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.
【저자키워드】 transcriptomics, Statistical methods, 【초록키워드】 Diseases, Bayesian, experiment, disease, change, single-cell, cell type, Imbalance, biological processes, complex, hallmark, scRNA-seq data, sample sizes, scRNAseq, Effect, Cell, changes in, demonstrated, analyses, driver, Still, 【제목키워드】 Bayesian, Data analysis, single-cell,