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Enhancing statistical accuracy in gene perturbation studies.

Vijender Kalmotia1

  • 1Department of Biomedical Engineering, Wright State University, 3640 Colonel Glenn Hwy, Dayton, OH, 45435, US.

Bio Systems
|June 2, 2026
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Summary

This study enhances the SCEPTRE framework for analyzing gene expression in CRISPR screens. The improved method accurately adjusts for technical biases, reducing false discoveries in high-throughput perturbation studies.

Keywords:
ChIP-seq enrichmentConditional randomization testing (CRT)False discovery controlFunctional genomicsGene–enhancer interactionsGenome-wide association studies (GWAS)Hi-C interaction frequencyHigh-throughput perturbation analysisModel misspecification robustnessMultiplicity of infection (MOI)Negative binomial modellingNoncoding genome annotationRegulatory genomicsResampling-based inferenceSCEPTRE frameworkSingle-cell CRISPR screeningSingle-cell RNA-seqStatistical calibrationTechnical confoundinggRNA detection modelling

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Analyzing gene expression in high-throughput perturbation studies is complex.
  • Confounding technical factors often impact accuracy.
  • Existing methods may struggle with high-multiplicity-of-infection (MOI) screens.

Purpose of the Study:

  • To evaluate and extend the SCEPTRE framework for high-MOI CRISPR screens.
  • To improve the accuracy of gene expression analysis in perturbation studies.
  • To address challenges posed by technical biases and sequencing errors.

Main Methods:

  • Applied and extended the SCEPTRE (Single-Cell PerTurbation screens via Conditional REsampling) framework.
  • Utilized a resampling-based methodology.
  • Validated the approach on high-MOI CRISPR screens.

Main Results:

  • Demonstrated SCEPTRE's applicability to high-MOI CRISPR screens.
  • The extended framework effectively adjusts for sequencing biases.
  • Reduced false discovery rates while preserving statistical power.

Conclusions:

  • The enhanced SCEPTRE framework provides a robust method for gene expression analysis in complex perturbation screens.
  • This approach improves the reliability of results from high-MOI CRISPR experiments.
  • Offers a powerful tool for researchers in genomics and systems biology.