Joint Detection of Change Points in Multichannel Single-Molecule Measurements

Recent developments in single-molecule measurement technology have expanded the capability to measure multiple parameters. These emergent modalities provide more holistic observations of complex biomolecular processes and call for new analysis methods to detect state changes in multichannel data. Here we develop an algorithm called MULLR (MUlti-channel Log-Likelihood Ratio test) to jointly identify change points in multichannel single-molecule measurements. MULLR is an extension of the popular single-channel implementation for change point detection based on a binary segmentation and log-likelihood ratio test framework. We validate the algorithm on simulated data and characterize the power of detection and false positive rate. We show that MULLR can identify change points in experimental multichannel data and naturally works with different noise statistics and time resolutions across channels. Further, we quantify the benefit of MULLR compared to single-channel analysis. We envision that the MULLR algorithm will be useful to a range of multiparameter single-molecule measurements.