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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月27日

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  • Jerome Freudenberg,
  • Jingyou Rao,
  • Matthew K. Howard,
  • Christian Macdonald,
  • Noah F. Greenwald,
  • Willow Coyote-Maestas,
  • Harold Pimentel

摘要

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.