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用于指导阵列 CRISPR 筛选实验中分析方法选择的统计模拟模型。

A statistical simulation model to guide the choices of analytical methods in arrayed CRISPR screen experiments.

机构信息

Data Sciences & Quantitative Biology, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, England.

Functional Genomics, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, England.

出版信息

PLoS One. 2024 Aug 20;19(8):e0307445. doi: 10.1371/journal.pone.0307445. eCollection 2024.

DOI:10.1371/journal.pone.0307445
PMID:39163294
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11335118/
Abstract

An arrayed CRISPR screen is a high-throughput functional genomic screening method, which typically uses 384 well plates and has different gene knockouts in different wells. Despite various computational workflows, there is currently no systematic way to find what is a good workflow for arrayed CRISPR screening data analysis. To guide this choice, we developed a statistical simulation model that mimics the data generating process of arrayed CRISPR screening experiments. Our model is flexible and can simulate effects on phenotypic readouts of various experimental factors, such as the effect size of gene editing, as well as biological and technical variations. With two examples, we showed that the simulation model can assist making principled choice of normalization and hit calling method for the arrayed CRISPR data analysis. This simulation model is implemented in an R package and can be downloaded from Github.

摘要

基因敲除芯片是一种高通量的功能基因组筛选方法，通常使用 384 孔板，在不同的孔中具有不同的基因敲除。尽管有各种计算工作流程，但目前还没有系统的方法来找到基因敲除芯片筛选数据分析的好工作流程。为了指导这种选择，我们开发了一个统计模拟模型，该模型模拟了基因敲除芯片筛选实验的数据生成过程。我们的模型具有灵活性，可以模拟各种实验因素（例如基因编辑的效应大小）以及生物和技术变化对表型读数的影响。通过两个示例，我们表明模拟模型可以帮助对基因敲除芯片数据分析的归一化和命中调用方法进行有原则的选择。这个模拟模型是用 R 包实现的，可以从 Github 上下载。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8e3/11335118/72868b98a2ed/pone.0307445.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8e3/11335118/b8e9f51ab3df/pone.0307445.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8e3/11335118/2bbe6670df30/pone.0307445.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8e3/11335118/4a2cfa340c5b/pone.0307445.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8e3/11335118/b9e963a9e3ec/pone.0307445.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8e3/11335118/72868b98a2ed/pone.0307445.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8e3/11335118/b8e9f51ab3df/pone.0307445.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8e3/11335118/2bbe6670df30/pone.0307445.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8e3/11335118/4a2cfa340c5b/pone.0307445.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8e3/11335118/b9e963a9e3ec/pone.0307445.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8e3/11335118/72868b98a2ed/pone.0307445.g005.jpg

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引用本文的文献

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Correction: A statistical simulation model to guide the choices of analytical methods in arrayed CRISPR screen experiments.更正：一种用于指导阵列式CRISPR筛选实验中分析方法选择的统计模拟模型。
PLoS One. 2025 Mar 20;20(3):e0320884. doi: 10.1371/journal.pone.0320884. eCollection 2025.

本文引用的文献

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High-content CRISPR screening.高内涵CRISPR筛选
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Massively Parallel CRISPR-Based Genetic Perturbation Screening at Single-Cell Resolution.大规模并行基于 CRISPR 的遗传干扰筛选技术，实现单细胞分辨率。
Adv Sci (Weinh). 2023 Feb;10(4):e2204484. doi: 10.1002/advs.202204484. Epub 2022 Dec 11.
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SCEPTRE improves calibration and sensitivity in single-cell CRISPR screen analysis.SCEPTRE 提高了单细胞 CRISPR 筛选分析中的校准和灵敏度。
Genome Biol. 2021 Dec 20;22(1):344. doi: 10.1186/s13059-021-02545-2.
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Single-cell image analysis to explore cell-to-cell heterogeneity in isogenic populations.单细胞图像分析探索同基因群体中的细胞间异质性。
Cell Syst. 2021 Jun 16;12(6):608-621. doi: 10.1016/j.cels.2021.05.010.
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A benchmark of algorithms for the analysis of pooled CRISPR screens.用于分析汇集 CRISPR 筛选的算法基准。
Genome Biol. 2020 Mar 9;21(1):62. doi: 10.1186/s13059-020-01972-x.
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