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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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| Publicado en: | BMC Genomics |
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| Autores principales: | , , , , , , |
| Formato: | Artigo |
| Lenguaje: | Inglês |
| Publicado: |
BioMed Central
2017
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| Materias: | |
| Acceso en línea: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5594474/ https://ncbi.nlm.nih.gov/pubmed/28893186 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s12864-017-4112-9 |
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