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

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 targeted protein markers per cell at the single-cell level. However, data analysis can be difficult, due to the large size and dimensionality of datasets as well as limitations of existing computational methods. Here, we present diffcyt, a new computational framework for differential discovery analyses in high-dimensional cytometry data, based on a combination of high-resolution clustering and 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.

Abstract

Lukas Weber et al. present diffcyt, an open-source computational framework for differential analysis of high-dimensional flow and mass cytometry data. The diffcyt framework uses high-resolution clustering and empirical Bayes moderated tests to provide improved statistical performance, flexible experimental designs, and fast runtimes.