CASK: Cellular Analysis of Spatiotemporal K-functions

CASK (Cellular Analysis of Spatiotemporal K-functions) is a repository accompanying the publication "Spatial and temporal signatures of cell competition revealed by K-function analysis". This repository provides Jupyter notebooks for spatiotemporal K-function clustering analysis and single-cell labeling, offering distilled quantitative methods to study the spatiotemporal dynamics of cellular competition.

Key Features

• K-function Analysis: Quantitatively evaluates the spatial and temporal clustering of wild-type cell mitoses relative to mutant cell elimination events.
• Single-Cell Labeling: Includes notebooks for labeling and tracking individual cells to analyze heterogeneity in cellular behaviors.
• Publication-focused: Reproduces key findings from the study.
• Simple Setup: No installation required; explore the included Jupyter notebooks directly.

Included Notebooks

1. Space-time_K-Function_analysis.ipynb: Implements the spatiotemporal K-function clustering analysis.
2. Single-Cell Labeling Notebooks:
   • align.ipynb: Aligns imaging data for accurate cell tracking.
   • stardist_segmentation.ipynb: Segments cell images using the StarDist algorithm.
   • cellx_classify.ipynb: Classifies cell types based on imaging data.
   • btrack_tracking.ipynb: Utilizes Bayesian tracking for single-cell movement analysis.
   • napari_viewer.ipynb: Visualizes cell data using the Napari viewer.

Note: This repository does not include segmentation or tracking tools. Those were performed using bespoke pipelines described in the publication.

Usage

1. Clone the repository:

   git clone https://github.com/nthndy/Cask.git
   cd Cask

2. Navigate to the provided notebooks and explore the analysis.

Citation

If you use this repository, please cite the publication:
Spatial and temporal signatures of cell competition revealed by K-function analysis
Nathan J. Day, Jasmine Michalowska, et al. (2024).

License

This project is licensed under the GNU General Public License v3.0. See the LICENSE file for details.

Acknowledgments

This work was conducted at University College London (UCL). Special thanks to the Lowe and Charras labs for their contributions and support.