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

Ciucci, S., Ge, Y., Duran, C., Palladini, A., Jimenez-Jimenez, V., Martinez-Sanchez, L. M., et al. (2017). Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies. SCIENTIFIC REPORTS, 7: 43946. doi:10.1038/srep43946.

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Ciucci, Sara1, Author
Ge, Yan1, Author
Duran, Claudio1, Author
Palladini, Alessandra1, Author
Jimenez-Jimenez, Victor1, Author
Martinez-Sanchez, Luisa Maria1, Author
Wang, Yuting1, Author
Sales, Susanne1, Author
Shevchenko, Andrej1, Author
Poser, Steven W.1, Author
Herbig, Maik1, Author
Otto, Oliver1, Author
Androutsellis-Theotokis, Andreas1, Author
Guck, Jochen2, Author           
Gerl, Mathias J.1, Author
Cannistraci, Carlo Vittorio1, Author
Affiliations:
1external, ou_persistent22              
2External Organizations, ou_persistent22              

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 Abstract: 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 alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted. Here we present PC-corr: a simple algorithm that associates to any PCA segregation a discriminative network of features. Such network can be inspected in search of functional modules useful in the definition of combinatorial and multiscale biomarkers from multifaceted omic data in systems and precision biomedicine. We offer proofs of PC-corr efficacy on lipidomic, metagenomic, developmental genomic, population genetic, cancer promoteromic and cancer stem-cell mechanomic data. Finally, PC-corr is a general functional network inference approach that can be easily adopted for big data exploration in computer science and analysis of complex systems in physics.

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Language(s): eng - English
 Dates: 2017
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1038/srep43946
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Title: SCIENTIFIC REPORTS
Source Genre: Journal
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Publ. Info: MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND : NATURE PUBLISHING GROUP
Pages: - Volume / Issue: 7 Sequence Number: 43946 Start / End Page: - Identifier: ISSN: 2045-2322