Evidence map›Paper›PMID 42239142›Full record

ArticlebioRxiv : the preprint server for biology2026

PerturbPlan: An analytical framework for designing Perturb-seq experiments.

Ziang Niu, Yihui He, James Galante, Andreas R Gschwind, Judhajeet Ray, Lars M Steinmetz, Jesse M Engreitz, Eugene Katsevich

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Ziang NiuDepartment of Statistics and Data Science, Wharton School, University of Pennsylvania, Philadelphia, USA.
Yihui HeDepartment of Statistics and Data Science, Wharton School, University of Pennsylvania, Philadelphia, USA.
James GalanteDepartment of Genetics, Stanford University School of Medicine, Stanford, California, USA.
Andreas R GschwindDepartment of Genetics, Stanford University School of Medicine, Stanford, California, USA.
Judhajeet RayThe Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Lars M SteinmetzDepartment of Genetics, Stanford University School of Medicine, Stanford, California, USA.
Jesse M EngreitzDepartment of Genetics, Stanford University School of Medicine, Stanford, California, USA.
Eugene KatsevichDepartment of Statistics and Data Science, Wharton School, University of Pennsylvania, Philadelphia, USA.

Funding

Stanford Center for Connecting DNA Variants to Function and PhenotypeUM1HG011972 · NHGRI · STANFORD UNIVERSITY · PI JESSE M ENGREITZ, THOMAS QUERTERMOUS · 2021 to 2026
$10.5M
Scientific Core: Perturb-seq library generation, sequencing, and data analysisP01HL180323 · NHLBI · STANFORD UNIVERSITY · PI Marlene Rabinovitch · 2025 to 2026
$7.1M
Function-based exploration of genetic variation at genome-scaleR01HG011664 · NHGRI · STANFORD UNIVERSITY · PI STEINMETZ, LARS M · 2022 to 2025
$2.9M
MorPhiC: Constructing a Catalog of Cellular Programs to Identify and Annotate Human Disease GenesU01HG013176 · NHGRI · STANFORD UNIVERSITY · PI JESSE M ENGREITZ, Anshul Kundaje · 2023 to 2026
$1.9M
NHGRI NIH HHS R01 HG011664NHGRI NIH HHS U01 HG013176NHGRI NIH HHS UM1 HG011972NHLBI NIH HHS P01 HL180323
6 · The paper itself

Abstract

CRISPR screens with single-cell RNA-seq readouts provide a powerful tool for characterizing the functions of noncoding elements and genes. However, designing these experiments to balance statistical power and cost is challenging, given the large number of design parameters. The only available tool for this purpose is a simulation-based power calculator, but it is computationally costly and requires high-performance computing to run. We derive a novel analytical formula for the power to detect perturbation-expression associations, recapitulating power estimates from the simulation-based tool while reducing runtime by up to seven orders of magnitude. This acceleration unlocks the possibility of interactive single-cell CRISPR screen design. Accordingly, we develop PerturbPlan, an interactive web application built on the analytical power formula. PerturbPlan helps users address 11 design questions for two types of single-cell CRISPR screens, Perturb-seq and targeted Perturb-seq (TAP-seq). We apply PerturbPlan to carry out a comparative analysis of three recent Perturb-seq designs, demonstrating how optimal design varies across experiments of different scales. We also use PerturbPlan to quantify the cost savings of a recent TAP-seq study relative to a hypothetical Perturb-seq study assaying the same perturbations, illustrating how the tool can inform decisions about targeted versus whole-transcriptome readouts. In sum, PerturbPlan is the first tool to facilitate flexible and interactive design of well-powered single-cell CRISPR screen experiments.

Identifiers

PMID42239142
PMCPMC13228452

What Socratic holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.