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Loss of Conservation of Graph Centralities in Reverse-engineered Transcriptional Regulatory Networks
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology. Uppsala Univ, Dept Immunol Genet & Pathol, Sci Life Lab, Dag Hammarskjoldsvag 20, S-75185 Uppsala, Sweden..ORCID iD: 0000-0002-0364-2709
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology. Uppsala University, Science for Life Laboratory, SciLifeLab.
Mälardalen Univ, Sch Educ Culture & Commun UKK, Div Appl Math, Box 883, S-72123 Vasteras, Sweden..
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology.
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2017 (English)In: Methodology and Computing in Applied Probability, ISSN 1387-5841, E-ISSN 1573-7713, Vol. 19, no 4, p. 1089-1105Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2017. Vol. 19, no 4, p. 1089-1105
Keyword [en]
Transcriptional regulatory network inference, Simulated gene expression, Graph centrality
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-340897DOI: 10.1007/s11009-017-9554-7ISI: 000413792200006OAI: oai:DiVA.org:uu-340897DiVA: diva2:1180924
Conference
15th Applied Stochastic Models and Data Analysis International Conference (ASMDA), JUN 30-JUL 04, 2015, Univ Piraeus, Piraeus, GREECE
Funder
Swedish Childhood Cancer Foundation
Available from: 2018-02-07 Created: 2018-02-07 Last updated: 2018-02-07Bibliographically approved

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Weishaupt, HolgerJohansson, PatrikNelander, SvenSwartling, Fredrik J.
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