A permutation-based non-parametric analysis of CRISPR screen data

Clustered regularly-interspaced short palindromic repeats (CRISPR) screens are usually implemented in cultured cells to identify genes with critical functions. Although several methods have been developed or adapted to analyze CRISPR screening data, no single specific algorithm has gained popularity...

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Bibliographic Details
Published in:BMC genomics 2017-07, Vol.18 (1), p.545-545, Article 545
Main Authors: Jia, Gaoxiang, Wang, Xinlei, Xiao, Guanghua
Format: Article
Language:English
Subjects:
Algorithms
Base sequence
Cancer
Care and treatment
Clustered Regularly Interspaced Short Palindromic Repeats - genetics
Diagnosis
False discovery rate
Functional genomics
Gene mutation
Methodology
Microbial drug resistance
Negative selection
Next generation sequencing
Positive selection
RNA interference
RNA, Guide, CRISPR-Cas Systems - genetics
Statistics, Nonparametric
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Summary:Clustered regularly-interspaced short palindromic repeats (CRISPR) screens are usually implemented in cultured cells to identify genes with critical functions. Although several methods have been developed or adapted to analyze CRISPR screening data, no single specific algorithm has gained popularity. Thus, rigorous procedures are needed to overcome the shortcomings of existing algorithms. We developed a Permutation-Based Non-Parametric Analysis (PBNPA) algorithm, which computes p-values at the gene level by permuting sgRNA labels, and thus it avoids restrictive distributional assumptions. Although PBNPA is designed to analyze CRISPR data, it can also be applied to analyze genetic screens implemented with siRNAs or shRNAs and drug screens. We compared the performance of PBNPA with competing methods on simulated data as well as on real data. PBNPA outperformed recent methods designed for CRISPR screen analysis, as well as methods used for analyzing other functional genomics screens, in terms of Receiver Operating Characteristics (ROC) curves and False Discovery Rate (FDR) control for simulated data under various settings. Remarkably, the PBNPA algorithm showed better consistency and FDR control on published real data as well. PBNPA yields more consistent and reliable results than its competitors, especially when the data quality is low. R package of PBNPA is available at: https://cran.r-project.org/web/packages/PBNPA/ .
ISSN:1471-2164
1471-2164
DOI:10.1186/s12864-017-3938-5