cellDancer is a modularized, parallelized, and scalable tool based on a deep learning framework for the RNA velocity analysis of scRNA-seq. Our website of tutorials is available at cellDancer Website.
Cite
Shengyu Li#, Pengzhi Zhang#, Weiqing Chen, Lingqun Ye, Kristopher W. Brannan, Nhat-Tu Le, Jun-ichi Abe, John P. Cooke, Guangyu Wang. A relay velocity model infers cell-dependent RNA velocity. Nature Biotechnology (2023) https://doi.org/10.1038/s41587-023-01728-5
- Estimate cell-specific RNA velocity for each gene.
- Derive cell fates in embedding space.
- Estimate pseudotime for each cell in embedding space.
cellDancer is updated to v1.1.4
- Released cellDancer at PyPI. Mainly updated requirements.txt and setup.py.
cellDancer is updated to v1.1.3
- Added
celldancer.utilities.to_dynamo
andcelldancer.utilities.export_velocity_to_dynamo
to import cellDancer results to dynamo. - Added deep learning parameters n_neighbors, dt, and learning_rate in function
cellDancer.velocity()
. - Added new loss function: mix, rmse in function
cellDancer.velocity()
.
cellDancer requires Python version >= 3.7.6 to run.
To run cellDancer locally, create an conda or Anaconda environment as conda create -n cellDancer python==3.7.6
, and activate the new environment with conda activate cellDancer
. cellDancer could be installed with pip install celldancer
.
To install cellDancer from source code, run:
pip install 'your_path/Source Code/cellDancer'
.
For M1 Mac users if you encountered a problem while installing bezier. Please refer to the following link: https://bezier.readthedocs.io/en/2021.2.12/#installing
If any other dependency could not be installed with pip install celldancer
, try pip install --no-deps celldancer
. Then install the dependencies by pip install -r requirements.txt
.