文章基本信息

标题：THRESHOLD SELECTION BASED ON RANDOM MATRIX THEORY FOR GENE CO-EXPRESSION NETWORK
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作者：Laura BARACALDO ; Luis LEAL ; Liliana LOPEZ-KLEINE 等
期刊名称：Revista Brasileira de Biometria
印刷版ISSN：0102-0811
电子版ISSN：1983-0823
出版年度：2018
卷号：36
期号：2
页码：376-384
DOI：10.28951/rbb.v36i2.205
出版社：Universidade Federal de Lavras
摘要：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.
关键词：Similarity matrices; threshold selection; random matrix theory; gene co-expression networks