{
  "abstract": "Single-cell perturbation screens increasingly infer mechanism from the direction of a treatment's displacement in a shared transcriptomic embedding, yet no sample-size rule states how many cells that direction needs. We derive a closed-form, anisotropic cell quota for the treated cells required to hold the angular error of an estimated perturbation direction at a chosen tolerance. Because noise aligned with the effect changes only its length, the quota depends on the noise perpendicular to the effect, on the effect magnitude and on the control-pool size, and it diverges below a magnitude floor set by the control alone. The deterministic core is machine-checked in Lean 4. Across four public atlases (six screens) reprocessed through one embedding, the per-cell noise variance is near 1.0 on chemical and genome-wide genetic platforms. On Tahoe-100M, referenced to its plate-shared DMSO vehicle of about 3,100 cells, 55% of 56,827 conditions are control-pool-limited and only 11% are over-sampled at a 5.7-degree tolerance. Genome-wide CRISPRi knockdowns are instead detection- and depth-limited. A downsample-and-measure test on every atlas recovers the parameter-free angular-error slope. The calculator turns a pilot covariance, an effect magnitude and a control design into a per-condition cell budget and flags conditions that no treated depth can resolve.",
  "abstract_shown": true,
  "affiliations": [
    "Hong Hung Hospital"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "L. T. H. Tran"
    },
    {
      "name": "V. T. Nguyen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/95mkw44xh5jst.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.02.754788",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.02.754788v1.full",
  "id": "95mkw44xh5jst",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.02.754788v1",
  "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",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7220476137",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.02.754788v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/95mkw44xh5jst.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:07Z",
      "native_id": "10.64898/2026.10.02.754788",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.02.754788v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "How many cells resolve a perturbation direction? A closed-form, control-aware cell quota for single-cell perturbation screens",
  "title_text": "How many cells resolve a perturbation direction? A closed-form, control-aware cell quota for single-cell perturbation screens",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:07Z",
  "url": "https://research.draconitic.com/paper/95mkw44xh5jst",
  "venue": "bioRxiv",
  "versions": []
}