Evidence map›Paper›PMID 34930414›Full record

ArticleGenome biology2021

SCEPTRE improves calibration and sensitivity in single-cell CRISPR screen analysis.

Timothy Barry, Xuran Wang, John A Morris, Kathryn Roeder, Eugene Katsevich

Abstract read
In one paragraph

Article in Genome biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.

0numbers the graph read from it
0cells of the map it votes in
59citing 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

59 citing papers in PubMed.

  1. Article
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  4. 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
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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

5 authors.

Timothy BarryDepartment of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, 15213, PA, USA.
Xuran WangDepartment of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, 15213, PA, USA.
John A MorrisNew York Genome Center, New York, USA.
Kathryn RoederDepartment of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, 15213, PA, USA.
Eugene KatsevichDepartment of Statistics and Data Science, Wharton School, University of Pennsylvania, Philadelphia, 19104, PA, USA. ekatsevi@wharton.upenn.edu.ORCID 0000-0002-4356-627X

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
NIMH NIH HHS R01 MH123184
6 · The paper itself

Abstract

Single-cell CRISPR screens are a promising biotechnology for mapping regulatory elements to target genes at genome-wide scale. However, technical factors like sequencing depth impact not only expression measurement but also perturbation detection, creating a confounding effect. We demonstrate on two single-cell CRISPR screens how these challenges cause calibration issues. We propose SCEPTRE: analysis of single-cell perturbation screens via conditional resampling, which infers associations between perturbations and expression by resampling the former according to a working model for perturbation detection probability in each cell. SCEPTRE demonstrates very good calibration and sensitivity on CRISPR screen data, yielding hundreds of new regulatory relationships supported by orthogonal biological evidence.

Indexed as

CRISPR-Cas SystemsGenome, HumanCalibrationChromatin Immunoprecipitation SequencingClustered Regularly Interspaced Short Palindromic RepeatsGene EditingGene ExpressionHumansSingle-Cell Analysis

Identifiers

PMID34930414
PMCPMC8686614

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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.