DyNetViewer is a Cytoscape app to provide support for constructing, analyzing, and visualizing dynamic networks in Cytoscape 3.
[Please downlaod the manual.](https://drive.google.com/open?id=0B2XsJM1bezIYZGUwTGZEWWVlV1k)
DyNetViewer is an OSGi bundle to provide support for constructing, analyzing, and visualizing dynamic networks in Cytoscape 3.
Dynamic network can be constructed by integrating a static network with time-course data. For example, one can construct a dynamic protein interaction network by integrating time-course gene expression data. In DyNetViewer, four different methods were integrated to construct dynamic protein interaction networks (TC-PIN , NF-APIN , APIN ,TS-PIN ) . One can construct a dynamic network by using the integrated methods or import a dynamic network directly. The constructed network or imported network can be displayed dynamically.
DyNetViewer also provides node centrality analysis. Twelve typical centrality measures including Betweeness Centrality (BC)[5,6], Closeness Centrality (CC) , Degree Centrality (DC), Eigenvector Centrality (EC), Local Average Connectivity-based method (LAC), Network Centrality (NC), Subgraph Centrality (SC), Information Centrality (IC),Stress, Radiality, Eccentricity, Centroid, were integrated in DyNetViewer. DyNetViewer supports calculation of multiple centralities simultaneously and plots the change chart of different centralities against time.
DyNetViewer also integrates clustering algorithms (such as TSN-PCD) for identifying and analyzing the clusters of dynamic networks. The clusters from dynamic network can also be viewed dynamically.
This app is developed by Jie Yang, designed and directed by Dr. Min Li, from Central South University.
DyNetViewer is an OSGi bundle application of the revamped Cytoscape 3.0.
• Download Cytoscape 3.4.0 beta. ([http://www.cytoscape.org/])
• Install DyNetViewer from AppStore.
• ... or install/update the application in Cytoscape with App -> AppManager -> Install Apps -> dydetviewer-1.0.0.beta
• ...or download dynetviewer-1.0.0.jar and save it under CytoscapeConfiguration/3/apps/installed in your home directory
1.Tang X, Wang J, Liu B, et al. A comparison of the functional modules identified from time course and static PPI network data[J]. BMC bioinformatics, 2011, 12(1): 1.
2.Xiao Q, Wang J, Peng X, et al. Detecting protein complexes from active protein interaction networks constructed with dynamic gene expression profiles[J]. Proteome science, 2013, 11(1): 1.
3.Wang J, Peng X, Li M, et al. Active protein interaction network and its application on protein complex detection[C]//Bioinformatics and Biomedicine (BIBM), 2011 IEEE International Conference on. IEEE, 2011: 37-42.
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10.Li, M., Wang, J., Chen, X., Wang, H., & Pan, Y. (2011). A local average connectivity-based method for identifying essential proteins from the network level. Computational biology and chemistry, 35(3), 143-150.
11.Wang, J., Li, M., Wang, H., & Pan, Y. (2012). Identification of essential proteins based on edge clustering coefficient, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9(4), 1070-1080.
12.Estrada, E., & Rodriguez-Velazquez, J. A. (2005). Subgraph centrality in complex networks. Physical Review E, 71(5), 056103.
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14.Li M, Wu X, Wang J, et al. Towards the identification of protein complexes and functional modules by integrating PPI network and gene expression data[J]. BMC bioinformatics, 2012, 13(1): 1.
Please cite the following manual when you use DyNetViewer:
Min Li, Jie Yang, Jianxin Wang. DyNetViewer: a Cytoscape app for dynamic network construction, analysis and visualization.
###5. Contact Information
If you have any problems or suggestions of DyNetViewer, please contact us at Email: ***firstname.lastname@example.org***,***JieYang930808@gmail.com***.
###6. Related Tools
Our group also developed several other related tools:
Providing calculation, evaluation and visualization analysis for several centralities of weighted and unweighted network.
a Cytoscape app for analysis and visualization of clusters from network
Clustering based on FAG-EC, EAGLE or MCODE. Found cluster can be subjected to GO enrichment analysis.