scCODA is a Bayesian model for compositional single-cell data analysis.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 34824236.
- Also identified by DOI 10.1038/s41467-021-27150-6 and PMC identifier 8616929.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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.
Medical subject headings
- Single-Cell Analysis