Cell shape analysis using graph neural networks
Project description
Cellshape-cloud is an easy-to-use tool to analyse the shapes of cells using deep learning and, in particular, graph-neural networks. The tool provides the ability to train popular graph-based autoencoders on point cloud data of 2D and 3D single cell masks as well as providing pre-trained networks for inference.
To install
pip install cellshape-cloud
Usage
import torch
from cellshape_cloud import CloudAutoEncoder
model = CloudAutoEncoder(num_features=128,
k=20,
encoder_type="dgcnn",
decoder_type="foldingnet")
points = torch.randn(1, 2048, 3)
recon, features = model(points)
Parameters
num_features
: int.
The size of the latent space of the autoencoder. If you have rectangular images, make sure your image size is the maximum of the width and heightk
: int.
The number of neightbours to use in the k-nearest-neighbours graph construction.encoder_type
: str.
The type of encoder: 'foldingnet' or 'dgcnn'decoder_type
: str.
The type of decoder: 'foldingnet' or 'dgcnn'
For developers
- Fork the repository
- Clone your fork
git clone https://github.com/USERNAME/cellshape-cloud
- Install an editable version (
-e
) with the development requirements (dev
)
cd cellshape-cloud
pip install -e .[dev]
- To install pre-commit hooks to ensure formatting is correct:
pre-commit install
- To release a new version:
Firstly, update the version with bump2version (bump2version patch
,
bump2version minor
or bump2version major
). This will increment the
package version (to a release candidate - e.g. 0.0.1rc0
) and tag the
commit. Push this tag to GitHub to run the deployment workflow:
git push --follow-tags
Once the release candidate has been tested, the release version can be created with:
bump2version release
References
[1] An Tao, 'Unsupervised Point Cloud Reconstruction for Classific Feature Learning', GitHub Repo, 2020
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