Analyzing Single-Molecule Protein Transportation Experiments via Hierarchical Hidden Markov Models

Chen, Yang; Shen, Kuang; Shan, Shu‐ou; Kou, S. C.

doi:10.1080/01621459.2016.1140050

Cited by 14 publications

(21 citation statements)

References 62 publications

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“…Since these estimates tend to be midway between the higher and lower decay constants, they have the effect of causing the higher estimate to have negative bias, and the lower estimate to have positive bias, a trend which is seen in our simulated data (Figs 3 and 4, lower panels). It is worth noting that other methods of dwell time analysis, including Bayesian methods and Hidden Markov Models, have been shown to systematically underestimate decay constants, similar to what we find for the GMM (7, 8).…”

Section: Discussionsupporting

confidence: 84%

“…Other methods, such as particle tracking using micro-beads, can also yield data on single molecule state transitions (6). In general, the determination of how many states are present and how long the molecule dwells in each state can be challenging, and several methods have been developed to deal with this problem (7, 8). Here, we do not treat this problem, but address the problem of the statistical analysis of the dwell times independent of the method in which they are determined.…”

Section: Introductionmentioning

confidence: 99%

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Analyzing Dwell Times with the Generalized Method of Moments

Piatt

Price

2018

Preprint

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19The Generalized Method of Moments (GMM) is a statistical method for the analysis of 20 samples from random processes. First developed for the analysis of econometric data, the method 21 is here formulated to extract hidden kinetic parameters from measurements of single molecule 22 dwell times. Our method is based on the analysis of cumulants of the measured dwell times. We 23 develop a general form of an objective function whose minimization can return estimates of decay 24 parameters for any number of intermediates directly from the data. We test the performance of 25 our technique using both simulated and experimental data. We also compare the performance of

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Section: Discussionsupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Analyzing Dwell Times with the Generalized Method of Moments

Piatt

Price

2018

Preprint

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“…We evaluated the BIC and report solutions that map to large initial improvements of BIC [22]. Structure learning without prior knowledge on the considered set of reactions achieves 32 true positive at the cost of 2 false positive reactions (10 5 single cell trajectories, 33 time points, capture efficiency 0.05, Fig 6A).…”

Section: Resultsmentioning

confidence: 99%

Sparse Regression Based Structure Learning of Stochastic Reaction Networks from Single Cell Snapshot Time Series

2016

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Stochastic chemical reaction networks constitute a model class to quantitatively describe dynamics and cell-to-cell variability in biological systems. The topology of these networks typically is only partially characterized due to experimental limitations. Current approaches for refining network topology are based on the explicit enumeration of alternative topologies and are therefore restricted to small problem instances with almost complete knowledge. We propose the reactionet lasso, a computational procedure that derives a stepwise sparse regression approach on the basis of the Chemical Master Equation, enabling large-scale structure learning for reaction networks by implicitly accounting for billions of topology variants. We have assessed the structure learning capabilities of the reactionet lasso on synthetic data for the complete TRAIL induced apoptosis signaling cascade comprising 70 reactions. We find that the reactionet lasso is able to efficiently recover the structure of these reaction systems, ab initio, with high sensitivity and specificity. With only < 1% false discoveries, the reactionet lasso is able to recover 45% of all true reactions ab initio among > 6000 possible reactions and over 102000 network topologies. In conjunction with information rich single cell technologies such as single cell RNA sequencing or mass cytometry, the reactionet lasso will enable large-scale structure learning, particularly in areas with partial network structure knowledge, such as cancer biology, and thereby enable the detection of pathological alterations of reaction networks. We provide software to allow for wide applicability of the reactionet lasso.

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“…Other methods, such as particle tracking using micro-beads, can also yield data on single molecule rates [7]. The determination of how many states are present and how long the molecule dwells in each state can be challenging, and several methods have been developed to deal with this obstacle [8,9]. Most current methods have focused on analyzing FRET signals, however dwell times can be produced by other methods also, such as bead loss assays [10].…”

Section: Introductionmentioning

confidence: 99%

Analyzing dwell times with the Generalized Method of Moments

Piatt

Price

2019

PLoS ONE

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The Generalized Method of Moments (GMM) is a statistical method for the analysis of samples from random processes. First developed for the analysis of econometric data, the method is here formulated to extract hidden kinetic parameters from measurements of single molecule dwell times. Our method is based on the analysis of cumulants of the measured dwell times. We develop a general form of an objective function whose minimization can return estimates of decay parameters for any number of intermediates directly from the data. We test the performance of our technique using both simulated and experimental data. We also compare the performance of our method to nonlinear least-squares minimization (NL-LSQM), a commonly-used technique for analysis of single molecule dwell times. Our findings indicate that the GMM performs comparably to NL-LSQM over most of the parameter range we explore. It offers some benefits compared with NL-LSQM in that it does not require binning, exhibits slightly lower bias and variance with small sample sizes (N<20), and is somewhat superior in identifying fast decay times with these same low count data sets. Additionally, a comparison with the Classical Method of Moments (CMM) shows that the CMM can fail in many cases, whereas the GMM always returns estimates. Our results show that the GMM can be a useful tool and complements standard approaches to analysis of single molecule dwell times.

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Analyzing Single-Molecule Protein Transportation Experiments via Hierarchical Hidden Markov Models

Cited by 14 publications

References 62 publications

Analyzing Dwell Times with the Generalized Method of Moments

Analyzing Dwell Times with the Generalized Method of Moments

Sparse Regression Based Structure Learning of Stochastic Reaction Networks from Single Cell Snapshot Time Series

Analyzing dwell times with the Generalized Method of Moments

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