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

Abstract

1High-dimensional flow and mass cytometry allow cell types and states to be characterized in great detail by measuring expression levels of more than 40 targeted 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.