Rosace: a robust deep mutational scanning analysis framework employing position and mean-variance shrinkage
Category
Published on
Type
journal-article
Author
Jingyou Rao and Ruiqi Xin and Christian Macdonald and Matthew K. Howard and Gabriella O. Estevam and Sook Wah Yee and Mingsen Wang and James S. Fraser and Willow Coyote-Maestas and Harold Pimentel
Citation
Rao, J., Xin, R., Macdonald, C., Howard, M. K., Estevam, G. O., Yee, S. W., Wang, M., Fraser, J. S., Coyote-Maestas, W., & Pimentel, H. (2024). Rosace: a robust deep mutational scanning analysis framework employing position and mean-variance shrinkage. Genome Biology, 25(1). https://doi.org/10.1186/s13059-024-03279-7
Abstract
AbstractDeep 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.
