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Automated identification of maximal differential cell populations in flow cytometry data.
Yue, Alice; Chauve, Cedric; Libbrecht, Maxwell W; Brinkman, Ryan R.
Afiliação
  • Yue A; Department of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada.
  • Chauve C; Department of Mathematics, Simon Fraser University, Burnaby, British Columbia, Canada.
  • Libbrecht MW; LaBRI, University of Bordeaux, Bordeaux, France.
  • Brinkman RR; Department of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada.
Cytometry A ; 101(2): 177-184, 2022 02.
Article em En | MEDLINE | ID: mdl-34559446
ABSTRACT
We introduce a new cell population score called SpecEnr (specific enrichment) and describe a method that discovers robust and accurate candidate biomarkers from flow cytometry data. Our approach identifies a new class of candidate biomarkers we define as driver cell populations, whose abundance is associated with a sample class (e.g., disease), but not as a result of a change in a related population. We show that the driver cell populations we find are also easily interpretable using a lattice-based visualization tool. Our method is implemented in the R package flowGraph, freely available on GitHub (github.com/aya49/flowGraph) and on BioConductor.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article