Abstract
Deep mutational scanning (DMS) enables functional insight into protein mutations with multiplexed measurements of thousands of genetic variants in a protein simultaneously. The small sample size of DMS renders classical statistical methods ineffective, for example, p-values cannot be correctly calibrated when treating variants independently. We propose Rosace, a Bayesian framework for analyzing growth-based deep mutational scanning data. Rosace leverages amino acid position information to increase power and control the false discovery rate by sharing information across parameters via shrinkage. To benchmark Rosace against existing methods, we developed Rosette, a simulation framework that simulates the distributional properties of DMS. Further, we show that Rosace is robust to the violation of model assumptions and is more powerful than existing tools under Rosette simulation and real data.
Competing Interest Statement
The authors have declared no competing interest.
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