Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies

Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis (PCA). However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing. An...

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Detalles Bibliográficos
Autores principales: Ciucci, Sara, Ge, Yan, Durán, Claudio, Palladini, Alessandra, Jiménez-Jiménez, Víctor, Martínez-Sánchez, Luisa María, Wang, Yuting, Sales, Susanne, Shevchenko, Andrej, Poser, Steven W., Herbig, Maik, Otto, Oliver, Androutsellis-Theotokis, Andreas, Guck, Jochen, Gerl, Mathias J., Cannistraci, Carlo Vittorio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5347127/
https://www.ncbi.nlm.nih.gov/pubmed/28287094
http://dx.doi.org/10.1038/srep43946

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5347127/
https://www.ncbi.nlm.nih.gov/pubmed/28287094
http://dx.doi.org/10.1038/srep43946

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