Random Matrix Analysis for Gene Interaction Networks in Cancer Cells

Scientific Reports
Ayumi Kikkawa

Abstract

Investigations of topological uniqueness of gene interaction networks in cancer cells are essential for understanding the disease. Although cancer is considered to originate from the topological alteration of a huge molecular interaction network in cellular systems, the theoretical study to investigate such complex networks is still insufficient. It is necessary to predict the behavior of a huge complex interaction network from the behavior of a finite size network. Based on the random matrix theory, we study the distribution of the nearest neighbor level spacings P(s) of interaction matrices of 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 used for the inference. We observe the Wigner distribution of P(s) when the gene networks are dense networks that have more than ~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 sparseness of the networks and to edge frequ...Continue Reading

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Citations

Mar 23, 2019·Scientific Reports·Ziqiao YinZhiming Zheng
Jul 11, 2020·Scientific Reports·K PolovnikovS V Ulianov
May 19, 2021·PLoS Computational Biology·Weimiao WuZuoheng Wang

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Datasets Mentioned

BETA
GSE8057
GSE12391
GSE13255
GSE18521
GSE24080
GSE10972
GSE29013
GSE4122
GSE13598
GSE31547

Software Mentioned

TCNG
MATLAB
BN
SiGN
SQLite
RMT

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