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Abstract: We present a technique to characterize differentially expressed genes interms of their position in a high-dimensional co-expression network. The set-upof Gaussian graphical models is used to construct representations of theco-expression network in such a way that redundancy and the propagation ofspurious information along the network are avoided. The proposed inferenceprocedure is based on the minimization of the Bayesian Information CriterionBIC in the class of decomposable graphical models. This class of models canbe used to represent complex relationships and has suitable properties thatallow to make effective inference in problems with high degree of complexitye.g. several thousands of genes and small number of observations e.g.10-100 as typically occurs in high throughput gene expression studies. Takingadvantage of the internal structure of decomposable graphical models, weconstruct a compact representation of the co-expression network that allows toidentify the regions with high concentration of differentially expressed genes.It is argued that differentially expressed genes located in highlyinterconnected regions of the co-expression network are less informative thandifferentially expressed genes located in less interconnected regions. Based onthat idea, a measure of uncertainty that resembles the notion of relativeentropy is proposed. Our methods are illustrated with three publicallyavailable data sets on microarray experiments the larger involving more than50,000 genes and 64 patients and a short simulation study.



Author: Gabriel C. G. de Abreu, Rodrigo Labouriau

Source: https://arxiv.org/







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