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Ciucci S, Ge Y, Durán C, et al. Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies. Sci Rep. 2017;7:43946doi: 10.1038/srep43946.
Ciucci, S., Ge, Y., Durán, C., Palladini, A., Jiménez-Jiménez, V., Martínez-Sánchez, L. M., Wang, Y., Sales, S., Shevchenko, A., Poser, S. W., Herbig, M., Otto, O., Androutsellis-Theotokis, A., Guck, J., Gerl, M. J., & Cannistraci, C. V. (2017). Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies. Scientific reports, 743946. https://doi.org/10.1038/srep43946
Ciucci, Sara, et al. "Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies." Scientific reports vol. 7 (2017): 43946. doi: https://doi.org/10.1038/srep43946
Ciucci S, Ge Y, Durán C, Palladini A, Jiménez-Jiménez V, Martínez-Sánchez LM, Wang Y, Sales S, Shevchenko A, Poser SW, Herbig M, Otto O, Androutsellis-Theotokis A, Guck J, Gerl MJ, Cannistraci CV. Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies. Sci Rep. 2017 Mar 13;7:43946. doi: 10.1038/srep43946. PMID: 28287094; PMCID: PMC5347127.
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Sci Rep. 2017 Mar 13;7:43946. doi: 10.1038/srep43946.

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

Scientific reports

Sara Ciucci, Yan Ge, Claudio Durán, Alessandra Palladini, Víctor Jiménez-Jiménez, Luisa María Martínez-Sánchez, Yuting Wang, Susanne Sales, Andrej Shevchenko, Steven W Poser, Maik Herbig, Oliver Otto, Andreas Androutsellis-Theotokis, Jochen Guck, Mathias J Gerl, Carlo Vittorio Cannistraci

Affiliations

  1. Biomedical Cybernetics Group, Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Department of Physics, Technische Universität Dresden, Tatzberg 47/49, 01307 Dresden, Germany.
  2. Lipotype GmbH, Tatzberg 47, 01307 Dresden, Germany.
  3. Membrane Biochemistry Group, DZD Paul Langerhans Institute, Technische Universität Dresden, Tatzberg 47/49, 01307 Dresden, Germany.
  4. Integrin Signalling Group, Fundación Centro Nacional de Investigaciones Cardiovasculares Carlos III, Melchor Fernández Almagro 3, 28029 Madrid, Spain.
  5. MPI of Molecular Cell Biology and Genetics, Pfotenhauerstrstraße 108, 01307 Dresden, Germany.
  6. Center for Regenerative Therapies Dresden (CRTD), Center for Molecular and Cellular Bioengineering (CMCB), Technische Universität Dresden, Fetscherstraße 105, 01307 Dresden, Germany.
  7. Department of Internal Medicine III, University Hospital Carl Gustav Carus at the Technische Universität Dresden, Fetscherstr.74, 01307 Dresden, Germany.
  8. Cellular Machines Group, Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Technische Universität Dresden, Tatzberg 47/49, 01307 Dresden, Germany.
  9. Department of Stem Cell Biology, Centre for Biomolecular Sciences, Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham NG7 2RD, U.K.

PMID: 28287094 PMCID: PMC5347127 DOI: 10.1038/srep43946

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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