celldancer 1.1.4

Study RNA velocity through neural network.

cellDancer - Estimating Cell-dependent RNA Velocity

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.

cellDancer’s key applications

• Estimate cell-specific RNA velocity for each gene.

• Derive cell fates in embedding space.

• Estimate pseudotime for each cell in embedding space.

What’s new

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 and celldancer.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().

Installation

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'

The dependencies could also be installed by pip install -r requirements.txt.