Testing for differential abundance in mass cytometry data.
Accepted version
Peer-reviewed
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Repository DOI
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Authors
Lun, Aaron TL
Richard, Arianne C
Marioni, John C
Abstract
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.
Description
Keywords
Computer Simulation, Flow Cytometry, Image Processing, Computer-Assisted, Software
Journal Title
Nature Methods
Conference Name
Journal ISSN
1548-7091
1548-7105
1548-7105
Volume Title
14
Publisher
Nature Publishing Group
Publisher DOI
Sponsorship
Cancer Research UK (C14303/A17197)
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.