A Divisive Segmentation and Clustering Scheme for Accelerated and Improved Single-Molecule Time Series Idealization (DiSC)

Abstract

Single-molecule methods offer unprecedented view into the heterogenous dynamics underlying chemical and biological interactions. As their popularity grows, so too does the demand for efficient and accurate methods to reliably extract kinetic parameters from large, often noisy, time series data sets. Conventional methods include change-point analysis and hidden Markov modeling; the former being computationally efficient and parameter free at the cost of lower accuracy, and the latter being the opposite. Here, we introduce DiSC, a new idealization scheme that combines the strengths of both these methods for parameter free acceleration of hidden Markov modeling. DiSC identifies segments and clusters simultaneously in a divisive manner, wherein the growth of each node is controlled by a specified information criterion. The identified clusters and change-points are then used as priors for hidden Markov modeling to improve the overall fit at a fraction of the standard computational cost. We validate the performance of DiSC using simulated data and determine DiSC is both faster and more accurate for state and change-point identification than the standard cutting-edge methods.