TY - JOUR T1 - <em>propeller</em>: testing for differences in cell type proportions in single cell data JF - bioRxiv DO - 10.1101/2021.11.28.470236 SP - 2021.11.28.470236 AU - Belinda Phipson AU - Choon Boon Sim AU - Enzo Porrello AU - Alex W Hewitt AU - Joseph Powell AU - Alicia Oshlack Y1 - 2021/01/01 UR - http://biorxiv.org/content/early/2021/11/28/2021.11.28.470236.abstract N2 - Single cell RNA Sequencing (scRNA-seq) has rapidly gained popularity over the last few years for profiling the transcriptomes of thousands to millions of single cells. To date, there are more than a thousand software packages that have been developed to analyse scRNA-seq data. These focus predominantly on visualization, dimensionality reduction and cell type identification. Single cell technology is now being used to analyse experiments with complex designs including biological replication. One question that can be asked from single cell experiments which has not been possible to address with bulk RNA-seq data is whether the cell type proportions are different between two or more experimental conditions. As well as gene expression changes, the relative depletion or enrichment of a particular cell type can be the functional consequence of disease or treatment. However, cell type proportions estimates from scRNA-seq data are variable and statistical methods that can correctly account for different sources of variability are needed to confidently identify statistically significant shifts in cell type composition between experimental conditions. We present propeller, a robust and flexible method that leverages biological replication to find statistically significant differences in cell type proportions between groups. The propeller method is publicly available in the open source speckle R package (https://github.com/Oshlack/speckle).Competing Interest StatementThe authors have declared no competing interest. ER -