March 3, 2026Open Access

Accurate variant effect estimation in FACS-based deep mutational scanning data with Lilace

Key Points

Variant effect estimation using a Bayesian statistical model improves accuracy in FACS-based DMS data analysis.
Lilace achieved a reduced false discovery rate while maintaining sensitivity across simulated OCT1 and Kir2.1 datasets.
Challenges include measurement variance and multidimensional FACS readouts that complicate variant analysis.
Robust validation of Lilace demonstrates its effectiveness in estimating uncertainty in variant effects.

Abstract

Deep mutational scanning (DMS) coupled with fluorescence-activated cell sorting (FACS) provides a high-throughput method to link genetic variants with quantitative molecular phenotypes. Analysis of these experiments is challenging due to measurement variance and the multidimensional FACS readout. However, no statistical method has yet been developed to address these challenges. 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 datasets, demonstrating an improved false discovery rate while largely maintaining sensitivity.

Accurate variant effect estimation in FACS-based deep mutational scanning data with Lilace

Key Points

Abstract

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