Moving average filtering with deconvolution (MAD) for hidden Markov model with filtering and correlated noise.
Correlated noise
Deconvolution
EM algorithm
Filter approximation
Hidden Markov models
Level-dependent noise
MscL
Parameter estimation
Patch clamp
Journal
European biophysics journal : EBJ
ISSN: 1432-1017
Titre abrégé: Eur Biophys J
Pays: Germany
ID NLM: 8409413
Informations de publication
Date de publication:
May 2019
May 2019
Historique:
received:
06
05
2018
accepted:
22
04
2019
revised:
14
02
2019
pubmed:
28
4
2019
medline:
14
6
2019
entrez:
28
4
2019
Statut:
ppublish
Résumé
Ion channel data recorded using the patch clamp technique are low-pass filtered to remove high-frequency noise. Almanjahie et al. (Eur Biophys J 44:545-556, 2015) based statistical analysis of such data on a hidden Markov model (HMM) with a moving average adjustment for the filter but without correlated noise, and used the EM algorithm for parameter estimation. In this paper, we extend their model to include correlated noise, using signal processing methods and deconvolution to pre-whiten the noise. The resulting data can be modelled as a standard HMM and parameter estimates are again obtained using the EM algorithm. We evaluate this approach using simulated data and also apply it to real data obtained from the mechanosensitive channel of large conductance (MscL) in Escherichia coli. Estimates of mean conductances are comparable to literature values. The key advantages of this method are that it is much simpler and computationally considerably more efficient than currently used HMM methods that include filtering and correlated noise.
Identifiants
pubmed: 31028435
doi: 10.1007/s00249-019-01368-1
pii: 10.1007/s00249-019-01368-1
doi:
Substances chimiques
Escherichia coli Proteins
0
Ion Channels
0
MscL protein, E coli
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
383-393Subventions
Organisme : National Health and Medical Research Council
ID : APP1047980
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