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

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

Abstract 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 par...

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Bibliographic Details
Main Authors: Ulykbek Kairov, Laura Cantini, Alessandro Greco, Askhat Molkenov, Urszula Czerwinska, Emmanuel Barillot, Andrei Zinovyev
Format: Article
Language:English
Published: BMC 2017-09-01
Series:BMC Genomics
Subjects:
Transcriptome
Independent component analysis
Reproducibility
Cancer
Online Access:http://link.springer.com/article/10.1186/s12864-017-4112-9

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http://link.springer.com/article/10.1186/s12864-017-4112-9

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