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Fast Step Transition and State Identification (STaSI) for Discrete Single-Molecule Data Analysis.


ABSTRACT: We introduce a step transition and state identification (STaSI) method for piecewise constant single-molecule data with a newly derived minimum description length equation as the objective function. We detect the step transitions using the Student's t test and group the segments into states by hierarchical clustering. The optimum number of states is determined based on the minimum description length equation. This method provides comprehensive, objective analysis of multiple traces requiring few user inputs about the underlying physical models and is faster and more precise in determining the number of states than established and cutting-edge methods for single-molecule data analysis. Perhaps most importantly, the method does not require either time-tagged photon counting or photon counting in general and thus can be applied to a broad range of experimental setups and analytes.

SUBMITTER: Shuang B 

PROVIDER: S-EPMC4167035 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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Fast Step Transition and State Identification (STaSI) for Discrete Single-Molecule Data Analysis.

Shuang Bo B   Cooper David D   Taylor J Nick JN   Kisley Lydia L   Chen Jixin J   Wang Wenxiao W   Li Chun Biu CB   Komatsuzaki Tamiki T   Landes Christy F CF  

The journal of physical chemistry letters 20140828 18


We introduce a step transition and state identification (STaSI) method for piecewise constant single-molecule data with a newly derived minimum description length equation as the objective function. We detect the step transitions using the Student's <i>t</i> test and group the segments into states by hierarchical clustering. The optimum number of states is determined based on the minimum description length equation. This method provides comprehensive, objective analysis of multiple traces requir  ...[more]

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