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

Sgouralis, Ioannis; Pressé, Steve

doi:10.1016/j.bpj.2017.04.009

Cited by 44 publications

(79 citation statements)

References 39 publications

(57 reference statements)

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“…In particular, analyzing data derived from mobile biomolecules within an illuminated confocal region breaks down the perennial parametric Bayesian paradigm that has been the workhorse of biophysical data analysis [16,44,55,60,70,92,96]. We argue here that BNPs-which provide principled extensions of Bayesian logic [31,93]-show promise in Biophysics [46,55,87,88,90,92] and give us a working solution to prior parametric challenges.…”

Section: Discussionmentioning

confidence: 99%

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Pitching single-focus confocal data analysis one photon at a time with Bayesian nonparametrics

Tavakoli

Jazani

Sgouralis

et al. 2019

Preprint

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Fluorescence time traces are used to report on dynamical properties of biomolecules. The basic unit of information drawn from these traces is the individual photon. Single photons carry instantaneous information from the biomolecule, from which they are emitted, to the detector on timescales as fast as microseconds. Thus from confocal microscope it is theoretically possible to monitor biomolecular dynamics at such timescales. In practice, however, signals are stochastic and in order to deduce dynamical information through traditional means-such as fluorescence correlation spectroscopy (FCS) and related techniques-fluorescence signals are collected and temporally auto-correlated over several minutes. So far, it has been impossible to analyze dynamical properties of biomolecules on timescales approaching data acquisition as this demands that we estimate the instantaneous number of biomolecules emitting photons and their positions within the confocal volume. The mathematical structure of this problem demands that we abandon the normal ("parametric") Bayesian paradigm. Here, we exploit novel mathematical tools, namely Bayesian nonparametrics, that allow us to deduce in a principled fashion the same information normally deduced from FCS but from the direct analysis of significantly smaller datasets starting from individual photon arrivals. We discuss the implications of this method in helping dramatically reduce phototoxic damage on the sample and the ability to monitor out-of-equilibrium processes.

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Section: Discussionmentioning

confidence: 99%

“…While BNPs have had a deep impact on Data Sci-ence since their inception, they are relatively new to Biophysics with a handful of papers [18,47,73] published to date using BNPs in Biophysical applications [50,55,[87][88][89][90]92].…”

Section: Introductionmentioning

confidence: 99%

Pitching single-focus confocal data analysis one photon at a time with Bayesian nonparametrics

Tavakoli

Jazani

Sgouralis

et al. 2019

Preprint

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“…arrivals. The underlying theory, Bayesian nonparametrics (BNPs) [40], is a powerful set of tools still under active development and largely unknown to the physical sciences [4,39,[41][42][43][44][45][46][47][48].…”

Section: Fig 2 Estimates Of Diffusion Coefficients From Photon Arrivalmentioning

confidence: 99%

Pitching Single-Focus Confocal Data Analysis One Photon at a Time with Bayesian Nonparametrics

et al. 2020

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Fluorescence time traces are used to report on dynamical properties of molecules. The basic unit of information in these traces is the arrival time of individual photons, which carry instantaneous information from the molecule, from which they are emitted, to the detector on timescales as fast as microseconds. Thus, it is theoretically possible to monitor molecular dynamics at such timescales from traces containing only a sufficient number of photon arrivals. In practice, however, traces are stochastic and in order to deduce dynamical information through traditional means-such as fluorescence correlation spectroscopy (FCS) and related techniques-they are collected and temporally autocorrelated over several minutes. So far, it has been impossible to analyze dynamical properties of molecules on timescales approaching data acquisition without collecting long traces under the strong assumption of stationarity of the process under observation or assumptions required for the analytic derivation of a correlation function. To avoid these assumptions, we would otherwise need to estimate the instantaneous number of molecules emitting photons and their positions within the confocal volume. As the number of molecules in a typical experiment is unknown, this problem demands that we abandon the conventional analysis paradigm. Here, we exploit Bayesian nonparametrics that allow us to obtain, in a principled fashion, estimates of the same quantities as FCS but from the direct analysis of traces of photon arrivals that are significantly smaller in size, or total duration, than those required by FCS.

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“…We found that cc-EM correctly predicts the number of states in 91% of cases, given a higher robustness to false positive detection than the MDL algorithm in (40), and with much less estimate errors as can be seen with the RMSE boxplots 5. We also compared our proposed cc-EM algorithm to a Bayesian non parametric approach using the xl-ICON framework (65) to assess their specificity wrt false positive event detection and their robustness to overfitting under mixed noises (AWGN+pink+shot). To do so, we simulated a single fake state with 100 randomly positioned impulses simulating fake events, and sequentially corrupted the resulting trace by both AWGN and pink noises, given a SNR of 1dB, which emphasizes the magnitude of the transitions by the time varying noise variance, and thus increasing the risk of detecting false positive short-lived states.…”

Section: F3 Robustness To the Sensor Baseline Driftmentioning

confidence: 99%

Unsupervised Idealization of Nano-Electronic Sensors Recordings with Concept Drifts: A Compressive Feature Learning Approach for Non-Stationary Single-Molecule Data Analysis

Ouqamra

2020

Preprint

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Single-molecule nanocircuits based on field-effect transistors (smFETs) are emerging and promising nano-bioelectronic sensors for the functional detection of molecular dynamics involved in biochemical transformations, in particular for applications in cancer thanks to a potentially better understanding of some hidden and complex molecular interactions. In fact, functionalized carbon nanotubes have been recently exploited to probe molecular events occurring at a single molecule scale with ultra high sensitivity and specificity, such as nucleic acids hybridization, enzyme folding in catalysis reactions, or protein-nucleic acids interactions. Extracting the kinetics and thermodynamics from such single-molecule dynamics implies robust analytic tools that can handle the complexity of the sensed reaction system changing between transient and steady-state molecular conformations, but also some challenging signal specificities, such as the multi-source composition of the recorded signals, the mixed and high-level noises, and the sensor baseline drift, leading to non-stationary time series. We present a new smFET data analysis framework, based on a compressive feature learning scheme to optimize unsupervised idealization of smFET traces, by a precise and accurate molecular events detection and states characterization algorithm, tailored for non-stationary signals at high sampling rate and long acquisition periods, without any prior knowledge on the data generating process nor signal prefiltering. Experimental results show the accuracy and robustness of our trace idealization algorithm to stochastic state-space models, and better performances than commonly used hidden Markov models.

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

Cited by 44 publications

References 39 publications

Pitching single-focus confocal data analysis one photon at a time with Bayesian nonparametrics

Pitching single-focus confocal data analysis one photon at a time with Bayesian nonparametrics

Pitching Single-Focus Confocal Data Analysis One Photon at a Time with Bayesian Nonparametrics

Unsupervised Idealization of Nano-Electronic Sensors Recordings with Concept Drifts: A Compressive Feature Learning Approach for Non-Stationary Single-Molecule Data Analysis

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