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=0B2XsJM1bezIYdWV5NE03enplMlE) You can also obtain some sample data files [here](https://drive.google.com/open?id=0B2XsJM1bezIYMDY5cHE4VUZjRWM).
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 , DPIN ,ST-APIN ) . 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 on a dynamic network. 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 Centrality(SC-1) , Radiality Centrality(RC-1) , Eccentricity Centrality(EC-1) , Centroid Centrality(CC-1)  were integrated in DyNetViewer. DyNetViewer supports calculation of multiple centralities simultaneously and plots the change chart of different centralities against time.
In DyNetViewer, four different graph clustering algorithms for analyzing clusters of dynamic networks are: MCODE , EAGLE , ClusterONE  and TSN-PCD . 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 -> dynetviewer-3.0.0.beta
• ...or download dynetviewer-3.0.0.jar and save it under CytoscapeConfiguration/3/apps/installed in your home directory
 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.
 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.
 Wang, J., Peng, X., Li, M., & Pan, Y. (2013). Construction and application of dynamic protein interaction network based on time course gene expression data. Proteomics, 13(2), 301-312.
 Meng, X., Li, M., Wang, J., Wu, F. X., & Pan, Y. (2016, December). Construction of the spatial and temporal active protein interaction network for identifying protein complexes. In Bioinformatics and Biomedicine (BIBM), 2016 IEEE International Conference on (pp. 631-636).
 Anthonisse J M. The rush in a directed graph[J]. Stichting Mathematisch Centrum. Mathematische Besliskunde, 1971 (BN 9/71): 1-10.
 Newman, M. E. (2005). A measure of betweenness centrality based on random walks. Social networks, 27(1), 39-54.
 Sabidussi, G. (1966). The centrality index of a graph. Psychometrika, 31(4), 581-603.
 Jeong, H., Mason, S. P., Barabási, A. L., &Oltvai, Z. N. (2001). Lethality and centrality in protein networks. Nature, 411(6833), 41-42.
 Bonacich, P. (1987). Power and centrality: A family of measures. American journal of sociology, 1170-1182.
 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.
 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.
 Estrada, E., & Rodriguez-Velazquez, J. A. (2005). Subgraph centrality in complex networks. Physical Review E, 71(5), 056103.
 Stephenson, K., &Zelen, M. (1989). Rethinking centrality: Methods and examples. Social Networks, 11(1), 1-37.
 Shimbel, A. (1953). Structural parameters of communication networks. The bulletin of mathematical biophysics, 15(4), 501-507.
 Valente, T. W., & Foreman, R. K. (1998). Integration and radiality: measuring the extent of an individual's connectedness and reachability in a network. Social networks, 20(1), 89-105.
 Hage, P., & Harary, F. (1995). Eccentricity and centrality in networks. Social networks, 17(1), 57-63.
 Harary, F. 1969. Graph Theory. Reading, Mass: Addison-Wesley.
 Bader, G. D., & Hogue, C. W. (2003). An automated method for finding molecular complexes in large protein interaction networks. BMC bioinformatics, 4(1), 2.
 Shen, H., Cheng, X., Cai, K., & Hu, M. B. (2009). Detect overlapping and hierarchical community structure in networks. Physica A: Statistical Mechanics and its Applications, 388(8), 1706-1712.
 Nepusz, T., Yu, H., & Paccanaro, A. (2012). Detecting overlapping protein complexes in protein-protein interaction networks. Nature methods, 9(5), 471-472.
 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: ***email@example.com***,***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.
Integrated algorithms for the network control problem.