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.