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 proposed by Li et al. (Nature Biotechnology, 2023). RNA velocity reflects the dynamic process of cell state transitions based on scRNA-seq experiment data. cellDancer enables the prediction of RNA velocity and the dynamic kinetics of RNA in single-cell resolution.

cellDancer’s key applications

  • Enable accurate inference of dynamic cell state transitions in heterogeneous cell populations.

key1

Example: Mouse gastrulation erythroid maturation.

  • Estimate cell-specific transcription (α), splicing (β) and degradation (γ) rates for each gene and reveal RNA turnover strategies.

key2

Example: Mouse hippocampus development.

  • Improves downstream analysis such as vector field predictions.

key3

Example: Mouse pancreatic endocrinogenesis.

Latest news

  • Our work of cellDancer has been published at Nature Biotechnology! (4/3/2023)

  • cellDancer has been released to PyPI (3/21/2023).

Support

Welcome bug reports and suggestions to our Github issue page!

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