Estimating cellular redundancy in networks of genetic expression

TL;DR

This paper introduces a spectral method based on hypergraph models with real coefficients to estimate cellular redundancy, a new measure of gene expression heterogeneity, using both simulated and real gene expression data.

Contribution

It presents a novel approach to quantify gene expression heterogeneity through spectral analysis of hypergraph models with real coefficients.

Findings

Spectral analysis can effectively estimate cellular redundancy.

The method distinguishes different levels of gene expression heterogeneity.

Application to real data demonstrates practical utility.

Abstract

Networks of genetic expression can be modelled by hypergraphs with the additional structure that real coefficients are given to each vertex-edge incidence. The spectra, i.e. the multiset of the eigenvalues, of such hypergraphs, are known to encode structural information of the data. We show how these spectra can be used, in particular, in order to give an estimation of cellular redundancy, a novel measure of gene expression heterogeneity, of the network. We analyze some simulated and real data sets of gene expression for illustrating the new method proposed here.