{
  "abstract": "Spatial transcriptomics (ST) measures gene expression while preserving spatial context within tissues, enabling detailed characterization of tissue organization. As ST technologies advance, aligning datasets across tissue sections, individuals, platforms, and developmental stages has become increasingly important but remains challenging due to sparse expression, biological heterogeneity, and geometric distortions between slices. We introduce OT-knn, a method for ST alignment that integrates local neighborhood information within an optimal transport framework. Rather than relying solely on single-spot expression, OT-knn reconstructs each spot using its spatial k-nearest neighbors, capturing microenvironment context that is more robust to noise and variability. These representations are then used to derive probabilistic correspondences between slices. We evaluate OT-knn using simulated data with known ground-truth alignment and real datasets from multiple ST platforms, including human dorsolateral prefrontal cortex data (10x Genomics Visium), mouse brain aging data with both within-donor and cross-donor comparisons (MERFISH), a multi-stage axolotl brain dataset (Stereo-seq), and a cross-platform analysis using mouse embryo datasets profiled by Stereo-seq and seqFISH. Across these settings, OT-knn achieves accurate and robust alignment, particularly in the presence of spatial deformation, donor heterogeneity, developmental variation and technological differences. Author summary: Understanding how cells are arranged within tissues is important for studying how organs develop, age, and respond to disease. Spatial transcriptomics is a new technology that measures gene activity while keeping track of where each measurement comes from in the tissue. However, comparing data from different tissue slices, different individuals, or different time points is difficult. Tissues can change shape during preparation, gene measurements can be noisy, and natural biological differences exist across samples.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Statistics, The Pennsylvania State University, University Park, Pennsylvania, United States of America"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jia Song"
    },
    {
      "name": "Qunhua Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bhafav3tqstbx.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pcbi.1014041",
  "fulltext_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014041",
  "id": "bhafav3tqstbx",
  "issns": [
    "1553-7358"
  ],
  "landing_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014041",
  "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/Cerebrum"
    ],
    [
      "plos",
      "/Biology and life sciences/Anatomy/Brain/Prefrontal cortex"
    ],
    [
      "plos",
      "/Biology and life sciences/Biochemistry/Metabolism/Biological transport"
    ],
    [
      "plos",
      "/Biology and life sciences/Computational biology/Genome analysis/Transcriptome analysis"
    ],
    [
      "plos",
      "/Biology and life sciences/Genetics/Gene expression"
    ],
    [
      "plos",
      "/Biology and life sciences/Genetics/Genomics/Genome analysis/Transcriptome analysis"
    ],
    [
      "plos",
      "/Biology and life sciences/Molecular biology/Molecular biology techniques/Marker genes"
    ],
    [
      "plos",
      "/Medicine and health sciences/Anatomy/Brain/Cerebrum"
    ],
    [
      "plos",
      "/Medicine and health sciences/Anatomy/Brain/Prefrontal cortex"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Classical mechanics/Damage mechanics/Deformation"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Classical mechanics/Deformation"
    ],
    [
      "plos",
      "/Research and analysis methods/Database and informatics methods/Bioinformatics/Sequence analysis/Sequence alignment"
    ],
    [
      "plos",
      "/Research and analysis methods/Molecular biology techniques/Marker genes"
    ]
  ],
  "openalex": "W7214013740",
  "pdf_url": "https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1014041&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-22",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/bhafav3tqstbx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:43Z",
      "native_id": "10.1371/journal.pcbi.1014041",
      "source": "plos",
      "url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014041"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "plos:gene expression",
      "subfield": "molecular-biology"
    },
    {
      "field": "biology",
      "origin": "plos:computational biology",
      "subfield": "bioinformatics"
    },
    {
      "field": "medicine",
      "origin": "plos:anatomy",
      "subfield": "physiology"
    }
  ],
  "title": "OT-knn: A neighborhood-aware optimal transport framework for aligning spatial transcriptomics data",
  "title_text": "OT-knn: A neighborhood-aware optimal transport framework for aligning spatial transcriptomics data",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:43Z",
  "url": "https://research.draconitic.com/paper/bhafav3tqstbx",
  "venue": "PLOS Computational Biology",
  "versions": []
}