Join

Plate IV.265 · Publications

scCODA is a Bayesian model for compositional single-cell data analysis

Article in Nature Communications, 25 November 2021.

Open accessSingle Cell RNA, Gene Expression

  1. M. Büttner
  2. J. Ostner
  3. C. L. Müller
  4. F. J. Theis
  5. B. Schubert

Abstract

Compositional changes of cell types are main drivers of biological processes. Their detection through single-cell experiments is difficult due to the compositionality of the data and low sample sizes. We introduce scCODA (https://github.com/theislab/scCODA), a Bayesian model addressing these issues enabling the study of complex cell type effects in disease, and other stimuli. scCODA demonstrated excellent detection performance, while reliably controlling for false discoveries, and identified experimentally verified cell type changes that were missed in original analyses.

Versions2

  1. PublishedNature Communications10.1038/s41467-021-27150-6
  2. PreprintbioRxiv10.1101/2020.12.14.422688

Cite

@article{buttner2021sccoda,
  title = {{scCODA is a Bayesian model for compositional single-cell data analysis}},
  author = {Büttner, M. and Ostner, J. and Müller, C. L. and Theis, F. J. and Schubert, B.},
  journal = {Nature Communications},
  publisher = {Springer Science and Business Media LLC},
  year = {2021},
  month = nov,
  doi = {10.1038/s41467-021-27150-6},
  url = {https://doi.org/10.1038/s41467-021-27150-6},
  pmid = {34824236},
  pmcid = {PMC8616929}
}
.bib file