Department of Computer Science, UCLA, Los Angeles, CA, USA.
Computational and Systems Biology Interdepartmental Program, UCLA, Los Angeles, CA, USA.
Genome Biol. 2024 May 24;25(1):138. doi: 10.1186/s13059-024-03279-7.
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 Rosace, a Bayesian framework for analyzing growth-based DMS data. Rosace leverages amino acid position information to increase power and control the false discovery rate by sharing information across parameters via shrinkage. We also developed Rosette for simulating the distributional properties of DMS. We show that Rosace is robust to the violation of model assumptions and is more powerful than existing tools.
深度突变扫描 (DMS) 可同时测量蛋白质中数千个遗传变异的影响。由于样本量小，经典的统计方法变得无效。例如，当独立处理变体时，无法正确校准 p 值。我们提出了 Rosace，这是一种用于分析基于生长的 DMS 数据的贝叶斯框架。Rosace 利用氨基酸位置信息通过收缩在参数之间共享信息来增加功效并控制假发现率。我们还开发了 Rosette 来模拟 DMS 的分布特性。我们表明 Rosace 对模型假设的违反具有鲁棒性，并且比现有工具更强大。
