scCODA is a Bayesian model for compositional single-cell data analysis
Maren Büttner, Johannes Ostner, Christian L. Müller, Fabian J. Theis, Benjamin Schubert
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.
Topics & Concepts
Bayesian probabilityComputer sciencePrinciple of compositionalityCompositional dataData typeCell typeSample (material)Artificial intelligencePattern recognition (psychology)Data miningCellMachine learningBiologyChemistryGeneticsProgramming languageChromatographySingle-cell and spatial transcriptomicsCell Image Analysis TechniquesMetabolomics and Mass Spectrometry Studies