Abstract PR10: ScreenBEAM: a Novel Meta-Analysis Algorithm for Functional Genomics Screens via Bayesian Hierarchical Modeling

Abstract

Abstract Motivation: Functional genomics (FG) screens, using RNAi or CRISPR technology, have become a standard tool for systematic, genome-wide loss-of-function studies for therapeutic target discovery. As in many large-scale assays, however, off-target effects, variable reagents' potency, and experimental noise must be accounted for appropriately control for false positives. Indeed, rigorous statistical analysis of high-throughput FG screening data remains challenging, particularly when integrative analyses are used to combine multiple sh/sgRNAs targeting the same gene in the library. Method: We use large RNAi and CRISPR repositories that are publicly available to evaluate a novel meta-analysis approach for FG screens via Bayesian hierarchical modeling, ScreenBEAM (Screening Bayesian Evaluation and Analysis Method). Results: Results from our analysis show that the proposed strategy, which seamlessly combines all available data, robustly outperforms classical algorithms developed for microarray data sets as well as recent approaches designed for next generation sequencing technologies (NGS). Remarkably, the ScreenBEAM algorithm works well even when the quality of FG screens is relatively low, which accounts for about 80-95% of the public datasets. Availability: R package and source code are available at: https://github.com/jyyu/ScreenBEAM. Citation Format: Jiyang Yu, Jose M. Silva, Andrea Califano. ScreenBEAM: a Novel Meta-Analysis Algorithm for Functional Genomics Screens via Bayesian Hierarchical Modeling [abstract]. In: Proceedings of the AACR Precision Medicine Series: Opportunities and Challenges of Exploiting Synthetic Lethality in Cancer; Jan 4-7, 2017; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2017;16(10 Suppl):Abstract nr PR10.