{
  "abstract": "Predicting transcriptional responses to specific perturbations is critical for understanding cellular regulatory mechanisms and accelerating drug discovery. Single-cell RNA sequencing destroys each measured cell, yielding only unpaired populations of control and perturbed cells. However, existing methods typically model perturbation prediction at the single-cell level and assume cell-to-cell correspondence, which conflicts with the unpaired nature of the observed data. To address this challenge, we propose PopPert, a framework that explicitly parameterizes population-level joint gene expression distributions for collective transcriptional state modeling. Given a control population distribution and a perturbation condition, PopPert predicts perturbation-induced changes in distribution parameters, eliminating the need for cell-level correspondence and reducing sensitivity to single-cell noise. To effectively capture gene co-expression patterns, PopPert leverages a low-rank Gaussian Copula to model cross-gene statistical dependencies and construct the joint gene expression distribution, additionally allowing sampling of synthetic perturbed single-cell profiles. Across multiple single-cell benchmarks spanning both genetic and chemical perturbations, PopPert achieves superior overall performance in differential expression recovery, perturbation effect estimation, and population-level distribution matching. These results establish population-level joint distribution learning as an effective paradigm for predicting transcriptional responses from unpaired single-cell populations. Code for PopPert is publicly available at https://github.com/whd1125/PopPert.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.01357",
  "author_total": 4,
  "authors": [
    {
      "name": "Handong Wang"
    },
    {
      "name": "Jiaxin Qi"
    },
    {
      "name": "Haochen Feng"
    },
    {
      "name": "Baisheng Lai"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6gzk4jc2w8wk3.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.01357",
  "fulltext_url": null,
  "id": "6gzk4jc2w8wk3",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.01357",
  "language": null,
  "licence": "CC-BY-NC-SA-4.0",
  "licence_label": "CC BY-NC-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "q-bio.GN"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7206179593",
  "pdf_url": "https://arxiv.org/pdf/2609.01357",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6gzk4jc2w8wk3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:05Z",
      "native_id": "2609.01357",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.01357"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.GN",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "PopPert: Population-level Joint-Distribution Modeling for Single-Cell Perturbation Prediction",
  "title_text": "PopPert: Population-level Joint-Distribution Modeling for Single-Cell Perturbation Prediction",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:05Z",
  "url": "https://research.draconitic.com/paper/6gzk4jc2w8wk3",
  "venue": "arXiv",
  "versions": []
}