Variational algorithms for analyzing noisy multi-state diffusion trajectories

Single particle tracking offers a non-invasive high-resolution probe of biomolecular reactions inside living cells. However, efficient data analysis methods that correctly account for various noise soures are needed to realize the full quantitative potential of the method. We report new algorithms for hidden Markov based analysis of single particle tracking data, which incorporate most sources of experimental noise, including heterogeneuous localization errors and missing positions. Compared to previous implementations, the algorithms offer significant speed-ups, support for a wider range of inference methods, and a simple user interface. This will enable more advanced and exploratory quantitative analysis of single particle tracking data.