An efficient Bayesian inference framework for coalescent-based nonparametric phylodynamics
An efficient Bayesian inference framework for coalescent-based nonparametric phylodynamics
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DOI：
10.1093/bioinformatics/btv378
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发表时间：
2015-10-15
期刊：
影响因子：
5.8
通讯作者：
Shahbaba, Babak
中科院分区：
文献类型：
--
作者：
Lan, Shiwei;Palacios, Julia A.;Shahbaba, Babak
动机：种群规模动态领域的重点是利用从感兴趣的种群中获取的当前遗传样本来重建种群规模动态的问题。该技术已广泛用于生物学的许多领域，但对于研究快速演变的传染病病原体（例如流感病毒）的传播特别有用。系统动力学推理使用一个结合模型，该模型定义了从种群中随机抽样的个体的系谱的概率密度。当我们假设这样的系谱是已知的，合并模型，配备了高斯过程之前的人口规模的轨迹，允许非参数贝叶斯估计的人口规模动态。虽然这种方法是相当强大的，在传染病监测过程中收集的大数据集的挑战国家的最先进的贝叶斯推理动力学和需求推理的方法，以相对较低的计算cost.Results：为了满足这一需求，我们提供了一个计算效率的贝叶斯推理框架的基础上结合过程模型的哈密顿蒙特卡罗。此外，我们表明，通过分裂的哈密顿函数，我们可以进一步提高这种方法的效率。使用几个模拟和真实的数据集，我们表明，我们的方法提供了准确的估计人口规模的动态，是大大快于其他方法的基础上椭圆切片采样器和大都会调整Langevin算法。
Motivation: The field of phylodynamics focuses on the problem of reconstructing population size dynamics over time using current genetic samples taken from the population of interest. This technique has been extensively used in many areas of biology but is particularly useful for studying the spread of quickly evolving infectious diseases agents, e.g. influenza virus. Phylodynamic inference uses a coalescent model that defines a probability density for the genealogy of randomly sampled individuals from the population. When we assume that such a genealogy is known, the coalescent model, equipped with a Gaussian process prior on population size trajectory, allows for nonparametric Bayesian estimation of population size dynamics. Although this approach is quite powerful, large datasets collected during infectious disease surveillance challenge the state-of-the-art of Bayesian phylodynamics and demand inferential methods with relatively low computational cost.Results: To satisfy this demand, we provide a computationally efficient Bayesian inference framework based on Hamiltonian Monte Carlo for coalescent process models. Moreover, we show that by splitting the Hamiltonian function, we can further improve the efficiency of this approach. Using several simulated and real datasets, we show that our method provides accurate estimates of population size dynamics and is substantially faster than alternative methods based on elliptical slice sampler and Metropolis-adjusted Langevin algorithm.