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Super-sparse principal component analyses for high-throughput genomic data
BACKGROUND: Principal component analysis (PCA) has gained popularity as a method for the analysis of high-dimensional genomic data. However, it is often difficult to interpret the results because the principal components are linear combinations of all variables, and the coefficients (loadings) are t...
| Autores principales: | , , , |
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| Formato: | Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2010
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2902448/ https://www.ncbi.nlm.nih.gov/pubmed/20525176 http://dx.doi.org/10.1186/1471-2105-11-296 |