      "name": "Kaiyuan Yang"
    },
    {
      "affs": [
        0
      ],
      "name": "Jiabei Cheng",
      "orcid": "0009-0008-7601-4800"
    },
    {
      "affs": [
        1
      ],
      "name": "Jiachen Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Hong-Bin Shen"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiaoyong Pan"
    },
    {
      "affs": [
        2
      ],
      "name": "Ye Yuan",
      "orcid": "0000-0002-6967-6856"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bytpv9aernby8.bib",
  "cited_by": 10,
  "doi": "10.1016/j.crmeth.2026.101612",
  "fulltext_url": null,
  "id": "bytpv9aernby8",
  "issns": [
    "2667-2375"
  ],
  "landing_url": "https://doi.org/10.1016/j.crmeth.2026.101612",
  "language": "en",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7214919201",
  "pdf_url": null,
  "pmcid": null,
  "pmid": "42815472",
  "published": "2026-09-01",
  "publisher": "Elsevier BV",
  "ris": "https://research.draconitic.com/paper/bytpv9aernby8.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T13:20:49Z",
      "native_id": "W7214919201",
      "source": "openalex",
      "url": "https://doi.org/10.1016/j.crmeth.2026.101612"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Single-cell and spatial transcriptomics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "stFormer integrates spatial ligand signaling into a foundation model for spatial transcriptomics",
  "title_text": "stFormer integrates spatial ligand signaling into a foundation model for spatial transcriptomics",
  "type": "peer-reviewed",
  "updated_at": "2026-11-07T13:20:49Z",
  "url": "https://research.draconitic.com/paper/bytpv9aernby8",
  "venue": "Cell Reports Methods",
  "versions": []
}