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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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:
Article
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
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author Büttner, M.
Ostner, J.
Müller, C. L.
Theis, F. J.
Schubert, B.
author_facet Büttner, M.
Ostner, J.
Müller, C. L.
Theis, F. J.
Schubert, B.
author_sort Büttner, M.
collection PubMed
description 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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spelling pubmed-86169292021-12-01 scCODA is a Bayesian model for compositional single-cell data analysis Büttner, M. Ostner, J. Müller, C. L. Theis, F. J. Schubert, B. Nat Commun Article 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. Nature Publishing Group UK 2021-11-25 /pmc/articles/PMC8616929/ /pubmed/34824236 http://dx.doi.org/10.1038/s41467-021-27150-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Büttner, M.
Ostner, J.
Müller, C. L.
Theis, F. J.
Schubert, B.
scCODA is a Bayesian model for compositional single-cell data analysis
title scCODA is a Bayesian model for compositional single-cell data analysis
title_full scCODA is a Bayesian model for compositional single-cell data analysis
title_fullStr scCODA is a Bayesian model for compositional single-cell data analysis
title_full_unstemmed scCODA is a Bayesian model for compositional single-cell data analysis
title_short scCODA is a Bayesian model for compositional single-cell data analysis
title_sort sccoda is a bayesian model for compositional single-cell data analysis
topic Article
url 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
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