THRESHOLD SELECTION BASED ON RANDOM MATRIX THEORY FOR GENE CO-EXPRESSION NETWORK - BARACALDO, Laura; LEAL, Luis; LOPEZ-KLEINE, Liliana (Brazilian Journal of Biometrics, 2018)
Random Matrix Theory (RMT) methods for threshold selection had only been applied in a very low number of studies aiming the construction of Gene Co-expression Networks (GCN) and several open questions remained, especially regarding the general applicability regardless the diverse data structure of gene expression data sets. Moreover, no clear methodology to follow at each step was available. Here, we show, that RMT methodology is, in fact, capable to differentiate Gaussian Orthogonal Ensemble (GOE) from Gaussian Diagonal Ensemble (GDE) structure for a great number of simulated data sets and that results are similar to those obtained with the reference method of clustering coefficient.