{
  "abstract": "Multiparameter flow cytometry generates high-dimensional, unordered single-cell mea- surement data for disease diagnosis and monitoring, yet analysis often remains dependent on manual gating, limiting scalability and reproducibility. Existing machine-learning ap- proaches can reduce annotation burden but frequently require large training cohorts and offer limited interpretability. To address these challenges, we introduce FlowLOT , an optimal-transport-based framework that models the single-cell measurement data of a pa- tient sample as an empirical cellular distribution and maps it directly into a fixed-length feature vector. Within a single transparent architecture, FlowLOT unifies high-dimensional classification, interpretable visualization, and continuous quantitative inference. In few-shot regimes, using as few as 16 patients per class on FlowCAP-II and 8 patients per class on BLAST110, it accurately distinguishes healthy from acute myeloid leukemia (AML) sam- ples, reaching 94.3% and 98.0% balanced accuracy, respectively. The underlying embedding exposes marker-level variation driving disease-associated population shifts and enables quantitative measurable residual disease (MRD) estimation, achieving a Pearson correlation of 0.82 on held-out samples and 0.79 under cross-dataset transfer. Furthermore, at the clinically relevant 0.1% threshold for leukemia-associated immunophenotype (LAIP) residual disease, FlowLOT detects positivity with 72% sensitivity at 100% specificity. By replacing subjective manual gating and black-box deep learning with a distribution-aware framework, FlowLOT offers a sample-efficient, scalable, and interpretable solution for high- dimensional cytometry under realistic clinical and experimental constraints.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.17906",
  "author_total": 6,
  "authors": [
    {
      "name": "Naqib Sad Pathan"
    },
    {
      "name": "Mohammad Shifat-E-Rabbi"
    },
    {
      "name": "Kristofor E. Pas"
    },
    {
      "name": "Ivan Medri"
    },
    {
      "name": "Bartek Rajwa"
    },
    {
      "name": "Gustavo K. Rohde"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a7xkczq1cpthq.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.17906",
  "fulltext_url": null,
  "id": "a7xkczq1cpthq",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.17906",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "q-bio.QM"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.17906",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/a7xkczq1cpthq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:05Z",
      "native_id": "2609.17906",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.17906"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "FlowLOT: Linearized Optimal Transport for Flow Cytometry Analysis",
  "title_text": "FlowLOT: Linearized Optimal Transport for Flow Cytometry Analysis",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:05Z",
  "url": "https://research.draconitic.com/paper/a7xkczq1cpthq",
  "venue": "arXiv",
  "versions": []
}