Random matrix analysis for gene interaction networks in cancer cells

TL;DR

This study applies random matrix theory to analyze gene interaction networks in cancer cells, revealing a transition from Wigner to Poisson distributions based on network density, which aids understanding of network topology.

Contribution

It introduces a novel application of random matrix analysis to gene interaction networks, linking spectral properties to network density and interaction reliability.

Findings

Dense networks (>38,000 edges) exhibit Wigner distribution.

Sparse networks show Poisson distribution.

Distribution correlates with network size and edge frequency.

Abstract

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.