A framework for stability-based module detection in correlation graphs

Statistical Analysis and Data Mining

article

Mingmei Tian¹  Rachael Hageman Blair¹  Lina Mu²  Matthew Bonner²  Richard Browne³  Han Yu⁴ 

[1] Department of Biostatistics, State University of New York at Buffalo;Department of Epidemiology and Environmental Health, State University of New York at Buffalo;Department of Biotechnical and Clinical Laboratory Sciences, State University of New York at Buffalo;Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center

关键词: clustering;    graphical model;    Jaccard coefficient;    module detection;    network;    stability;   

DOI  :  10.1002/sam.11495

学科分类：社会科学、人文和艺术（综合）

来源: John Wiley & Sons, Inc.

【 摘 要 】

Graphs can be used to represent the direct and indirect relationships between variables, and elucidate complex relationships and interdependencies. Detecting structure within a graph is a challenging problem. This problem is studied over a range of fields and is sometimes termed community detection, module detection, or graph partitioning. A popular class of algorithms for module detection relies on optimizing a function of modularity to identify the structure. In practice, graphs are often learned from the data, and thus prone to uncertainty. In these settings, the uncertainty of the network structure can become exaggerated by giving unreliable estimates of the module structure. In this work, we begin to address this challenge through the use of a nonparametric bootstrap approach to assessing the stability of module detection in a graph. Estimates of stability are presented at the level of the individual node, the inferred modules, and as an overall measure of performance for module detection in a given graph. Furthermore, bootstrap stability estimates are derived for complexity parameter selection that ultimately defines a graph from data in a way that optimizes stability. This approach is utilized in connection with correlation graphs but is generalizable to other graphs that are defined through the use of dissimilarity measures. We demonstrate our approach using a broad range of simulations and on a metabolomics dataset from the Beijing Olympics Air Pollution study. These approaches are implemented using bootcluster package that is available in the R programming language.

【 授权许可】

Unknown