Omixer: multivariate and reproducible sample randomization to proactively counter batch effects in omics studies

MOTIVATION: Batch effects heavily impact results in omics studies, causing bias and false positive results, but software to control them preemptively is lacking. Sample randomization prior to measurement is vital for minimizing these effects, but current approaches are often ad hoc, poorly documente...

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Detalles Bibliográficos
Autores principales: Sinke, Lucy, Cats, Davy, Heijmans, Bastiaan T
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Applications Notes
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10262301/
https://www.ncbi.nlm.nih.gov/pubmed/33693546
http://dx.doi.org/10.1093/bioinformatics/btab159

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10262301/
https://www.ncbi.nlm.nih.gov/pubmed/33693546
http://dx.doi.org/10.1093/bioinformatics/btab159

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