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Removing unwanted variation with CytofRUV to integrate multiple CyTOF datasets.
Trussart, Marie; Teh, Charis E; Tan, Tania; Leong, Lawrence; Gray, Daniel Hd; Speed, Terence P.
Afiliação
  • Trussart M; Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.
  • Teh CE; School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia.
  • Tan T; The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.
  • Leong L; Department of Medical Biology, The University of Melbourne, Parkville, Australia.
  • Gray DH; The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.
  • Speed TP; Department of Medical Biology, The University of Melbourne, Parkville, Australia.
Elife ; 92020 09 07.
Article em En | MEDLINE | ID: mdl-32894218
ABSTRACT
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Espectrometria de Massas / Algoritmos / Bases de Dados Factuais / Biologia Computacional / Análise de Célula Única Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Espectrometria de Massas / Algoritmos / Bases de Dados Factuais / Biologia Computacional / Análise de Célula Única Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article