title: Random matrix analysis of localization properties of gene coexpression network.
abstract: We analyze gene coexpression network under the random matrix theory framework. The nearest-neighbor spacing distribution of the adjacency matrix of this network follows Gaussian orthogonal statistics of random matrix theory (RMT). Spectral rigidity test follows random matrix prediction for a certain range and deviates afterwards. Eigenvector analysis of the network using inverse participation ratio suggests that the statistics of bulk of the eigenvalues of network is consistent with those of the real symmetric random matrix, whereas few eigenvalues are localized. Based on these IPR calculations, we can divide eigenvalues in three sets: (a) The nondegenerate part that follows RMT. (b) The nondegenerate part, at both ends and at intermediate eigenvalues, which deviates from RMT and expected to contain information about important nodes in the network. (c) The degenerate part with zero eigenvalue, which fluctuates around RMT-predicted value. We identify nodes corresponding to the dominant modes of the corresponding eigenvectors and analyze their structural properties.
authors.affiliation: Department of Physics and Centre for Computational Science and Engineering, National University of Singapore, 117456, Republic of Singapore.
authors.forename: Sarika
authors.initials: S
authors.lastname: Jalan
authors.forename: Norbert
authors.initials: N
authors.lastname: Solymosi
authors.forename: Gábor
authors.initials: G
authors.lastname: Vattay
authors.forename: Baowen
authors.initials: B
authors.lastname: Li
mesh_terms: D000544:Alzheimer Disease / Q000175:diagnosis / Q000235:genetics / Q000378:metabolism / Q000473:pathology; D001921:Brain / Q000378:metabolism; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D008957:Models, Genetic