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Related Experiment Video

Updated: Jun 12, 2026

Pooled CRISPR-Based Genetic Screens in Mammalian Cells
09:05

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Published on: September 4, 2019

Causal effect estimation from trans-regulatory single-cell CRISPR screens.

Oliver P Christensen1, Alex Markham2, Hyunseung Kang3

  • 1Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark; Pioneer Centre for SMARTbiomed, University of Copenhagen, Copenhagen, Denmark.

Cell Genomics
|June 10, 2026
PubMed
Summary

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Single-cell CRISPR screens can estimate causal genetic effects on gene expression. This review details assumptions for interpreting these results and highlights how violating them can bias findings.

Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Single-cell transcriptomics and CRISPR screening enable large-scale genetic perturbation studies.
  • These methods offer potential for causal inference in gene expression.

Purpose of the Study:

  • To introduce causal effect estimation principles for single-cell CRISPR studies.
  • To outline assumptions for interpreting statistical associations as causal effects.
  • To review existing statistical approaches and their limitations.

Main Methods:

  • Review of statistical concepts for causal inference.
  • Description of assumptions required for causal interpretation in trans-regulatory screens.
  • Illustrative example demonstrating assumption violation effects.
Keywords:
Perturb-seqcausal effect estimationsingle-cell CRISPR screenssingle-cell transcriptomics

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Last Updated: Jun 12, 2026

Pooled CRISPR-Based Genetic Screens in Mammalian Cells
09:05

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Published on: September 4, 2019

Genome-Wide CRISPR Screen for Unveiling Radiosensitive and Radioresistant Genes
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Genome-Wide CRISPR Screen for Unveiling Radiosensitive and Radioresistant Genes

Published on: May 23, 2025

Cell Surface Receptor Identification Using Genome-Scale CRISPR/Cas9 Genetic Screens
08:49

Cell Surface Receptor Identification Using Genome-Scale CRISPR/Cas9 Genetic Screens

Published on: June 6, 2020

Main Results:

  • Identified key assumptions for causal interpretation of single-cell CRISPR screen data.
  • Provided an overview of statistical methods applicable to these studies.
  • Demonstrated potential biases arising from violated assumptions.

Conclusions:

  • Causal inference from single-cell CRISPR screens requires careful consideration of underlying assumptions.
  • Existing statistical methods can provide causal estimates when assumptions are met.
  • Violations of these assumptions can lead to misleading conclusions about gene regulatory effects.