Testing for differential abundance in mass cytometry data.

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Abstrakt

Tytuł:: Testing for differential abundance in mass cytometry data.
Autorzy:: Lun ATL; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
Richard AC; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.; Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK.
Marioni JC; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.; EMBL European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK.; Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge, UK.
Źródło:: Nature methods [Nat Methods] 2017 Jul; Vol. 14 (7), pp. 707-709. Date of Electronic Publication: 2017 May 15.
Typ publikacji:: Journal Article
Język:: English
Imprint Name(s):: Original Publication: New York, NY : Nature Pub. Group, c2004-
MeSH Terms:: Software*
Flow Cytometry/*methods
Image Processing, Computer-Assisted/*methods
Computer Simulation
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Grant Information:: 22231 United Kingdom CRUK_ Cancer Research UK
Entry Date(s):: Date Created: 20170516 Date Completed: 20170816 Latest Revision: 20240210
Update Code:: 20240210
PubMed Central ID:: PMC6155493
DOI:: 10.1038/nmeth.4295
PMID:: 28504682
: Czasopismo naukowe

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.

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