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Transcriptome-wide analysis of differential expression in perturbation atlases.

Ajay Nadig1,2,3,4, Joseph M Replogle5,6,7, Angela N Pogson8

  • 1Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. anadig@broadinstitute.org.

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Summary

We developed a new statistical model, transcriptome-wide analysis of differential expression (TRADE), to better detect gene effects from noisy Perturb-seq data. TRADE reveals more widespread transcriptional changes than previously identified.

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Area of Science:

  • Genomics
  • Computational Biology
  • Systems Biology

Background:

  • Single-cell CRISPR screens like Perturb-seq offer large-scale transcriptomic profiling of genetic perturbations.
  • However, the resulting data are often noisy, leading to the undetected effects of many perturbations.

Purpose of the Study:

  • Introduce transcriptome-wide analysis of differential expression (TRADE), a novel statistical model.
  • To accurately account for estimation error and identify true differential expression effects.
  • To quantify the total effect of a perturbation across the transcriptome, termed 'transcriptome-wide impact'.

Main Methods:

  • Developed TRADE, a statistical model designed for the distribution of differential expression effects.
  • Applied TRADE to analyze several large Perturb-seq datasets.
  • Estimated the transcriptome-wide impact of gene perturbations.

Main Results:

  • TRADE detects numerous transcriptional effects missed by standard analyses, revealing them in aggregate.
  • A typical gene perturbation impacts an estimated 45 genes; an essential gene impacts over 500.
  • Observed moderate consistency of perturbation effects across different cell types.
  • Identified perturbations with dose-dependent qualitative variations in transcriptional responses.
  • Clarified the relationship between genetic and transcriptomic correlations in neuropsychiatric disorders.

Conclusions:

  • TRADE enhances the detection of subtle but widespread transcriptional effects from noisy single-cell CRISPR screen data.
  • The transcriptome-wide impact metric provides a comprehensive measure of perturbation effects.
  • Findings advance the understanding of gene function, regulatory networks, and disease mechanisms.