2026-06-14T03:42:09Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/51592024-09-27T08:54:32Zcom_20.500.12105_19604com_20.500.12105_2051col_20.500.12105_19605

00925njm 22002777a 4500 dc Ciucci, Sara author Ge, Yan author Duran, Claudio author Palladini, Alessandra author Jimenez-Jimenez, Victor author Martinez-Sanchez, Luisa Maria author Wang, Yuting author Sales, Susanne author Shevchenko, Andrej author Poser, Steven W. author Herbig, Maik author Otto, Oliver author Androutsellis-Theotokis, Andreas author Guck, Jochen author Gerl, Mathias J. author Cannistraci, Carlo Vittorio author 2017 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. Sci Rep. 2017; 7:43946 10.1038/srep43946 2045-2322 Scientific Reports 28287094 http://hdl.handle.net/20.500.12105/5159 PROTON-PUMP INHIBITORS SQUAMOUS-CELL CARCINOMA LARGE GENE LISTS TRANSCRIPTION FACTOR LUNG-CANCER NEUROENDOCRINE DIFFERENTIATION BACTERIAL MICROBIOTA REGULATORY NETWORKS MOLECULAR ANALYSIS HIGH-THROUGHPUT Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies