DynDiffNet provide support for constructing, analyzing, and visualizing dynamic differential networks in Cytoscape 3.
[Please downlaod the manual](https://drive.google.com/file/d/1TCfeVDvSM1wN29OCjVkuLFWnFv5eDw4Q/view?usp=sharing).
## 1.Introduction
DynDiffNet offers a diverse of functionalities for constructing dynamic differential networks, identifying disease modules, analyzing module evolution, and evaluating node centrality. Additionally, DynDiffNet provides comprehensive visualization capabilities for dynamic differential networks and analysis results, enabling users to conveniently observe critical nodes or disease modules. Furthermore, DynDiffNet can be applied to various types of dynamic differential networks.
DynDiffNet supports the construction of three types of dynamic differential networks, including multiple dynamic differential networks, disease-background dynamic differential networks, and temporal dynamic differential networks. It provides functionality for importing and exporting dynamic differential networks and dynamically visualizing sub-networks at different time points. Additionally, users can access network properties such as the number of nodes, edges, and the diameter.
DynDiffNet offers four disease module identification algorithms, including TSN-PCD[1], EAGLE[2], MCODE[3], and ClusterONE[4]. The results are presented as thumbnails in the results panel. The panel displays all disease modules or modules in a sub-network at a specific time point, allowing users to update the module list as they adjust the time slider.
DynDiffNet offers module evolution analysis for disease modules at different time points. The improved GED[5] algorithm defines seven categories of module evolution events: maintenance, contraction, growth, division, merging, dissolution, and formation. The interface shows all events along with their respective counts, and visually presents the disease modules related to the selected event in a tabular format.
DynDiffNet integrates six classical centrality analysis methods, including Betweenness Centrality (BC)[6], Closeness Centrality (CC)[7], Degree Centrality (DC)[8], Eigenvector Centrality (EC)[9], Local Average Connectivity-based method (LAC)[10], and Network Centrality (NC)[11]. Users can run multiple centrality metrics simultaneously.
This app is developed by Zhangyi Huang, designed and directed by Dr. Min Li, from Central South University.
## 2.Installation
DynDiffNet is an OSGi bundle application of the Cytosscape 3.0.
- Download Cytoscape 3.10.0.
- Install DynDiffNet from AppStore.
- … or install/update the application in Cytoscape with App -> AppManager -> Install Apps -> DynDiffNet -1.0.0.
- … or download DynDiffNet-1.0.0.jar and save it under CytoscapeConfiguration/3/apps/installed in your home directory.
## 3.References
[1] 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): 1-15.
[2] Shen H, Cheng X, Cai K, et al. Detect overlapping and hierarchical community structure in networks[J]. Physica A: Statistical Mechanics and its Applications, 2009, 388(8): 1706-1712.
[3] Bader G D, Hogue C W V. An automated method for finding molecular complexes in large protein interaction networks[J]. BMC bioinformatics, 2003, 4(1): 1-27.
[4] Nepusz T, Yu H, Paccanaro A. Detecting overlapping protein complexes in protein-protein interaction networks[J]. Nature methods, 2012, 9(5): 471-472.
[5] Bródka P, Saganowski S, Kazienko P. GED: the method for group evolution discovery in social networks[J]. Social Network Analysis and Mining, 2013, 3: 1-14.
[6] Newman M E J. A measure of betweenness centrality based on random walks[J]. Social networks, 2005, 27(1): 39-54.
[7] Bavelas A. Communication patterns in task‐oriented groups[J]. The journal of the acoustical society of America, 1950, 22(6): 725-730.
[8] Jeong H, Mason S P, Barabási A L, et al. Lethality and centrality in protein networks[J]. Nature, 2001, 411(6833): 41-42.
[9] Bonacich P. Power and centrality: A family of measures[J]. American journal of sociology, 1987, 92(5): 1170-1182.
[10] Li M, Wang J, Chen X, et al. A local average connectivity-based method for identifying essential proteins from the network level[J]. Computational biology and chemistry, 2011, 35(3): 143-150.
[11] Wang J, Li M, Wang H, et al. Identification of essential proteins based on edge clustering coefficient[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2011, 9(4): 1070-1080.
## 4.Contact Information
If you have any problem or suggestions of DynDiffNet, please contact us at Email: limin@mail.csu.edu.cn, narniayiyi@gmail.com.
