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Testing for differential abundance in mass cytometry data.

Accepted version
Peer-reviewed

Type

Article

Change log

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

Volume Title

14

Publisher

Nature Publishing Group
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.