CytoCluster: A Cytoscape Plugin for Cluster Analysis and Visualization of Biological Networks.

Min Li,Dongyan Li,Jianxin Wang,Yu Tang,Fangxiang Wu

doi:10.3390/ijms18091880

Abstract

Nowadays, cluster analysis of biological networks has become one of the most important approaches to identifying functional modules as well as predicting protein complexes and network biomarkers. Furthermore, the visualization of clustering results is crucial to display the structure of biological networks. Here we present CytoCluster, a cytoscape plugin integrating six clustering algorithms, HC-PIN (Hierarchical Clustering algorithm in Protein Interaction Networks), OH-PIN (identifying Overlapping and Hierarchical modules in Protein Interaction Networks), IPCA (Identifying Protein Complex Algorithm), ClusterONE (Clustering with Overlapping Neighborhood Expansion), DCU (Detecting Complexes based on Uncertain graph model), IPC-MCE (Identifying Protein Complexes based on Maximal Complex Extension), and BinGO (the Biological networks Gene Ontology) function. Users can select different clustering algorithms according to their requirements. The main function of these six clustering algorithms is to detect protein complexes or functional modules. In addition, BinGO is used to determine which Gene Ontology (GO) categories are statistically overrepresented in a set of genes or a subgraph of a biological network. CytoCluster can be easily expanded, so that more clustering algorithms and functions can be added to this plugin. Since it was created in July 2013, CytoCluster has been downloaded more than 9700 times in the Cytoscape App store and has already been applied to the analysis of different biological networks. CytoCluster is available from http://apps.cytoscape.org/apps/cytocluster.

Highlights

In recent years, people have paid more and more attention to recognizing life activities within a cell by protein interactions and protein complexes [1,2,3] in the field of systems biology
Clustering Protein-protein interaction (PPI) networks can be useful for isolating groups of interacting proteins that participate in the same biological processes or that, together, perform specific biological functions
In order to solve the above limitations, we developed a new plugin named CytoCluster, which integrates six new clustering algorithms in total

Summary

Introduction

People have paid more and more attention to recognizing life activities within a cell by protein interactions and protein complexes [1,2,3] in the field of systems biology. The spectral-based clustering algorithms predict protein complexes based on the spectrum theory, such as QCUT (Combines spectral graph partitioning and a local search to optimize the modularity Q) [23], ADMSC (Adjustable Diffusion Matrix-based Spectral Clustering) [24], and SSCC (Semi-Supervised Consensus Clustering) [25] These spectral-based clustering methods can be a simple and fast approach to a certain extent. Among all of the apps, there are several apps, such as ClusterViz [35], clusterMake [36], and ClusterONE [16], which are used to detect and visualize protein complexes in PPI networks They are all useful tools with different clustering methods, which have been used in different areas of life sciences in recent years. Our app becomes a versatile tool that offers such comprehensive clustering algorithms, in addition to the BinGO function for biological networks

Architecture

Calculation and Basic Analysis

Cases Studies