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Schlagwörter:
IEEE Keywords: Hidden Markov models; Ions; Deconvolution; Markov processes; Data models; Nanobioscience; Signal resolution;
INSPEC: Controlled Indexing: deconvolution; hidden Markov models; statistical analysis
INSPEC: Non-Controlled Indexing: filtered ion channel recordings; flickering event detection; model-free segmentation method; local deconvolution; multiresolution criterion; sampling rate; flickering events; temporal scales; filter frequency; deconvolution step; precise determination; dwell times; amplitude levels; common thresholding methods; comprehensive simulation study; preprocessing method; refined hidden Markov analysis; slow gating events; R function jules; JULES; statistical multiresolution techniques; fully automatic multiresolution idealization
Author Keywords: Amplitude reconstruction; deconvolution; dynamic programming; gramicidin A; inverse problems; m-dependency; model-free; peak detection; planar patch clamp; statistical multiresolution criterion
Zusammenfassung:
We propose a new model-free segmentation method, JULES, which combines recent statistical multiresolution techniques with local deconvolution for idealization of ion channel recordings. The multiresolution criterion takes into account scales down to the sampling rate enabling the detection of flickering events, i.e., events on small temporal scales, even below the filter frequency. For such small scales the deconvolution step allows for a precise determination of dwell times and, in particular, of amplitude levels, a task which is not possible with common thresholding methods. This is confirmed theoretically and in a comprehensive simulation study. In addition, JULES can be applied as a preprocessing method for a refined hidden Markov analysis. Our new methodolodgy allows us to show that gramicidin A flickering events have the same amplitude as the slow gating events. JULES is available as an R function jules in the package clampSeg.
