Abstract Deep mutational scanning (DMS) measures the effects of thousands of genetic variants in a protein simultaneously. The small sample size renders classical statistical methods ineffective. For example, p -values cannot be correctly calibrated when treating variants independently. We propose , a Bayesian framework for analyzing growth-based DMS data. leverages amino acid position information to increase power and control the false discovery rate by sharing information across parameters via shrinkage. We also developed for simulating the distributional properties of DMS. We show that is robust to the violation of model assumptions and is more powerful than existing tools.

Rosace: a robust deep mutational scanning analysis framework employing position and mean-variance shrinkage