Binary Time Series Modeling With Application to Adhesion Frequency Experiments

Hung, Ying; Zarnitsyna, Veronika I.; Zhang, Yan; Zhu, Cheng; Wu, Chenxi

doi:10.1198/016214508000000508

Cited by 16 publications

(20 citation statements)

References 26 publications

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“…Further extensions can be made by replacing Z t (x) with a spatio-temporal Gaussian process Z(t, x), but the computational cost will be higher. Without the Gaussian process assumption in (3), the mean function is closely related to the Zeger-Qaqish (1988) model and its extensions in Hung et al (2008) and Benjamin et al (2003), all of which take into account the autoregressive predictors in logistic regression.…”

Section: Generalized Gaussian Process Models For Binary Time Seriesmentioning

confidence: 99%

A Generalized Gaussian Process Model for Computer Experiments With Binary Time Series

Sung

Hung

Rittase

et al. 2019

Journal of the American Statistical Association

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Non-Gaussian observations such as binary responses are common in some computer experiments. Motivated by the analysis of a class of cell adhesion experiments, we introduce a generalized Gaussian process model for binary responses, which shares some common features with standard GP models. In addition, the proposed model incorporates a flexible mean function that can capture different types of time series structures. Asymptotic properties of the estimators are derived, and an optimal predictor as well as its predictive distribution are constructed. Their performance is examined via two simulation studies. The methodology is applied to study computer simulations for cell adhesion experiments. The fitted model reveals important biological information in repeated cell bindings, which is not directly observable in lab experiments.

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Section: Generalized Gaussian Process Models For Binary Time Seriesmentioning

confidence: 99%

A Generalized Gaussian Process Model for Computer Experiments With Binary Time Series

Sung

Hung

Rittase

et al. 2019

Journal of the American Statistical Association

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“…Moreover, the thermal fluctuations are independently distributed given their binding status (e.g., binding or not), but the transition from one status to another can be dependent. For example, in some receptor-ligand systems (Zarnitsyna et al 2007;Hung et al 2008), the chance of having a binding in the next contact is increased (or decreased) if there is a binding in the immediate past. Because of the dependence, standard approaches such as change point techniques (Bhattacharya 1994;Carlstein et al 1994;Hawkins and Zamba 2005;Krishnaiah and Miao 1988) are not directly applicable.…”

Section: Introductionmentioning

confidence: 98%

Hidden Markov Models With Applications in Cell Adhesion Experiments

Hung

Wang

Zarnitsyna

et al. 2013

Journal of the American Statistical Association

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Estimation of the number of hidden states is challenging in hidden Markov models. Motivated by the analysis of a specific type of cell adhesion experiments, a new framework based on a hidden Markov model and double penalized order selection is proposed. The order selection procedure is shown to be consistent in estimating the number of states. A modified expectation-maximization algorithm is introduced to efficiently estimate parameters in the model. Simulations show that the proposed framework outperforms existing methods. Applications of the proposed methodology to real data demonstrate the accuracy of estimating receptor-ligand bond lifetimes and waiting times which are essential in kinetic parameter estimation. Supplementary materials for this article are available online.

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“…We consider a weight vector suggested in Zou (2006) witĥ ν j = |β j | −ρ , where ρ > 0 andβ j is a root-n-consistent estimator of β j . In Hung (2011), it is shown that the MLE of β is root-n-consistent under model (2), therefore it can be applied.…”

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Order selection in nonlinear time series models with application to the study of cell memory

Hung¹

2012

Ann. Appl. Stat.

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Cell adhesion experiments are biomechanical experiments studying the binding of a cell to another cell at the level of single molecules. Such a study plays an important role in tumor metastasis in cancer study. Motivated by analyzing a repeated cell adhesion experiment, a new class of nonlinear time series models with an order selection procedure is developed in this paper. Due to the nonlinearity, there are two types of overfitting. Therefore, a double penalized approach is introduced for order selection. To implement this approach, a global optimization algorithm using mixed integer programming is discussed. The procedure is shown to be asymptotically consistent in estimating both the order and parameters of the proposed model. Simulations show that the new order selection approach outperforms standard methods. The finite-sample performance of the estimator is also examined via a simulation study. The application of the proposed methodology to a T-cell experiment provides a better understanding of the kinetics and mechanics of cell adhesion, including quantifying the memory effect on a repeated unbinding force experiment and identifying the order of the memory.

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Binary Time Series Modeling With Application to Adhesion Frequency Experiments

Cited by 16 publications

References 26 publications

A Generalized Gaussian Process Model for Computer Experiments With Binary Time Series

A Generalized Gaussian Process Model for Computer Experiments With Binary Time Series

Hidden Markov Models With Applications in Cell Adhesion Experiments

Order selection in nonlinear time series models with application to the study of cell memory

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