Investigations of topological uniqueness of gene interaction networks in cancer cells are essential for understanding the disease. Based on the random matrix theory, we study the distribution of the nearest neighbor level spacings P(s) of interaction matrices for gene networks in human cancer cells. The interaction matrices are formed using the Cancer Network Galaxy (TCNG) database, which is a repository of gene interactions inferred by a Bayesian network model. In TCNG database, 256 NCBI GEO entries regarding gene expressions in human cancer cells were selected for the Bayesian network calculations  . We observe the Wigner distribution of P(s) when the gene networks are dense networks that have large numbers of edges. In the opposite case, when the networks have small numbers of edges,  P(s) becomes the Poisson distribution. We investigate relevance of P(s) both to the size of the networks and to edge frequencies that manifest reliance of the inferred gene interactions.
Investigations of topological uniqueness of gene interaction networks in cancer cells are essential for understanding this disease. Based on the random matrix theory, we study the distribution of the nearest neighbor level spacings P(s) of interaction matrices for gene networks in human cancer cells. The interaction matrices are computed using the Cancer Network Galaxy (TCNG) database, which is a repository of gene interactions inferred by a Bayesian network model. 256 NCBI GEO entries regarding gene expressions in human cancer cells have been selected for the Bayesian network calculations in TCNG . We observe the Wigner distribution of P(s) when the gene networks are dense networks that have more than \sim 38,000 edges. In the opposite case, when the networks have smaller numbers of edges, the distribution P(s) becomes the Poisson distribution. We investigate relevance of P(s) both to the size of the networks and to edge frequencies that manifest reliance of the inferred gene interactions.
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