diffcyt: Differential discovery in high-dimensional cytometry via high-resolution clustering

1 Abstract

High-dimensional flow and mass cytometry allow cell types and states to be characterized in great detail by measuring expression levels of more than 40 protein markers per cell. Here we present diffcyt, a new computational framework for differential discovery analyses in these datasets, based on (i) high-resolution clustering and (ii) empirical Bayes moderated tests adapted from transcriptomics. Our approach provides improved statistical performance, including for rare cell populations, along with flexible experimental designs and fast runtimes in an open-source framework.