Network module detection: Affinity search technique with the multi-node topological overlap measure

Ai Li,Steve Horvath

doi:10.1186/1756-0500-2-142

Abstract

BackgroundMany clustering procedures only allow the user to input a pairwise dissimilarity or distance measure between objects. We propose a clustering method that can input a multi-point dissimilarity measure d(i1, i2, ..., iP) where the number of points P can be larger than 2. The work is motivated by gene network analysis where clusters correspond to modules of highly interconnected nodes. Here, we define modules as clusters of network nodes with high multi-node topological overlap. The topological overlap measure is a robust measure of interconnectedness which is based on shared network neighbors. In previous work, we have shown that the multi-node topological overlap measure yields biologically meaningful results when used as input of network neighborhood analysis.FindingsWe adapt network neighborhood analysis for the use of module detection. We propose the Module Affinity Search Technique (MAST), which is a generalized version of the Cluster Affinity Search Technique (CAST). MAST can accommodate a multi-node dissimilarity measure. Clusters grow around user-defined or automatically chosen seeds (e.g. hub nodes). We propose both local and global cluster growth stopping rules. We use several simulations and a gene co-expression network application to argue that the MAST approach leads to biologically meaningful results. We compare MAST with hierarchical clustering and partitioning around medoid clustering.ConclusionOur flexible module detection method is implemented in the MTOM software which can be downloaded from the following webpage:

Highlights

Many clustering procedures only allow the user to input a pairwise dissimilarity or distance measure between objects
Our flexible module detection method is implemented in the multinode topological overlap measure (MTOM) software which can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/ MTOM/
Review of hierarchical clustering with the pairwise topological overlap measure Before we describe the details of the Module Affinity Search Technique (MAST) procedure, we method we first review a widely used module detection method which defines modules as branches of a hierarchical clustering tree

Summary

Introduction

Many clustering procedures only allow the user to input a pairwise dissimilarity or distance measure between objects. Since network neighborhood analysis is an important step in our proposed MAST procedure for module detection, we will review it in the following. The standard approach for using the pairwise topological overlap measure for module detection is to use it as input of a hierarchical clustering procedure which results in a cluster tree (dendrogram) [13].

Results

Conclusion