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

By Maren Büttner, J. Ostner, Christian L. Müller, Fabian J Theis, Benjamin Schubert. Nature Communications, 2021

310 citations recorded by the source corpus.

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. Imbalance and loss of cell types is a hallmark in many diseases. Still, quantifying compositional changes in scRNAseq data remains challenging. Here the authors present scCODA, a Bayesian model to assess cell type compositions in scRNA-seq data.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/16f3ad4e30f911f44f98efa0b948bb8cd13303ed

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Citations in the corpus

Best practices for single-cell analysis across modalities (2023, unknown): “3).”
Aitchison’s Compositional Data Analysis 40 Years on: A Reappraisal (2022, background): “Presence of compositional bias in single-cell data was reported in [14].”