Abstract
Deep mutational scanning (DMS) experiments interrogate the effect of genetic variants on protein function, often using fluorescence-activated cell sorting (FACS) to quantitatively measure molecular phenotypes, such as abundance or activity. Analysis of DMS experiments with a FACS readout is challenging due to measurement variance and the unique multidimensional nature of the phenotype. However, no statistical method has yet been developed to address the challenges of FACS-based DMS. Here we present Lilace, a Bayesian statistical model to estimate variant effects with uncertainty quantification from FACS-based DMS experiments. We validate Lilace's performance and robustness using simulated data and apply it to OCT1 and Kir2.1 DMS experiments, demonstrating an improved false discovery rate (FDR) while largely maintaining sensitivity.