MAUDE: Inferring expression changes in sorting-based CRISPR screens

Abstract

Pooled CRISPR screens allow high-throughput interrogation of genetic elements that alter expression of a reporter gene readout. New computational methods are needed to model these data. We created MAUDE (Mean Alterations Using Discrete Expression) for quantifying the impact of guide RNAs on a target gene’s expression in a pooled, sorting-based expression screen. MAUDE quantifies guide-level effects by modeling the distribution of cells across sorting expression bins. It then combines guides to estimate the statistical significance and effect size of targeted genetic elements. We show that MAUDE significantly improves over previous approaches and provide experimental design guidelines to best leverage MAUDE.