Unsupervised selection of optimal single-molecule time series idealization criterion

Area of Science:

• Biophysics
• Computational Biology
• Data Science

Background:

• Single-molecule (SM) techniques yield crucial mechanistic insights in biophysics.
• Analyzing large SM datasets requires efficient, unsupervised methods for identifying specific molecular behaviors.
• Existing unsupervised algorithms like divisive segmentation and clustering (DISC) require user-defined parameters.

Purpose of the Study:

• To investigate the impact of different objective criteria (OCs) on DISC algorithm performance for SM fluorescence data.
• To develop an automated method for selecting the optimal OC for DISC based on time series characteristics.
• To improve the analysis of high-throughput SM datasets with noisy signals and variable time windows.

Main Methods:

• Exploration of various objective criteria (OCs) within the DISC algorithm.
• Application of machine learning to establish a decision boundary for OC selection.
• Development of the AutoDISC approach for unsupervised, per-molecule optimization of DISC.

Main Results:

• Different OCs optimize DISC performance based on signal-to-noise ratio and data length.
• A machine learning-derived decision boundary enables unsupervised OC selection.
• AutoDISC effectively optimizes DISC for SM fluorescence data with varying properties.

Conclusions:

• AutoDISC facilitates unsupervised optimization of the DISC algorithm for SM data analysis.
• This method enhances the rapid analysis of large, complex SM fluorescence datasets.
• AutoDISC addresses challenges posed by noisy data and non-uniform time windows in SM experiments.