Methodology and Computing in Applied Probability

, Volume 19, Issue 4, pp 1089–1105 | Cite as

Loss of Conservation of Graph Centralities in Reverse-engineered Transcriptional Regulatory Networks

  • Holger Weishaupt
  • Patrik Johansson
  • Christopher Engström
  • Sven Nelander
  • Sergei Silvestrov
  • Fredrik J Swartling
Open Access
Article
  • 127 Downloads

Abstract

Graph centralities are commonly used to identify and prioritize disease genes in transcriptional regulatory networks. Studies on small networks of experimentally validated protein-protein interactions underpin the general validity of this approach and extensions of such findings have recently been proposed for networks inferred from gene expression data. However, it is largely unknown how well gene centralities are preserved between the underlying biological interactions and the networks inferred from gene expression data. Specifically, while previous studies have evaluated the performance of inference methods on synthetic gene expression, it has not been established how the choice of inference method affects individual centralities in the network. Here, we compare two gene centrality measures between reference networks and networks inferred from corresponding simulated gene expression data, using a number of commonly used network inference methods. The results indicate that the centrality of genes is only moderately conserved for all of the inference methods used. In conclusion, caution should be exercised when inspecting centralities in reverse-engineered networks and further work will be required to establish the use of such networks for prioritizing disease genes.

Keywords

Transcriptional regulatory network inference Simulated gene expression Graph centrality 

Mathematics Subject Classification (2010)

05Cxx 92C42 

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© The Author(s) 2017

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Department of Immunology, Genetics and Pathology, Science for Life LaboratoryUppsala UniversityUppsalaSweden
  2. 2.Division of Applied Mathematics, The School of Education, Culture and Communication (UKK)Mälardalen UniversityVästeråsSweden

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