https://github.com/SjoerdVBeentjes/ripleyk
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Tip revision: 1303af539403303786b6460fabef355e345ea6c9 authored by Sjoerd Beentjes on 04 February 2022, 01:45:38 UTC
Jupyter Notebook
Tip revision: 1303af5
README.md
## RipleyK

Jupyter Notebook to estimate Ripley's K statistic for a given number of 3D data points and assess if data are clustered/dispersed at a given scale r more than expected by chance, relative to N uniformly distributed data points.

- The input is a csv file with (x,y,z) coordinates of the data points, as well as a list of radii
- The output is a list of p-values, one for each radius, indicating significance of clustering

This approach has been applied in [Purshouse et al. (2022)](https://www.biorxiv.org/content/10.1101/2022.01.29.478046v1) to investige (non-)clustering of ecDNAs in patient-derived glioblastoma cell lines.
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