ccAFv2 R package

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ccAFv2 is a high-resolution classifier for cell cycle state identification in single-cell RNA-seq (scRNA-seq) data. It was trained on human neural stem cells and classifies six distinct cell cycle states (G1, Late G1, S, S/G2, G2/M, and M/Early G1) along with a Neural G0 state (G0). ccAFv2 incorporates a tunable parameter to refine classification certainty and outperforms existing methods while supporting additional cell cycle states.

Key Applications of ccAFv2:

  • Identify cell cycle states in diverse cell types and germ layers.
  • Classify cells, nuclei, and spatial transcriptomics data across species.
  • Regress cell cycle expression patterns to reveal biological signals.
  • Use as an R package (Seurat integration) or PyPI package (scanpy integration).

Citing ccAFv2

If you include or rely on ccAF2 when publishing research, please adhere to the following citation guide:

Citation for ccAFv2 (version 2):

Classifying cell cycle states and a quiescent-like G0 state using single-cell transcriptomics. Samantha A. O‘Connor, Leonor Garcia, Rori Hoover, Anoop P. Patel, Benjamin B. Bartelle, Jean-Philippe Hugnot, Patrick J. Paddison, Christopher L. Plaisier. bioRxiv [Preprint]. 2024 Apr 20:2024.04.16.589816. doi: 10.1101/2024.04.16.589816. PMID: 38659838

Citation for ccAF (version 1):

Neural G0: a quiescent-like state found in neuroepithelial-derived cells and glioma. Samantha A. O’Connor, Heather M. Feldman, Chad M. Toledo, Sonali Arora, Pia Hoellerbauer, Philip Corrin, Lucas Carter, Megan Kufeld, Hamid Bolouri, Ryan Basom, Jeffrey Delrow, Jose L. McFaline-Figueroa, Cole Trapnell, Steven M. Pollard, Anoop Patel, Patrick J. Paddison, Christopher L. Plaisier. Mol Syst Biol. 2021 Jun;17(6):e9522. doi: 10.15252/msb.20209522. PMID: 34101353

Acknowledgments

Samantha A. O‘Connor developer since 2021, Christopher L. Plaisier developer since 2021, maintainer

This website was developed by Rori Hoover and the Plaisier Lab using Just the Docs.

For other great packages from the Plaisier Lab, please check here.

Support

Found a bug or would like to see a feature implemented? Feel free to submit an issue. Your help to improve ccAF is highly appreciated.