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. 2018 Oct 4;8:14830. doi: 10.1038/s41598-018-33276-3

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Network visualization and community structure detection. (a) Normal tissue derived co-expression network. Edges included at ρ ≥ 0.8. Networks were visualised using the Cytoscape(49) perfuse force-directed layout algorithm, without knowledge of the identified community structure. Each community was assigned a colour according to the partitions identified by the Louvain algorithm. (b) Cancer tissue derived co-expression networks. All parameters as a. (c) Normalised modularity values for normal and tumour derived networks at different values of ρ.