A Graph-Theoretic Framework for Quantitative Analysis of Angiogenic Networks

This dataset includes some tube formation assay images and a Python-based analysis pipeline for evaluating tube formation microscopy images through graph-theoretic methods. The images are endothelial cell tube formation under different conditions and can been processed into skeletonized forms for graph extraction using the provided script. The accompanying Python script calculates 11 key graph metrics such as node degree, clustering coefficient, global efficiency, tortuosity, network density, connectivity index and radial zone distributions to assess angiogenic network morphology. Other metrics are also included for extra information purposes.

The provided code relies on the following Python packages:

networkx (for graph analysis)

scipy (for spatial and statistical computations)

skimage (for image processing and skeletonization)

matplotlib (for data visualization)

pandas (for data handling and results export)

This dataset allows users to reproduce the analysis pipeline described in our manuscript, visualize the network structure, and quantify the topological features of angiogenic networks. The script is ready to be used with microscopy images of endothelial cell tube formation assays to extract and analyze angiogenic growth patterns.