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1. Dataset

We present the usage of ZoomOut Webserver on gene interaction networks regarding significantly differentially expressed genes identified between 43 ERa-positive breast tumors including 14 tumors with PIK3CA mutations and 29 tumors without PIK3CA mutations obtained from Gene Expression Omnibus (GEO) ( database under series accession numbers GSE22035 [1]. These gene interaction networks have been produced using 11 widespread network inference methods. More specifically, six methods based on mutual information (Aracne.a [2], Aracne.m [2], MRNET [3], MRNETB [4], CLR [5] and C3net [6]), four correlation based (Genenet [7], WGCNA [8], Lasso [9], Adaptive Lasso [10]) and one tree based (Genie3 [11]), were applied in the expression values of the top 100 differentially expressed genes based on their p-values. Finally, for the network reconstruction based on biological information we used the Cytoscape platform and more specifically the GeneMania plugin [12].


Load Data Upload Data to Workspace

Users are able to upload their networks by pressing �Choose file� button, selecting multiple files (Ctrl+A for all files), and then pressing �Upload to Workspace� button. Users are also able to download and visualize their uploaded data.

Load Data Download or Visualize Data

Visualize Data Visualize Data

Users are able to navigate to the graph either by holding left mouse button and scrolling mouse or by using the navigation panel. By selecting a node in the graph, 1st neighborhood connections are only visible. By clicking again outside of the graph, the whole network is revealed again. Initial network is in random layout, however users are able to select between random, circular or Fruchterman-Reingold layout (as shown here). Nodes of the network can also be found and selected using �select node�.

2. Feature Networks Caclucations

Users are able to generate eleven global network characteristics � features by pressing �Generate Features�.
Image Workspace Global Network Features

3. Clustering Networks

In the present study, we have selected Affinity Propagation Clustering with Euclidean distance. As features we have selected Jaccard index, degree of distribution (mean), clustering distribution (mean) and closeness centrality (mean).
Image Clustering Clustering Parameters

After applying clustering, the following clustering image (dendrogram) is generated. Image Clustering Affinity Propagation Clsutering

By clicking the Visualize button, all networks with their calculated distances can be displayed. The larger the connection of two networks, the more bold the interconnected edge will be displayed
Image ClusteringNetwork Visualization

4. References

[1] M. Cizkova, G. Cizeron-Clairac, S. Vacher, A. Susini, C. Andrieu, R. Lidereau, and I. Bieche, “Gene expression profiling reveals new aspects of PIK3CA mutation in ERalpha-positive breast cancer: major implication of the Wnt signaling pathway,” PLoS One, vol. 5, no. 12, pp. e15647, 2010

[2] A. A. Margolin, I. Nemenman, K. Basso, C. Wiggins, G. Stolovitzky, R. Dalla Favera, and A. Califano, “ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context,” BMC Bioinformatics, vol. 7 Suppl 1, pp. S7, 2006

[3] P. E. Meyer, K. Kontos, F. Lafitte, and G. Bontempi, ”Information theoretic inference of large transcriptional regulatory networks”, EURASIP Journal on Bioinformatics and Systems Biology, Special Issue on Information-Theoretic Methods for Bioinformatics, 2007

[4] Patrick E. Meyer, Daniel Marbach, Sushmita Roy and Manolis Kellis, “Information-Theoretic Inferenceof Gene Networks Using Backward Elimination”, International Conference on Bioinformatics and Computational Biology, 2010

[5] Jeremiah J. Faith, Boris Hayete, Joshua T. Thaden, Ilaria Mogno, Jamey Wierzbowski, Guillaume Cottarel, Simon Kasif, James J. Collins, and Timothy S. Gardner ”Large-scale mapping and validation of escherichia coli transcriptional regulation from a compendium of expression profiles”, PLoS Biology, 2007

[6] Altay G., Asim M., Markowetz F., Neal D.,” Differential C3NET reveals disease networks of direct physical interactions”, BMC Bioinformatics, , 12:296, 2011

[7] R. Opgen-Rhein, and K. Strimmer, “From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data,” BMC Syst Biol, vol. 1, pp. 37, 2007

[8] P. Langfelder, and S. Horvath, “WGCNA: an R package for weighted correlation network analysis,” BMC Bioinformatics, vol. 9, pp. 559, 2008

[9] Tibshirani R “Regression Shrinkage and Selection via the Lasso”, Journal of the Royal Statistical Society, Series B, 58:267-288, 1996

[10] H. Zou, “The Adaptive Lasso and its Oracle Property”, Journal of the American Statistical Association 101 (476): 1418-1429, 2006

[11] Vân Anh Huynh-Thu, Alexandre Irrthum, Louis Wehenkel, Pierre Geurts, “Inferring Regulatory Networks from Expression Data Using Tree-Based Methods”, PLoS ONE, 2010

[12] K. Zuberi, M. Franz, H. Rodriguez, J. Montojo, C. T. Lopes, G. D. Bader, and Q. Morris, “GeneMANIA prediction server 2013 update,” Nucleic Acids Res, vol. 41, no. Web Server issue, pp. W115-22, Jul, 2013