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

Holger Weishaupt (), Patrik Johansson, Christopher Engström, Sven Nelander, Sergei Silvestrov and Fredrik J Swartling
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Holger Weishaupt: Uppsala University
Patrik Johansson: Uppsala University
Christopher Engström: Mälardalen University
Sven Nelander: Uppsala University
Sergei Silvestrov: Mälardalen University
Fredrik J Swartling: Uppsala University

Methodology and Computing in Applied Probability, 2017, vol. 19, issue 4, 1089-1105

Abstract: 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; 05Cxx; 92C42 (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11009-017-9554-7

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