Accurate quantification of CRISPR effects in pooled FACS screens using Waterbear

CRISPR screens are powerful tools to identify key genes that underlie biological processes. One important type of screen uses fluorescence activated cell sorting (FACS) to sort perturbed cells into bins based on the expression level of a marker gene, followed by guide RNA (gRNA) sequencing. Analysis of these data presents multiple statistical challenges due to multiple factors including the discrete nature of the bins and typically small numbers of replicate experiments. To address these challenges, we developed a robust and powerful Bayesian random effects model and software package called Waterbear. Furthermore, we used Waterbear to explore how various experimental design parameters affect statistical power to establish principled guidelines for future screens. Finally, we experimentally validated our model findings that, when using Waterbear for analysis, high power is maintained even at low coverage and a high multiplicity of infection. We anticipate that Waterbear will be of broad utility for analyzing FACS-based CRISPR screens. Pooled CRISPR screen followed by FACS to sort CD25 (IL2RA) high or low expressing cells.