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Cancer Res. 2018 Apr 15;78(8):2140-2143. doi: 10.1158/0008-5472.CAN-17-1383. Epub 2018 Feb 19.

CrosstalkNet: A Visualization Tool for Differential Co-expression Networks and Communities.

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Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
Goodman Cancer Research Centre, McGill University, Montreal, Canada.
Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
Ontario Institute of Cancer Research, Toronto, Ontario, Canada.


Variations in physiological conditions can rewire molecular interactions between biological compartments, which can yield novel insights into gain or loss of interactions specific to perturbations of interest. Networks are a promising tool to elucidate intercellular interactions, yet exploration of these large-scale networks remains a challenge due to their high dimensionality. To retrieve and mine interactions, we developed CrosstalkNet, a user friendly, web-based network visualization tool that provides a statistical framework to infer condition-specific interactions coupled with a community detection algorithm for bipartite graphs to identify significantly dense subnetworks. As a case study, we used CrosstalkNet to mine a set of 54 and 22 gene-expression profiles from breast tumor and normal samples, respectively, with epithelial and stromal compartments extracted via laser microdissection. We show how CrosstalkNet can be used to explore large-scale co-expression networks and to obtain insights into the biological processes that govern cross-talk between different tumor compartments.Significance: This web application enables researchers to mine complex networks and to decipher novel biological processes in tumor epithelial-stroma cross-talk as well as in other studies of intercompartmental interactions. Cancer Res; 78(8); 2140-3. ©2018 AACR.

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