Abstract
Pooled CRISPR screens allow researchers to interrogate genetic causes of complex phenotypes at the genome-wide scale and promise higher specificity and sensitivity compared to competing technologies. Unfortunately, two problems exist, particularly for CRISPRi/a screens: variability in guide efficiency and large rare off-target effects. We present a method, CRISPhieRmix, that resolves these issues by using a hierarchical mixture model with a broad-tailed null distribution. We show that CRISPhieRmix allows for more accurate and powerful inferences in large-scale pooled CRISPRi/a screens. We discuss key issues in the analysis and design of screens, particularly the number of guides needed for faithful full discovery.