Filter inference: A scalable nonlinear mixed effects inference approach for snapshot time series data.
Filter inference: A scalable nonlinear mixed effects inference approach for snapshot time series data.
复制标题
DOI：
10.1371/journal.pcbi.1011135
复制
发表时间：
2023-05
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
作者：
可变性是生物系统的内在属性，通常是其复杂行为的核心。例子从细胞信号传导途径的细胞间变异性到患者对治疗反应的变异性。一种流行的建模和理解这种变化的方法是非线性混合效应（NLME）建模。然而，随着被测个体数量的增长，从测量值估计NLME模型的参数很快变得计算昂贵，使得NLME推断对于具有数千个被测个体的数据集来说是棘手的。该缺点对于快照数据集特别受限，例如在细胞生物学中常见，其中高通量测量技术提供大量的单细胞测量。我们介绍了一种新的方法来估计NLME模型参数的快照测量，我们称之为过滤器推断。过滤器推断使用模拟个体的测量值来定义模型参数的近似可能性，避免了传统NLME推断方法的计算限制，并使从快照测量值进行有效推断成为可能。滤波器推理也可以很好地与模型参数的数量进行缩放，使用最先进的基于梯度的MCMC算法，如无U形转弯采样器（NUTS）。我们证明了过滤推理的性质，使用早期癌症生长建模和表皮生长因子信号通路建模的例子。非线性混合效应（NLME）模型广泛用于对群体中个体之间的差异进行建模。例如，在药理学中，它们用于模拟患者之间的治疗反应差异，在细胞生物学中，它们用于模拟细胞信号传导途径中的细胞间差异。然而，NLME模型引入了参数，这些参数通常需要从数据中估计。当被测量的个体数量（无论是患者还是细胞）太大时，这种估计在计算上变得难以处理。但是，在群体中测量的个体越多，就越能更好地理解变异性。当个体仅被测量一次时，尤其如此。这种快照测量在细胞生物学中特别常见，其中高通量测量技术提供大量的单细胞测量。在临床药理学中，由许多快照测量组成的数据集不太常见，但比跨患者的详细时间序列测量更容易获得且更便宜。我们的方法可以用来估计NLME模型的参数，从快照时间序列数据与数千个测量的个人。
Variability is an intrinsic property of biological systems and is often at the heart of their complex behaviour. Examples range from cell-to-cell variability in cell signalling pathways to variability in the response to treatment across patients. A popular approach to model and understand this variability is nonlinear mixed effects (NLME) modelling. However, estimating the parameters of NLME models from measurements quickly becomes computationally expensive as the number of measured individuals grows, making NLME inference intractable for datasets with thousands of measured individuals. This shortcoming is particularly limiting for snapshot datasets, common e.g. in cell biology, where high-throughput measurement techniques provide large numbers of single cell measurements. We introduce a novel approach for the estimation of NLME model parameters from snapshot measurements, which we call filter inference. Filter inference uses measurements of simulated individuals to define an approximate likelihood for the model parameters, avoiding the computational limitations of traditional NLME inference approaches and making efficient inferences from snapshot measurements possible. Filter inference also scales well with the number of model parameters, using state-of-the-art gradient-based MCMC algorithms such as the No-U-Turn Sampler (NUTS). We demonstrate the properties of filter inference using examples from early cancer growth modelling and from epidermal growth factor signalling pathway modelling. Nonlinear mixed effects (NLME) models are widely used to model differences between individuals in a population. In pharmacology, for example, they are used to model the treatment response variability across patients, and in cell biology they are used to model the cell-to-cell variability in cell signalling pathways. However, NLME models introduce parameters, which typically need to be estimated from data. This estimation becomes computationally intractable when the number of measured individuals—be they patients or cells—is too large. But, the more individuals are measured in a population, the better the variability can be understood. This is especially true when individuals are measured only once. Such snapshot measurements are particularly common in cell biology, where high-throughput measurement techniques provide large numbers of single cell measurements. In clinical pharmacology, datasets consisting of many snapshot measurements are less common but are easier and cheaper to obtain than detailed time series measurements across patients. Our approach can be used to estimate the parameters of NLME models from snapshot time series data with thousands of measured individuals.
登录
查看更多内容
影响因子：
3.7
作者：
Lecuit, Mathilde;Aucouturier, Pierre;Coppo, Paul
通讯作者：
Coppo, Paul
DOI：
10.2202/1544-6115.1389
发表时间：
2008-01-01
影响因子：
0.9
作者：
Joyce, Paul;Marjoram, Paul
通讯作者：
Marjoram, Paul
影响因子：
2
作者：
Lambert, Ben;Gavaghan, David J.;Tavener, Simon J.
通讯作者：
Tavener, Simon J.
影响因子：
4.4
作者：
Lambert, Ben;Vehtari, Aki
通讯作者：
Vehtari, Aki
DOI：
10.1080/01621459.2022.2086132
发表时间：
2022-07-08
影响因子：
3.7
作者：
Frazier, David T.;Nott, David J.;Kohn, Robert
通讯作者：
Kohn, Robert