Testing for differential abundance in mass cytometry data
Lun, A.T.L., Richard, A.C., Marioni, J.C.
When comparing biological conditions using mass cytometry data, a key challenge is to identify cellular populations that change in abundance. Here, we present a computational strategy for detecting 'differentially abundant' populations by assigning cells to hyperspheres, testing for significant differences between conditions and controlling the spatial false discovery rate. Our method (http://bioconductor.org/packages/cydar) outperforms other approaches in simulations and finds novel patterns of differential abundance in real data.
Lun, A.T.L., Richard, A.C., Marioni, J.C. "Testing for differential abundance in mass cytometry data" Nature Methods (2017): 707–9