Determining the optimal number of independent components for reproducible transcriptomic data analysis

BACKGROUND: Independent Component Analysis (ICA) is a method that models gene expression data as an action of a set of statistically independent hidden factors. The output of ICA depends on a fundamental parameter: the number of components (factors) to compute. The optimal choice of this parameter,...

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Detalles Bibliográficos
Autores principales: Kairov, Ulykbek, Cantini, Laura, Greco, Alessandro, Molkenov, Askhat, Czerwinska, Urszula, Barillot, Emmanuel, Zinovyev, Andrei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5594474/
https://www.ncbi.nlm.nih.gov/pubmed/28893186
http://dx.doi.org/10.1186/s12864-017-4112-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5594474/
https://www.ncbi.nlm.nih.gov/pubmed/28893186
http://dx.doi.org/10.1186/s12864-017-4112-9

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