Code for the paper A systematic evaluation of interneuron morphology representations for cell type discrimination.
All data for the publication figures can be found here
Make sure the following dependecies are installed:
numpy [v1.17.3], pandas[v0.24.0] , scipy[v1.3.1], sklearn[v0.21.3] , matplotlib [v3.0.3] and seaborn[v0.8.1]
Check out the repository via
git clone https://github.com/berenslab/morphology-benchmark.
Download all data from 
data to the location of the repository.
Now you can run all notebooks to generate the published figures.
Since this study has been implemented using DataJoint, it cannot be readily executed. The available notebooks are meant to showcase the processing. The exact code can be found in the folder schemata.
Example code on the computation of density maps, 2D persistence diagrams and morphometric statistics is shown in ROBUSTNESS ANALYSIS data generation.ipynb. The computation of all features can be found in the DataJoint tables schema.density, schema.morphometry and schema.persistence.
@article{laturnus2019systematic,
title={A systematic evaluation of interneuron morphology representations for cell type discrimination},
author={Laturnus, Sophie and Kobak, Dmitry and Berens, Philipp},
journal={bioRxiv},
pages={591370},
year={2019},
publisher={Cold Spring Harbor Laboratory}
}
| page | Original text | Correction |
|---|---|---|
| p.8 | "...e.g. it grew from 0.14 ± 0.06 to 0.17 ± 0.07, mean±95CI across all 21 pairs..." | (Cor)"...e.g. it grew from 0.14 (0.08 - 0.2, mean and 95% CI across all 21 pairs) to 0.17 (0.1 - 0.24)..." |