Por favor, use este identificador para citar o enlazar este Item:http://hdl.handle.net/20.500.12105/5159
Título
Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies
Autor(es)
Ciucci, Sara | Ge, Yan | Duran, Claudio | Palladini, Alessandra | Jimenez-Jimenez, Victor CNIC | Martinez-Sanchez, Luisa Maria | Wang, Yuting | Sales, Susanne | Shevchenko, Andrej | Poser, Steven W. | Herbig, Maik | Otto, Oliver | Androutsellis-Theotokis, Andreas | Guck, Jochen | Gerl, Mathias J. | Cannistraci, Carlo Vittorio
Fecha de publicación
2017
Cita
Sci Rep. 2017; 7:43946
Idioma
Inglés
Tipo de documento
journal article
Resumen
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.
Palabras clave
PROTON-PUMP INHIBITORS | SQUAMOUS-CELL CARCINOMA | LARGE GENE LISTS | TRANSCRIPTION FACTOR | LUNG-CANCER | NEUROENDOCRINE DIFFERENTIATION | BACTERIAL MICROBIOTA | REGULATORY NETWORKS | MOLECULAR ANALYSIS | HIGH-THROUGHPUT
Versión en línea
DOI
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