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Testing for differential abundance in mass cytometry data


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 ( outperforms other approaches in simulations and finds novel patterns of differential abundance in real data.

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Figure 1: A pipeline to determine differential cell population abundance from mass cytometry data.
Figure 2: Differentially abundant subpopulations in an MEF-reprogramming time course.

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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.

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Authors and Affiliations



A.T.L.L. developed the analysis pipeline, tested it with simulations and applied it to the real data. A.C.R. interpreted the results to identify the DA subpopulations. J.C.M. provided direction and advice on method development and biological interpretation. All authors wrote and approved the final manuscript.

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Correspondence to John C Marioni.

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The authors declare no competing financial interests.

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Supplementary Figures 1–24, Supplementary Table 1 and Supplementary Notes 1–9. (PDF 15317 kb)

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cydar software. (ZIP 190 kb)

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Lun, A., Richard, A. & Marioni, J. Testing for differential abundance in mass cytometry data. Nat Methods 14, 707–709 (2017).

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