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

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...

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Detalles Bibliográficos
Autores principales: Büttner, M., Ostner, J., Müller, C. L., Theis, F. J., Schubert, B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8616929/
https://www.ncbi.nlm.nih.gov/pubmed/34824236
http://dx.doi.org/10.1038/s41467-021-27150-6
Descripción
Sumario: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.