{
  "abstract": "Single-cell experiments have improved our ability to study biological systems by profiling thousands of cells simultaneously. The data can be viewed as a point cloud in a proper experimental space, whose shape is defined by the gene regulatory network. Persistent homology has been proposed as a method for studying the intrinsic structure of data and has shown the potential to reveal novel insights. Existing approaches focus on detecting topological features or groups of cells, without a focus on explainability and interpretability. Here, we introduce an explainable topological data analysis framework for single-cell data (XplainTDA) that identifies molecular determinants underlying the topological structure of the data via a perturbation-based approach. Across omics and data sets, we show that the method is stable, reproducible, and interpretable, extending persistent homology from topological description to molecular interpretation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "I. Giray"
    },
    {
      "name": "G. Malagoli"
    },
    {
      "name": "K. Schmid"
    },
    {
      "name": "M. Colome-Tatche"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/72fbm9xagxj4w.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.14.759129",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.14.759129v1.full",
  "id": "72fbm9xagxj4w",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.14.759129v1",
  "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",
      "1703|Topological and Geometric Data Analysis"
    ]
  ],
  "openalex": "W7223067589",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.14.759129v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/72fbm9xagxj4w.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:28:43Z",
      "native_id": "10.64898/2026.10.14.759129",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.14.759129v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "XplainTDA: Explaining the topological structure of single-cell multi-omics data through perturbation",
  "title_text": "XplainTDA: Explaining the topological structure of single-cell multi-omics data through perturbation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:28:43Z",
  "url": "https://research.draconitic.com/paper/72fbm9xagxj4w",
  "venue": "bioRxiv",
  "versions": []
}