Protein complex detection based on partially shared multi-view clustering.
BACKGROUND
Protein complexes are the key molecular entities to perform many essential biological functions. In recent years, high-throughput experimental techniques have generated a large amount of protein interaction data. As a consequence, computational analysis of such data for protein complex detection has received increased attention in the literature. However, most existing works focus on predicting protein complexes from a single type of data, either physical interaction data or co-complex interaction data. These two types of data provide compatible and complementary information, so it is necessary to integrate them to discover the underlying structures and obtain better performance in complex detection.


RESULTS
In this study, we propose a novel multi-view clustering algorithm, called the Partially Shared Multi-View Clustering model (PSMVC), to carry out such an integrated analysis. Unlike traditional multi-view learning algorithms that focus on mining either consistent or complementary information embedded in the multi-view data, PSMVC can jointly explore the shared and specific information inherent in different views. In our experiments, we compare the complexes detected by PSMVC from single data source with those detected from multiple data sources. We observe that jointly analyzing multi-view data benefits the detection of protein complexes. Furthermore, extensive experiment results demonstrate that PSMVC performs much better than 16 state-of-the-art complex detection techniques, including ensemble clustering and data integration techniques.


CONCLUSIONS
In this work, we demonstrate that when integrating multiple data sources, using partially shared multi-view clustering model can help to identify protein complexes which are not readily identifiable by conventional single-view-based methods and other integrative analysis methods. All the results and source codes are available on https://github.com/Oyl-CityU/PSMVC .
College of Information Engineering, Shenzhen University, Nanhai Ave 3688, Shenzhen, 518060, China.|Department of Electronic and Engineering, City University of Hong Kong, Tat Chee Avenue, Hong Kong, China.
Le
L
Ou-Yang
School of Mathematics and Statistics and Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan, 430079, China.
Xiao-Fei
XF
Zhang
Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Xin Gang Road West, Guangzhou, 510275, China. stsddq@mail.sysu.edu.cn.
Dao-Qing
DQ
Dai
School of Statistics and Management, Shanghai University of Finance and Economics, Guoding Road, Shanghai, 200433, China.
Meng-Yun
MY
Wu
School of Automation, China University of Geosciences, Wuhan, China.
Yuan
Y
Zhu
Shenzhen Polytechnic, Shenzhen, 518055, China.
Zhiyong
Z
Liu
Department of Electronic and Engineering, City University of Hong Kong, Tat Chee Avenue, Hong Kong, China.
Hong
H
Yan
D016000:Cluster Analysis; D016247:Information Storage and Retrieval; D054730:Protein Interaction Domains and Motifs / Q000276:immunology*; D025941:Protein Interaction Mapping / Q000379:methods*
Multi-view learning; Protein complex; Protein-protein interaction