{
  "abstract": "Perturbation screens hold the potential to systematically map regulatory processes at single-cell resolution, yet modeling and predicting transcriptome-wide responses to perturbations remains a major computational challenge. Existing methods often underperform simple baselines, fail to disentangle measurement noise from biological signal, and provide limited insight into the causal structure governing cellular responses. Here, we present the latent causal diffusion (LCD), a generative model that frames single-cell gene expression as a stationary diffusion process observed under measurement noise. LCD outperforms established approaches in predicting the distributional shifts of unseen perturbation combinations in single-cell RNA-sequencing screens while simultaneously learning a mechanistic dynamical system of gene regulation. To interpret these learned dynamics, we develop an approach we call causal linearization via perturbation responses (CLIPR), which yields an approximation of the direct causal effects between all genes modeled by the diffusion. CLIPR provably identifies causal effects under a linear drift assumption and recovers causal structure in both simulated systems and a genome-wide perturbation screen, where it clusters genes into coherent functional modules and resolves causal relationships that standard differential expression analysis cannot. The LCD-CLIPR framework bridges generative modeling with causal inference to predict unseen perturbation effects and map the underlying regulatory mechanisms of the transcriptome.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Science, Eidgenössische Technische Hochschule (ETH) Zürich, Zürich 8092, Switzerland.",
    "Laboratory for Information and Decision Systems, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139.",
    "Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA 02142.",
    "School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland.",
    "School of Life Sciences, Swiss Institute for Experimental Cancer Research, EPFL, Lausanne 1015, Switzerland.",
    "Max Planck Institute for Intelligent Systems, Tübingen 72076, Germany.",
    "European Laboratory for Learning and Intelligent Systems (ELLIS) Institute, Tübingen 72076, Germany."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Lars Lorch",
      "orcid": "0000-0001-7465-5892"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Jiaqi Zhang",
      "orcid": "0000-0001-9039-6843"
    },
    {
      "affs": [
        3,
        4
      ],
      "name": "Charlotte Bunne",
      "orcid": "0000-0003-1431-103X"
    },
    {
      "affs": [
        0
      ],
      "name": "Andreas Krause",
      "orcid": "0000-0001-7260-9673"
    },
    {
      "affs": [
        0,
        5,
        6
      ],
      "name": "Bernhard Schölkopf",
      "orcid": "0000-0002-8177-0925"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Caroline Uhler",
      "orcid": "0000-0002-7008-0216"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9pfqe8wvh3w59.bib",
  "cited_by": 0,
  "doi": "10.1073/pnas.2602630123",
  "fulltext_url": "https://europepmc.org/articles/PMC13663777",
  "id": "9pfqe8wvh3w59",
  "issns": [
    "0027-8424",
    "1091-6490"
  ],
  "landing_url": "https://europepmc.org/article/MED/42848419",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7125477613",
  "pdf_url": "https://europepmc.org/articles/PMC13663777?pdf=render",
  "pmcid": "PMC13663777",
  "pmid": "42848419",
  "published": "2026-10-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9pfqe8wvh3w59.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:02Z",
      "native_id": "MED:42848419",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42848419"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Single-cell and spatial transcriptomics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Latent causal diffusions for single-cell perturbation modeling.",
  "title_text": "Latent causal diffusions for single-cell perturbation modeling.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:02Z",
  "url": "https://research.draconitic.com/paper/9pfqe8wvh3w59",
  "venue": "Proceedings of the National Academy of Sciences of the United States of America",
  "versions": []
}