Removing unwanted variation with CytofRUV to integrate multiple CyTOF datasets.

Autor:	Trussart M; Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.; School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia., Teh CE; The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.; Department of Medical Biology, The University of Melbourne, Parkville, Australia., Tan T; The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.; Department of Medical Biology, The University of Melbourne, Parkville, Australia., Leong L; Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.; School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia., Gray DH; The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.; Department of Medical Biology, The University of Melbourne, Parkville, Australia., Speed TP; Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.; School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia.
Jazyk:	angličtina
Zdroj:	ELife [Elife] 2020 Sep 07; Vol. 9. Date of Electronic Publication: 2020 Sep 07.
DOI:	10.7554/eLife.59630
Abstrakt:	Mass cytometry (CyTOF) is a technology that has revolutionised single-cell biology. By detecting over 40 proteins on millions of single cells, CyTOF allows the characterisation of cell subpopulations in unprecedented detail. However, most CyTOF studies require the integration of data from multiple CyTOF batches usually acquired on different days and possibly at different sites. To date, the integration of CyTOF datasets remains a challenge due to technical differences arising in multiple batches. To overcome this limitation, we developed an approach called CytofRUV for analysing multiple CyTOF batches, which includes an R-Shiny application with diagnostic plots. CytofRUV can correct for batch effects and integrate data from large numbers of patients and conditions across batches, to confidently compare cellular changes and correlate these with clinically relevant outcomes. Competing Interests: MT, CT, TT, LL, DG, TS No competing interests declared (© 2020, Trussart et al.)
Databáze:	MEDLINE
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