V-SVA: an R Shiny application for detecting and annotating hidden sources of variation in single-cell RNA-seq data

SUMMARY: Single-cell RNA-sequencing (scRNA-seq) technology enables studying gene expression programs from individual cells. However, these data are subject to diverse sources of variation, including ‘unwanted’ variation that needs to be removed in downstream analyses (e.g. batch effects) and ‘wanted...

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Detalles Bibliográficos
Autores principales: Lawlor, Nathan, Marquez, Eladio J, Lee, Donghyung, Ucar, Duygu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Applications Notes
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7267827/
https://www.ncbi.nlm.nih.gov/pubmed/32119082
http://dx.doi.org/10.1093/bioinformatics/btaa128

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7267827/
https://www.ncbi.nlm.nih.gov/pubmed/32119082
http://dx.doi.org/10.1093/bioinformatics/btaa128

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