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Double-jeopardy: scRNA-seq doublet/multiplet detection using multi-omic profiling.
Sun, Bo; Bugarin-Estrada, Emmanuel; Overend, Lauren Elizabeth; Walker, Catherine Elizabeth; Tucci, Felicia Anna; Bashford-Rogers, Rachael Jennifer Mary.
Afiliación
  • Sun B; Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
  • Bugarin-Estrada E; Oxford Autoimmune Neurology Group, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
  • Overend LE; Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
  • Walker CE; Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
  • Tucci FA; Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
  • Bashford-Rogers RJM; Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
Cell Rep Methods ; 1(1): None, 2021 05 24.
Article en En | MEDLINE | ID: mdl-34278374
The computational detection and exclusion of cellular doublets and/or multiplets is a cornerstone for the identification the true biological signals from single-cell RNA sequencing (scRNA-seq) data. Current methods do not sensitively identify both heterotypic and homotypic doublets and/or multiplets. Here, we describe a machine learning approach for doublet/multiplet detection utilizing VDJ-seq and/or CITE-seq data to predict their presence based on transcriptional features associated with identified hybrid droplets. This approach highlights the utility of leveraging multi-omic single-cell information for the generation of high-quality datasets. Our method has high sensitivity and specificity in inflammatory-cell-dominant scRNA-seq samples, thus presenting a powerful approach to ensuring high-quality scRNA-seq data.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Programas Informáticos / Multiómica Tipo de estudio: Diagnostic_studies Idioma: En Revista: Cell Rep Methods Año: 2021 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Programas Informáticos / Multiómica Tipo de estudio: Diagnostic_studies Idioma: En Revista: Cell Rep Methods Año: 2021 Tipo del documento: Article