Estimating cellular redundancy in networks of genetic expression
Estimating cellular redundancy in networks of genetic expression
Networks of genetic expression can be modeled 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.
Cellular redundancy, Data analysis, Genetic expression, Hypergraphs, Spectral theory
108713
Mulas, Raffaella
1ceeaad9-da27-4bb3-bd5b-4f0c7ec422e5
Casey, Michael J.
9f9cc46f-b75a-40eb-8d3f-fa850bf4abee
1 November 2021
Mulas, Raffaella
1ceeaad9-da27-4bb3-bd5b-4f0c7ec422e5
Casey, Michael J.
9f9cc46f-b75a-40eb-8d3f-fa850bf4abee
Mulas, Raffaella and Casey, Michael J.
(2021)
Estimating cellular redundancy in networks of genetic expression.
Mathematical Biosciences, 341 (11), , [108713].
(doi:10.1016/j.mbs.2021.108713).
Abstract
Networks of genetic expression can be modeled 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.
Text
Estimating_cellular_redundancy_in_networks_of_genetic_expression
- Accepted Manuscript
More information
Accepted/In Press date: 16 July 2021
Published date: 1 November 2021
Additional Information:
Funding Information:
RM was supported by The Alan Turing Institute under the EPSRC, UK grant EP/N510129/1 .
Publisher Copyright:
© 2021 Elsevier Inc.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
Keywords:
Cellular redundancy, Data analysis, Genetic expression, Hypergraphs, Spectral theory
Identifiers
Local EPrints ID: 453434
URI: http://eprints.soton.ac.uk/id/eprint/453434
ISSN: 0025-5564
PURE UUID: ce938df3-0c77-499c-9e35-1f1151608395
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Date deposited: 14 Jan 2022 17:42
Last modified: 17 Mar 2024 06:55
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Contributors
Author:
Raffaella Mulas
Author:
Michael J. Casey
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