PT  - JOURNAL ARTICLE
AU  - M. Büttner
AU  - J. Ostner
AU  - CL. Müller
AU  - FJ. Theis
AU  - B. Schubert
TI  - scCODA: A Bayesian model for compositional single-cell data analysis
AID  - 10.1101/2020.12.14.422688
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.12.14.422688
4099  - http://biorxiv.org/content/early/2020/12/15/2020.12.14.422688.short
4100  - http://biorxiv.org/content/early/2020/12/15/2020.12.14.422688.full
AB  - 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 and identified experimentally verified cell type changes that were missed in original analyses.Competing Interest StatementThe authors have declared no competing interest.