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cellshape-cluster


Cellshape-cluster is an easy-to-use tool to analyse the cluster cells by their shape using deep learning and, in particular, deep-embedded-clustering. The tool provides the ability to train popular graph-based or convolutional autoencoders on point cloud or voxel data of 3D single cell masks as well as providing pre-trained networks for inference.

To install

pip install cellshape-cluster

Usage

import torch
from cellshape_cloud import CloudAutoEncoder
from cellshape_cluster import DeepEmbeddedClustering

autoencoder = CloudAutoEncoder(
    num_features=128, 
    k=20, 
    encoder_type="dgcnn"
)

model = DeepEmbeddedClustering(autoencoder=autoencoder, 
                               num_clusters=10,
                               alpha=1.0)

points = torch.randn(1, 2048, 3)

recon, features, clusters = model(points)

Parameters

  • autoencoder: CloudAutoEncoder or VoxelAutoEncoder.
    Instance of autoencoder class from cellshape-cloud or cellshape-voxel
  • num_clusters: int.
    The number of clusters to use in deep embedded clustering algorithm.
  • alpha: float.
    Degrees of freedom for the Student's t-distribution. Xie et al. (ICML, 2016) let alpha=1 for all experiments.

For developers

  • Fork the repository
  • Clone your fork
git clone https://github.com/USERNAME/cellshape-cluster 
  • Install an editable version (-e) with the development requirements (dev)
cd cellshape-cluster
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

Metadata

Release files for cellshape-cluster 0.0.3rc0

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