Testing for differential abundance in mass cytometry data.
Nature Publishing Group
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Lun, A., Richard, A., & Marioni, J. (2017). Testing for differential abundance in mass cytometry data.. Nature Methods, 14 (7), 707-709. https://doi.org/10.1038/nmeth.4295
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.
Computer Simulation, Flow Cytometry, Image Processing, Computer-Assisted, Software
This work was supported by Cancer Research UK (core funding to J.C.M., award no. A17197), the University of Cambridge and Hutchison Whampoa Limited. J.C.M. was also supported by core funding from EMBL.
Cancer Research UK (C14303/A17197)
External DOI: https://doi.org/10.1038/nmeth.4295
This record's URL: https://www.repository.cam.ac.uk/handle/1810/274619