{
  "abstract": "Single-cell technologies increasingly enable large-scale profiling of complex biological systems across many experimental perturbations, but understanding how a system reorganizes across an entire space of conditions is difficult to interpret from single-cell embeddings alone. Phenoscapes address this by embedding entire cell-state distributions, rather than individual cells, as points in a data-driven landscape. Thus, a single dot represents one experimental condition rather than one cell, yielding a map of the perturbation or patient landscape that reveals how phenotype varies continuously across conditions. Phenoscapes remain a comparatively new and underexplored representation and existing static phenoscapes position experimental conditions relative to one another without specifying trajectories that describe how a cellular population moves from one condition to another. Flow- and trajectory-based models for single-cell data are numerous, but existing approaches operate at the level of individual cells, are not built to respect the geometry of a phenoscape, and typically cannot generalize their predicted flows to conditions not seen during training. Here we introduce GeoSinkhorn Flow, a perturbation-conditioned, geometry-aware flow-matching framework specialized designed to convert a static phenoscape into a dynamic phenoscape, in which each condition-level point is additionally connected by geodesic, condition-aware transport trajectories that are consistent with the phenoscape's underlying geometry. GeoSinkhorn Flow can also model transitions directly between time points without constructing a phenoscape. GeoSinkhorn Flow uses heat-diffusion geometry to define manifold-aware transport costs and trains a geometric autoencoder whose latent Euclidean distances stay aligned with the static phenoscape geometry while extending it to unseen cells. By explicitly conditioning the learned transport dynamics on perturbation-specific gene or drug embeddings, GeoSinkhorn Flow generalizes its trajectories to new measurement conditions, produces intermediate progression states along a trajectory, and extends predictions to unseen cells and held-out perturbations. An unbalanced optimal transport variant further learns growth or transition weights, so that changes in relative cell-state abundance or transition propensity are modeled together with state movement. Across synthetic manifolds, patient-derived organoid drug responses, Perturb-seq knockdowns, and time-resolved CyTOF signaling, GeoSinkhorn Flow recovered intrinsic manifold geometry, inferred biologically coherent response trajectories, and generalized to held-out drug combinations, perturbation genes, and time points. Together, these results establish GeoSinkhorn Flow as a perturbation-conditioned, phenoscape-consistent dynamical framework, distinct from standard flow-matching approaches, for converting static phenoscapes into dynamic ones that generalize across experimental conditions.",
  "abstract_shown": true,
  "affiliations": [
    "Yale University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "name": "K. XU"
    },
    {
      "name": "A. Wilkinson"
    },
    {
      "name": "N. Shuang"
    },
    {
      "name": "G. Huguet"
    },
    {
      "name": "M. Gerstein"
    },
    {
      "name": "G. Wolf"
    },
    {
      "name": "C. J. Tape"
    },
    {
      "affs": [
        0
      ],
      "name": "S. Krishnaswamy"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/12fsk3en274yx.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.16.759724",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.16.759724v1.full",
  "id": "12fsk3en274yx",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.16.759724v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7223200276",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.16.759724v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/12fsk3en274yx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:12Z",
      "native_id": "10.64898/2026.10.16.759724",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.16.759724v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "GeoSinkhorn Flow: Geometry-Aware Flow Matching for Perturbation-Conditioned Dynamics in Single cell Data Phenoscapes",
  "title_text": "GeoSinkhorn Flow: Geometry-Aware Flow Matching for Perturbation-Conditioned Dynamics in Single cell Data Phenoscapes",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:12Z",
  "url": "https://research.draconitic.com/paper/12fsk3en274yx",
  "venue": "bioRxiv",
  "versions": []
}