Accelerating Bayesian inference of dependency between mixed-type biological traits.

IF 4.3 2区 生物学
PLoS Computational Biology Pub Date : 2023-08-28 eCollection Date: 2023-08-01 DOI:10.1371/journal.pcbi.1011419
Zhenyu Zhang, Akihiko Nishimura, Nídia S Trovão, Joshua L Cherry, Andrew J Holbrook, Xiang Ji, Philippe Lemey, Marc A Suchard
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引用次数: 0

Abstract

Inferring dependencies between mixed-type biological traits while accounting for evolutionary relationships between specimens is of great scientific interest yet remains infeasible when trait and specimen counts grow large. The state-of-the-art approach uses a phylogenetic multivariate probit model to accommodate binary and continuous traits via a latent variable framework, and utilizes an efficient bouncy particle sampler (BPS) to tackle the computational bottleneck-integrating many latent variables from a high-dimensional truncated normal distribution. This approach breaks down as the number of specimens grows and fails to reliably characterize conditional dependencies between traits. Here, we propose an inference pipeline for phylogenetic probit models that greatly outperforms BPS. The novelty lies in 1) a combination of the recent Zigzag Hamiltonian Monte Carlo (Zigzag-HMC) with linear-time gradient evaluations and 2) a joint sampling scheme for highly correlated latent variables and correlation matrix elements. In an application exploring HIV-1 evolution from 535 viruses, the inference requires joint sampling from an 11,235-dimensional truncated normal and a 24-dimensional covariance matrix. Our method yields a 5-fold speedup compared to BPS and makes it possible to learn partial correlations between candidate viral mutations and virulence. Computational speedup now enables us to tackle even larger problems: we study the evolution of influenza H1N1 glycosylations on around 900 viruses. For broader applicability, we extend the phylogenetic probit model to incorporate categorical traits, and demonstrate its use to study Aquilegia flower and pollinator co-evolution.

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加速混合型生物特征之间依赖性的贝叶斯推断。
在解释标本之间的进化关系的同时,推断混合型生物特征之间的依赖性具有很大的科学意义,但当特征和标本数量增加时,这仍然是不可行的。最先进的方法使用系统发育多变量probit模型,通过潜在变量框架来适应二元和连续特征,并使用有效的有界粒子采样器(BPS)来解决从高维截断正态分布中集成许多潜在变量的计算瓶颈。这种方法随着样本数量的增长而失效,并且无法可靠地表征性状之间的条件依赖性。在这里,我们提出了一个系统发育概率集模型的推理管道,它大大优于BPS。新颖性在于1)将最近的Zigzag哈密顿蒙特卡罗(Zigzag HMC)与线性时间梯度评估相结合,以及2)用于高度相关的潜在变量和相关矩阵元素的联合采样方案。在探索535种病毒的HIV-1进化的应用中,推断需要从11235维截断正态和24维协方差矩阵中联合采样。与BPS相比,我们的方法产生了5倍的加速,并使我们有可能了解候选病毒突变和毒力之间的部分相关性。计算加速现在使我们能够解决更大的问题:我们研究了大约900种病毒上甲型H1N1流感糖基化的进化。为了更广泛的适用性,我们扩展了系统发育概率模型,将分类特征纳入其中,并证明了它在研究Aquilegia花和传粉昆虫共同进化中的应用。
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来源期刊
PLoS Computational Biology
PLoS Computational Biology 生物-生化研究方法
CiteScore
7.10
自引率
4.70%
发文量
820
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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