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. 2015 Dec 9;7:126. doi: 10.1186/s13148-015-0159-0

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© Gao et al. 2015

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. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Fig. 1 — The FEM algorithm and examples of FEM modules in ER+ breast cancer. a The FEM algorithm first uses gene-centred statistics of differential DNA methylation, t ^(D)_g and differential mRNA expression, t ^(R)_g, here between normal and ER+ breast cancer, to weight the edges in a PPI network. The weight of the edge between gene g and h is constructed as indicated, where H(x) denotes the Heaviside function (H(x) = 1 if x > 0, H(x) = 0 if x < 0), which are being used to impose an anti-correlation. Hotspots of differential DNAm and mRNA expression are then inferred by running a module detection algorithm on the PPI network, which attempts to find subnetworks that maximize the modularity (average weight density) locally. b Examples of two FEMs centred around seed genes CAV1 and FSTL1 in ER+ breast cancer