Single Molecule Data Analysis: An Introduction

Tavakoli, Meysam; Taylor, J. Nicholas; Li, Chun‐Biu; Komatsuzaki, Tamiki; Pressé, Steve

doi:10.1002/9781119324560.ch4

Cited by 39 publications

(98 citation statements)

References 288 publications

(579 reference statements)

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“…We review both frequentist and Bayesian inference methods (sections 3.2.1 and 3.2.2) and refer the reader to a recent review for information theoretic inference schemes. 59 …”

Section: Data-driven Modeling: Key Conceptsmentioning

confidence: 99%

“…Here we review two approaches to parameter inference, frequentist or Bayesian, and we refer the reader to Tavakoli et al 59 and Pressé et al 113 for information theoretic approaches.…”

Section: Data-driven Modeling: Key Conceptsmentioning

confidence: 99%

“…While the derivation of the AIC is involved, 59,114 the AIC expression itself is simple

AIC false(M, boldD false) = - 2 \log p (boldD false| \overset{θ}{true}, M) + 2 K

where

\overset{θ}{true}

denotes the MLE of a given model.…”

Section: Data-driven Modeling: Key Conceptsmentioning

confidence: 99%

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Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis

Lee

Tsekouras

Calderon³

et al. 2017

Chem. Rev.

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141

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Super-resolution microscopy provides direct insight into fundamental biological processes occurring at length scales smaller than light’s diffraction limit. The analysis of data at such scales has brought statistical and machine learning methods into the mainstream. Here we provide a survey of data analysis methods starting from an overview of basic statistical techniques underlying the analysis of super-resolution and, more broadly, imaging data. We subsequently break down the analysis of super-resolution data into four problems: the localization problem, the counting problem, the linking problem, and what we’ve termed the interpretation problem.

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“…We review both frequentist and Bayesian inference methods (sections 3.2.1 and 3.2.2) and refer the reader to a recent review for information theoretic inference schemes. 59 …”

Section: Data-driven Modeling: Key Conceptsmentioning

confidence: 99%

“…Here we review two approaches to parameter inference, frequentist or Bayesian, and we refer the reader to Tavakoli et al 59 and Pressé et al 113 for information theoretic approaches.…”

Section: Data-driven Modeling: Key Conceptsmentioning

confidence: 99%

See 1 more Smart Citation

Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis

Lee

Tsekouras

Calderon³

et al. 2017

Chem. Rev.

Self Cite

141

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“…However, we cannot simply maximize our likelihood, since, as is well known, likelihood maximization would overfit the data by favoring too many steps in the photobleaching time trace (Kalafut and Visscher, 2008). Typically, to prevent overfitting, model selection criteria—including the SIC (Schwarz, 1978; Cavanaugh and Neath, 1999), the Akaike information criterion (AIC; Akaike, 1974), and others (Kadane and Lazar, 2004; reviewed most recently in Tavakoli et al. , 2016)—compare different models on the basis of 1) their fit to the data and 2) the number of parameters (i.e., the complexity) of the model (Claeskens and Hjort, 2008; Tavakoli et al.…”

Section: Methodsmentioning

confidence: 99%

“…The idea behind model averaging is simple: the likelihood function depends on a number of parameters (models), and, by averaging over these models, we account for models that are both good and bad fits to the data. Fundamentally, this process penalizes complexity by weighing into consideration models that are poor fits to the data (Schwarz, 1978; Tavakoli et al. , 2016).…”

Section: Methodsmentioning

confidence: 99%

A novel method to accurately locate and count large numbers of steps by photobleaching

Tsekouras

Custer

Jashnsaz

et al. 2016

MBoC

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112

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ICON: An Adaptation of Infinite HMMs for Time Traces with Drift

Sgouralis

Pressé

2017

Biophysical Journal

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Bayesian nonparametric methods have recently transformed emerging areas within data science. One such promising method, the infinite hidden Markov model (iHMM), generalizes the HMM that itself has become a workhorse in single molecule data analysis. The iHMM goes beyond the HMM by self-consistently learning all parameters learned by the HMM in addition to learning the number of states without recourse to any model selection steps. Despite its generality, simple features (such as drift), common to single molecule time traces, result in an overinterpretation of drift and the introduction of artifact states. Here we present an adaptation of the iHMM that can treat data with drift originating from one or many traces (e.g., Förster resonance energy transfer). Our fully Bayesian method couples the iHMM to a continuous control process (drift) self-consistently learned while learning all other quantities determined by the iHMM (including state numbers). A key advantage of this method is that all traces-regardless of drift or states visited across traces-may now be treated on an equal footing, thereby eliminating user-dependent trace selection (based on drift levels), preprocessing to remove drift, and postprocessing model selection based on state number.

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Single Molecule Data Analysis: An Introduction

Cited by 39 publications

References 288 publications

Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis

Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis

A novel method to accurately locate and count large numbers of steps by photobleaching

ICON: An Adaptation of Infinite HMMs for Time Traces with Drift

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