Maximum likelihood estimation in hidden Markov models with inhomogeneous noise

Diehn, Manuel; Munk, Axel; Rudolf, Daniel

doi:10.1051/ps/2018017

Cited by 7 publications

(7 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is wide agreement that, except in few counterexamples (Fuliński et al 1998;Mercik and Weron 2001;Goychuk et al 2005;Shelley et al 2010), the or small holes in the membrane, or because of additional high-frequency f 2 (violet) and long-tailed 1/f (pink) noise components, see for instance (Neher and Sakmann 1976;Venkataramanan et al 1998;Levis and Rae 1993). Hence, HMM-based analyses often rely on intensive preprocessing or on more complicated models: for instance, (Venkataramanan et al 1998) assumed an HMM that allows additional colored noise, and (Diehn et al 2019) provided modifications to incorporate inhomogeneous errors. Moreover, low-pass filtering often requires further, computationally demanding extensions, see for instance (Venkataramanan et al 1998;de Gunst et al 2001;Diehn 2017;Almanjahie et al 2019).…”

Section: Interplay Between Model-free Idealizations and Hidden Markov Modelsmentioning

confidence: 99%

“…We limit our discussion mostly to homogeneous HMMs, which means that the parameters, which describe state transition properties and noise distribution, are constant in time. Inhomogeneous HMMs, see, for instance, (Diehn et al 2019), are rarely used, as they are computationally more challenging and theoretical guarantees for parameter estimates are much harder to prove. As JULES detects short events, but finds many small events, which are most likely false positives, at areas of high conductance and high var-iance (see, for instance, the idealization of the observations around 0.36 nS in the middle panel).…”

Section: Hmm-based Analysismentioning

confidence: 99%

See 1 more Smart Citation

Analysis of patchclamp recordings: model-free multiscale methods and software

2021

Self Cite

View full text Add to dashboard Cite

Analysis of patchclamp recordings is often a challenging issue. We give practical guidance how such recordings can be analyzed using the model-free multiscale idealization methodology JSMURF, JULES, and HILDE. We provide an operational manual how to use the accompanying software available as an R-package and as a graphical user interface. This includes selection of the right approach and tuning of parameters. We also discuss advantages and disadvantages of model-free approaches in comparison to hidden Markov model approaches and explain how they complement each other.

show abstract

Section: Interplay Between Model-free Idealizations and Hidden Markov Modelsmentioning

confidence: 99%

Section: Hmm-based Analysismentioning

confidence: 99%

Analysis of patchclamp recordings: model-free multiscale methods and software

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Various extensions of HMMs have been introduced, see e.g. (Sin and Kim, 1995;Mari et al, 1997;Fine et al, 1998;Guan et al, 2016;Siekmann et al, 2016;Diehn et al, 2019). Most related to our setting are factorial HMMs (Ghahramani and Jordan, 1997;, that consider several independent unobservable chains.…”

Section: Related Workmentioning

confidence: 99%

Analyzing cross-talk between superimposed signals: Vector norm dependent hidden Markov models and applications

Vanegas¹,

Eltzner²,

Rudolf³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

We propose and investigate a hidden Markov model (HMM) for the analysis of aggregated, super-imposed two-state signal recordings. A major motivation for this work is that often these recordings cannot be observed individually but only their superposition. Among others, such models are in high demand for the understanding of cross-talk between ion channels, where each single channel might take two different states which cannot be measured separately. As an essential building block we introduce a parametrized vector norm dependent Markov chain model and characterize it in terms of permutation invariance as well as conditional independence. This leads to a hidden Markov chain "sum" process which can be used for analyzing aggregated two-state signal observations within a HMM. Additionally, we show that the model parameters of the vector norm dependent Markov chain are uniquely determined by the parameters of the "sum" process and are therefore identifiable. Finally, we provide algorithms to estimate the parameters and apply our methodology to real-world ion channel data measurements, where we show competitive gating.

show abstract

“…Methods for open channel noise: Idealization methodology can be divided into so called model-free methods [5]- [7], [16], [17] which do not rely on a specific model for the gating dynamics, to methodology based on hidden Markov models (HMM) [18]- [23] and to current distribution fitting [13], [24]- [26]. The latter often assume a hidden Markov model as well but focus on parameter estimation directly.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Idealization of Ion Channel Recordings – Open Channel Noise

Pein

Bartsch

Steinem

et al. 2021

IEEE Trans.on Nanobioscience

Self Cite

View full text Add to dashboard Cite

We propose a new model-free segmentation method for idealizing ion channel recordings. This method is designed to deal with heterogeneity of measurement errors. This in particular applies to open channel noise which, in general, is particularly difficult to cope with for model-free approaches. Our methodology is able to deal with lowpass filtered data which provides a further computational challenge. To this end we propose a multiresolution testing approach, combined with local deconvolution to resolve the lowpass filter. Simulations and statistical theory confirm that the proposed idealization recovers the underlying signal very accurately at presence of heterogeneous noise, even when events are shorter than the filter length. The method is compared to existing approaches in computer experiments and on real data. We find that it is the only one which allows to identify openings of the PorB porine at two different temporal scales. An implementation is available as an R package.

show abstract

Maximum likelihood estimation in hidden Markov models with inhomogeneous noise

Cited by 7 publications

References 29 publications

Analysis of patchclamp recordings: model-free multiscale methods and software

Analysis of patchclamp recordings: model-free multiscale methods and software

Analyzing cross-talk between superimposed signals: Vector norm dependent hidden Markov models and applications

Heterogeneous Idealization of Ion Channel Recordings – Open Channel Noise

Contact Info

Product

Resources

About