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Variational Algorithms for Analyzing Noisy Multistate Diffusion Trajectories
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DOI：10.1016/j.bpj.2018.05.027.png)
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摘要
En 中文
Single-particle tracking offers a noninvasive high-resolution probe of biomolecular reactions inside living cells. However, efficient data analysis methods that correctly account for various noise sources are needed to realize the full quantitative potential of the method. We report algorithms for hidden Markov-based analysis of single-particle tracking data, which incorporate most sources of experimental noise, including heterogeneous localization errors and missing positions. Compared to previous implementations, the algorithms offer significant speedups, 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.
Keyword:
SINGLE-MOLECULE TRAJECTORIES
HIDDEN MARKOV MODEL
BAYESIAN-APPROACH
TRACKING
SELECTION
MICROSCOPY
DYNAMICS
STATES
RATES
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期刊
IF:
3.1
论文数:
5.1W
被引数:
4.4W
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NATURE METHODS
NATURE METHODSIF32.1
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