Toolset for single-cell trajectory modeling.
Project description
A toolset for the modeling and analysis of single-cell trajectories.
Key Features
Single-Cell Trajectory Analysis: Leverages time-lapse imaging data to construct detailed trajectories of single-cell behavior, capturing changes in morphology and motility.
Morphodynamical State Decomposition: Utilizes data-driven methods to define and analyze cell states based on dynamic cellular features, providing insights into cell state transitions.
Dynamical Modeling: Implements MSMs and Koopman operator-based approaches to kinetically characterize cell state transitions and generate embeddings for visualizing cell dynamics.
Integration with Molecular Data: Maps live-cell imaging data to gene expression profiles, enabling predictions of RNA transcript levels based on cell state dynamics.
Tutorials: Includes jupyter-notebooks with links to Zenodo repositories with downloadable data to guide users through the process of trajectory embedding and MMIST (Molecular and Morphodynamics-Integrated Single-cell Trajectories).
References
Copperman, Jeremy, Sean M. Gross, Young Hwan Chang, Laura M. Heiser, and Daniel M. Zuckerman. “Morphodynamical cell state description via live-cell imaging trajectory embedding.” Communications Biology 6, no. 1 (2023): 484.
Copperman, Jeremy, Ian C. Mclean, Sean M. Gross, Young Hwan Chang, Daniel M. Zuckerman, and Laura M. Heiser. “Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state change.” bioRxiv (2024): 2024-01.
License
Free software: MIT license
Documentation
Tutorials
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