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ShrinkCRISPR: a flexible method for differential fitness analysis of CRISPR-Cas9 screen data.
Tissier, Renaud L M; Schie, Janne J M van; Wolthuis, Rob M F; Lange, Job de; Menezes, Renée de.
Afiliação
  • Tissier RLM; Biostatistics Unit, Netherlands Cancer institute, Amsterdam, The Netherlands. r.tissier@nki.nl.
  • Schie JJMV; Department of Clinical Genetics, Section Oncogenetics, Cancer Center Amsterdam, Amsterdam University Medical Center, Amsterdam, The Netherlands.
  • Wolthuis RMF; Department of Clinical Genetics, Section Oncogenetics, Cancer Center Amsterdam, Amsterdam University Medical Center, Amsterdam, The Netherlands.
  • Lange J; Department of Clinical Genetics, Section Oncogenetics, Cancer Center Amsterdam, Amsterdam University Medical Center, Amsterdam, The Netherlands.
  • Menezes R; Biostatistics Unit, Netherlands Cancer institute, Amsterdam, The Netherlands.
BMC Bioinformatics ; 24(1): 36, 2023 Feb 03.
Article em En | MEDLINE | ID: mdl-36732720
ABSTRACT

BACKGROUND:

CRISPR screens provide large-scale assessment of cellular gene functions. Pooled libraries typically consist of several single guide RNAs (sgRNAs) per gene, for a large number of genes, which are transduced in such a way that every cell receives at most one sgRNA, resulting in the disruption of a single gene in that cell. This approach is often used to investigate effects on cellular fitness, by measuring sgRNA abundance at different time points. Comparing gene knockout effects between different cell populations is challenging due to variable cell-type specific parameters and between replicates variation. Failure to take those into account can lead to inflated or false discoveries.

RESULTS:

We propose a new, flexible approach called ShrinkCRISPR that can take into account multiple sources of variation. Impact on cellular fitness between conditions is inferred by using a mixed-effects model, which allows to test for gene-knockout effects while taking into account sgRNA-specific variation. Estimates are obtained using an empirical Bayesian approach. ShrinkCRISPR can be applied to a variety of experimental designs, including multiple factors. In simulation studies, we compared ShrinkCRISPR results with those of drugZ and MAGeCK, common methods used to detect differential effect on cell fitness. ShrinkCRISPR yielded as many true discoveries as drugZ using a paired screen design, and outperformed both drugZ and MAGeCK for an independent screen design. Although conservative, ShrinkCRISPR was the only approach that kept false discoveries under control at the desired level, for both designs. Using data from several publicly available screens, we showed that ShrinkCRISPR can take data for several time points into account simultaneously, helping to detect early and late differential effects.

CONCLUSIONS:

ShrinkCRISPR is a robust and flexible approach, able to incorporate different sources of variations and to test for differential effect on cell fitness at the gene level. These improve power to find effects on cell fitness, while keeping multiple testing under the correct control level and helping to improve reproducibility. ShrinkCrispr can be applied to different study designs and incorporate multiple time points, making it a complete and reliable tool to analyze CRISPR screen data.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Sistemas CRISPR-Cas Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Sistemas CRISPR-Cas Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article