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

> 商业许可源文 · EUROPE_PMC · [CC-BY](https://creativecommons.org/licenses/by/)

书目信息

引用：Büttner M, Ostner J, Müller CL, Theis FJ, Schubert B. (2021). scCODA is a Bayesian model for compositional single-cell data analysis. Nature communications. PMID 34824236 · PMC8616929 · DOI 10.1038/s41467-021-27150-6

证据类型：PRIMARY_RESEARCH

主题：single-cell

被引次数（采集时）：368

原始记录：[Europe PMC](https://europepmc.org/article/MED/34824236)

来源许可：[CC-BY](https://creativecommons.org/licenses/by/)

作者摘要（按来源许可复用）

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.

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