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Abstract:
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Although several methods have been developed or adapted to analyze CRISPR screening data, no specific algorithm has gained popularity. Thus, rigorous procedures are needed to overcome shortcomings of existing algorithms. We implemented a Permutation-Based Non-Parametric Analysis (PBNPA) algorithm and compared its performance 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 ROC curves and FDR control for simulated data under various settings. Remarkably, the PBNPA algorithm showed better consistency and FDR control on published real data as well. To the best of our knowledge, our study is the first study that compared the performance of several algorithms on analyzing function genomics screening data with simulated datasets. PBNPA yields more consistent and reliable results than its competitors, especially when the data quality is low. Our PBNPA algorithm is a step toward achieving the goal of obtaining robust results of large scale genomic screening experiment while lowering the experimental cost.
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