Heterogeneous Idealization of Ion Channel Recordings -- Open Channel Noise

TL;DR

This paper introduces a novel, model-free segmentation method for ion channel recordings that effectively handles heterogeneity and lowpass filtering, improving accuracy in detecting channel events across multiple temporal scales.

Contribution

The authors develop a multiresolution testing approach combined with local deconvolution to accurately idealize ion channel data with heterogeneous noise and filtering effects, a significant advancement over existing methods.

Findings

Accurately recovers underlying signals despite heterogeneity and filtering.

Outperforms existing methods in simulations and real data.

Successfully identifies channel openings at multiple temporal scales.

Abstract

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.