{
  "abstract": "Multi-subject spatial transcriptomics is increasingly used to identify molecular differences across individuals and conditions while resolving where in tissue those differences occur. Yet subject-level differential expression (DE) analyses commonly require spatial regions to be specified in advance, separating localization from testing. Here we present SpaceDX, a framework that uses gene-specific attention-based spatial weighting to jointly test differential expression and localize the spatial expression patterns contributing to these associations. In simulations, SpaceDX outperforms cluster-based DE methods when differential expression does not align with predefined spatial boundaries. Across two multi-subject mouse brain datasets, SpaceDX detected 45.8% more DE genes than regional DESeq2 pseudobulk in a chronic stress model, while in an Alzheimer's disease model combining SpaceDX with DESeq2 increased discovery by 19.1% relative to DESeq2 alone. Across both datasets, the learned maps provided spatial context for condition-associated expression signals in addition to gene-level significance. In the chronic stress model, for example, oligodendrocyte- and myelin-associated genes showed shared localization to corresponding brain structures. Together, these results show that the spatial context of differential expression can be inferred jointly with gene-level associations, recovering signals obscured by regional aggregation without requiring the relevant tissue context to be defined in advance.",
  "abstract_shown": true,
  "affiliations": [
    "Helmholtz Munich"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "N. Stotzem"
    },
    {
      "name": "S. Chang"
    },
    {
      "name": "N. Cai"
    },
    {
      "affs": [
        0
      ],
      "name": "F. P. Casale"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8cvdz65xry0xj.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.28.758685",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.28.758685v1.full",
  "id": "8cvdz65xry0xj",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.28.758685v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.28.758685v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8cvdz65xry0xj.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:26Z",
      "native_id": "10.64898/2026.10.28.758685",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.28.758685v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Joint localization and testing reveals spatial context of differential expression",
  "title_text": "Joint localization and testing reveals spatial context of differential expression",
  "type": "preprint",
  "updated_at": "2026-10-31T00:20:46Z",
  "url": "https://research.draconitic.com/paper/8cvdz65xry0xj",
  "venue": "bioRxiv",
  "versions": []
}