{
  "abstract": "High-throughput spatial omics technologies enable molecular profiling within intact tissue architecture, yet identifying concise, predictive, and biologically interpretable marker panels for cell types, tissue domains, and disease-associated tissue classes remains challenging. This limitation hinders the development of actionable panels for targeted validation and downstream translation. Existing pipelines rely largely on univariate differential-expression analyses, which ignore joint molecular structure and provide limited predictive insight. Multivariate machine-learning methods, including random forest,XGBoost, elastic net, and specialized single-cell panel-selection approaches, can capture predictive patterns but typically lack explicit spatial modeling and probabilistic feature selection, relying instead on model-specific importance scores or user-specified panel sizes. We develop rapid kernel machine regression (RKMR), a scalable framework for spatial-omics marker discovery that integrates nonlinear kernel modeling, spike-and-slab variable selection, and spatial dependence. RKMR uses automatic relevance determination (ARD) kernels and sparsity-inducing priors to capture nonlinear marker-outcome relationships and implicit feature interactions while producing approximate posterior inclusion probabilities (PIPs) that quantify model-based uncertainty in feature inclusion. To scale inference to large spatial datasets, RKMR combines low-rank kernel approximations with stochastic variational optimization. In simulations, RKMR consistently achieves higher AUPRC than competing methods across a range of molecular-signal and spatial-effect settings. Across spatial transcriptomics and scRNA-seq datasets, RKMR identifies parsimonious marker sets that recover reported cell-type signatures and reproducible tissue-layer markers. These results establish RKMR as a scalable and uncertainty-aware framework for translating high-dimensional spatial omics data into robust, experimentally actionable marker panels.",
  "abstract_shown": true,
  "affiliations": [
    "Medical University of South Carolina"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "S. Seal"
    },
    {
      "name": "A. Chakraborty"
    },
    {
      "name": "C. Mattila"
    },
    {
      "name": "M. Rubinstein"
    },
    {
      "name": "P. Angel"
    },
    {
      "name": "D. Ghosh"
    },
    {
      "name": "D. Chung"
    },
    {
      "name": "B. Neelon"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fgtsk4rmkca9j.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.07.27.740999",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.07.27.740999v4.full",
  "id": "fgtsk4rmkca9j",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.07.27.740999v4",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7171852149",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.07.27.740999v4.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-27",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fgtsk4rmkca9j.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:08Z",
      "native_id": "10.64898/2026.07.27.740999",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.07.27.740999v4"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "RKMR: A Rapid Kernel Machine Regression Framework for Optimal Marker Detection in Spatial Omics Data",
  "title_text": "RKMR: A Rapid Kernel Machine Regression Framework for Optimal Marker Detection in Spatial Omics Data",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:08Z",
  "url": "https://research.draconitic.com/paper/fgtsk4rmkca9j",
  "venue": "bioRxiv",
  "versions": []
}