{
  "abstract": "Destructive sampling in single-cell sequencing obscures ancestor-descendant relationships, limiting the continuous reconstruction of cell-fate dynamics. Here we present Lineage-aware optimal transport for unbalanced single-cell dynamics (LOTUS), a framework that integrates time-resolved transcriptomic snapshots with clonal lineage information from engineered barcodes or endogenous T cell receptor clonotypes. LOTUS couples clone-level optimal transport with neural unbalanced transport to model continuous cellular state change, population growth and clonal propagation under lineage-consistent constraints. In branching simulations, LOTUS reduced lineage-inconsistent transport relative to benchmark methods, lowering off-target routing from 4.94-14.84% to 1.10%. On LARRY lineage-barcode scRNA-seq of hematopoiesis, LOTUS reconstructed branching trajectories, improved early neutrophil-monocyte fate-boundary prediction, and inferred multi-fate progenitors and fate-biased meta-clones, linking their distinct fate potentials to inferred epigenetic priming. On CellTagging scRNA-seq of fibroblast reprogramming, it distinguished reprogrammed from failed trajectories and prioritized fate-associated perturbation genes. In longitudinal scRNA-seq and TCR-seq of malaria-responsive memory (CD45RA-) CD4+ T cells, it preserved endogenous clonotype structure and reconstructed TR1 dynamics and regulatory networks. LOTUS thus converts lineage-resolved single-cell snapshots into interpretable, continuous models of fate dynamics and their regulatory programs.",
  "abstract_shown": true,
  "affiliations": [
    "Center for Quantitative Biology, Peking University, Beijing, 100871, China. Center for Machine Learning Research, Peking University. AI for Science Institute, B"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "J. Lu"
    },
    {
      "name": "H. Hou"
    },
    {
      "name": "Z. Zhang"
    },
    {
      "name": "Y. Lin"
    },
    {
      "name": "Y. Lin"
    },
    {
      "affs": [
        0
      ],
      "name": "P. Zhou"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6k5rhd0rc0zt7.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.28.762359",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.28.762359v1.full",
  "id": "6k5rhd0rc0zt7",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.28.762359v1",
  "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": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.28.762359v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6k5rhd0rc0zt7.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T00:21:33Z",
      "native_id": "10.64898/2026.10.28.762359",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.28.762359v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "LOTUS: lineage-aware optimal transport reconstructs continuous clonal fate dynamics",
  "title_text": "LOTUS: lineage-aware optimal transport reconstructs continuous clonal fate dynamics",
  "type": "preprint",
  "updated_at": "2026-11-05T00:20:56Z",
  "url": "https://research.draconitic.com/paper/6k5rhd0rc0zt7",
  "venue": "bioRxiv",
  "versions": []
}