Evidence map›Paper›PMID 38760839›Full record

ArticleGenome biology2024

Robust differential expression testing for single-cell CRISPR screens at low multiplicity of infection.

Timothy Barry, Kaishu Mason, Kathryn Roeder, Eugene Katsevich

Abstract read
In one paragraph

Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

  1. Article
  2. Article
  3. Canon enables causal inference of downstream genes in single-cell CRISPR studies via instrumental variable analysis.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  4. Dual-line Genome-scale CRISPR Screening Enables Robust Target Gene Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  5. Article
  6. Review
  7. Review
  8. Article
  9. Article
  10. Review
  11. Predicting the unseen: A diffusion-based debiasing framework for transcriptional response prediction at single-cell resolution.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  12. Article
  13. Article
  14. Large-scale discovery of neural enhancers for cis-regulation therapies.bioRxiv : the preprint server for biology · 2025
    Article
  15. Article
  16. Article
  17. Article
  18. Simultaneous inference for generalized linear models with unmeasured confounders.Journal of the American Statistical Association · 2025
    Article
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Timothy BarryDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, USA. tbarry@hsph.harvard.edu.ORCID 0000-0002-4356-627X
Kaishu MasonDepartment of Statistics and Data Science, Wharton School, University of Pennsylvania, Philadelphia, USA.
Kathryn RoederDepartment of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, USA.
Eugene KatsevichDepartment of Statistics and Data Science, Wharton School, University of Pennsylvania, Philadelphia, USA. ekatsevi@wharton.upenn.edu.

Funding

Methods for single-cell CRISPR screens and multiomic data: constructing powerful well-calibrated tests, circumventing unmeasured confounding, and accounting for denoising and imputationR01MH123184 · NIMH · CARNEGIE-MELLON UNIVERSITY · PI KATHRYN M ROEDER · 2020 to 2026
$3.8M
Foundation for the National Institutes of Health R01MH123184National Science Foundation DMS-2113072NIMH NIH HHS R01 MH123184
6 · The paper itself

Abstract

Single-cell CRISPR screens (perturb-seq) link genetic perturbations to phenotypic changes in individual cells. The most fundamental task in perturb-seq analysis is to test for association between a perturbation and a count outcome, such as gene expression. We conduct the first-ever comprehensive benchmarking study of association testing methods for low multiplicity-of-infection (MOI) perturb-seq data, finding that existing methods produce excess false positives. We conduct an extensive empirical investigation of the data, identifying three core analysis challenges: sparsity, confounding, and model misspecification. Finally, we develop an association testing method - SCEPTRE low-MOI - that resolves these analysis challenges and demonstrates improved calibration and power.

Indexed as

Single-Cell AnalysisClustered Regularly Interspaced Short Palindromic RepeatsCRISPR-Cas SystemsHumans

Identifiers

PMID38760839
PMCPMC11100084

What OpenQuestion holds

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Registered trials

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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 OpenQuestion graph.