Wu Xiaowei, Zhu Hongxiao
Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, United States.
Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, United States.
J Theor Biol. 2015 Feb 7;366:1-7. doi: 10.1016/j.jtbi.2014.11.009. Epub 2014 Nov 20.
Since fluctuation analysis was first introduced by Luria and Delbrück in 1943, it has been widely used to make inference about spontaneous mutation rates in cultured cells. Under certain model assumptions, the probability distribution of the number of mutants that appear in a fluctuation experiment can be derived explicitly, which provides the basis of mutation rate estimation. It has been shown that, among various existing estimators, the maximum likelihood estimator usually demonstrates some desirable properties such as consistency and lower mean squared error. However, its application in real experimental data is often hindered by slow computation of likelihood due to the recursive form of the mutant-count distribution. We propose a fast maximum likelihood estimator of mutation rates, MLE-BD, based on a birth-death process model with non-differential growth assumption. Simulation studies demonstrate that, compared with the conventional maximum likelihood estimator derived from the Luria-Delbrück distribution, MLE-BD achieves substantial improvement on computational speed and is applicable to arbitrarily large number of mutants. In addition, it still retains good accuracy on point estimation.
自1943年卢里亚和德尔布吕克首次引入波动分析以来，它已被广泛用于推断培养细胞中的自发突变率。在某些模型假设下，可以明确推导波动实验中出现的突变体数量的概率分布，这为突变率估计提供了基础。研究表明，在现有的各种估计器中，最大似然估计器通常表现出一些理想的特性，如一致性和较低的均方误差。然而，由于突变体计数分布的递归形式，其在实际实验数据中的应用常常受到似然计算速度慢的阻碍。我们基于具有非差异增长假设的生死过程模型，提出了一种快速的突变率最大似然估计器MLE-BD。模拟研究表明，与从卢里亚-德尔布吕克分布导出的传统最大似然估计器相比，MLE-BD在计算速度上有显著提高，并且适用于任意数量的突变体。此外，它在点估计上仍保持良好的准确性。
