• 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).

• 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.

Free software: MIT license

• celltraj documentation

• Morphodynamical Trajectory Embedding Tutorial
• MMIST: Molecular and Morphodynamics-Integrated Single-cell Trajectories Tutorial

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.