Change-in-velocity detection for multidimensional data

TL;DR

This paper presents CPLASS, a novel algorithm for detecting changes in velocity in multidimensional data, addressing challenges of continuity and parameter dependencies with a specialized penalty and MCMC approach, useful for intracellular transport analysis.

Contribution

Introduction of CPLASS, a new method combining a penalty function and MCMC for change-in-velocity detection in multidimensional data, tailored for biophysical applications.

Findings

Effective in analyzing intracellular transport data

Balances model complexity with biophysical realism

Robust to noisy short segments

Abstract

In this work, we introduce CPLASS (Continuous Piecewise-Linear Approximation via Stochastic Search), an algorithm for detecting changes in velocity within multidimensional data. The one-dimensional version of this problem is known as the change-in-slope problem (see Fearnhead & Grose, 2022; Baranowski et al., 2019). Unlike traditional changepoint detection methods that focus on changes in mean, detecting changes in velocity requires a specialized approach due to continuity constraints and parameter dependencies, which frustrate popular algorithms like binary segmentation and dynamic programming. To overcome these difficulties, we introduce a specialized penalty function to balance improvements in likelihood due to model complexity, and a Markov Chain Monte Carlo (MCMC)-based approach with tailored proposal mechanisms for efficient parameter exploration. Our method is particularly suited for analyzing intracellular transport data, where the multidimensional trajectories of microscale cargo are driven by teams of molecular motors that undergo complex biophysical transitions. To ensure biophysical realism in the results, we introduce a speed penalty that discourages overfitted of short noisy segments while maintaining consistency in the large-sample limit. Additionally, we introduce a summary statistic called the Cumulative Speed Allocation, which is robust with respect to idiosyncracies of changepoint detection while maintaining the ability to discriminate between biophysically distinct populations.