Modulated Modularity Clustering as an Exploratory Tool for Functional Genomic Inference

In recent years, the advent of high-throughput assays, coupled with their diminishing cost, has facilitated a systems approach to biology. As a consequence, massive amounts of data are currently being generated, requiring efficient methodology aimed at the reduction of scale. Whole-genome transcriptional profiling is a standard component of systems-level analyses, and to reduce scale and improve inference clustering genes is common. Since clustering is often the first step toward generating hypotheses, cluster quality is critical. Conversely, because the validation of cluster-driven hypotheses is indirect, it is critical that quality clusters not be obtained by subjective means. In this paper, we present a new objective-based clustering method and demonstrate that it yields high-quality results. Our method, modulated modularity clustering (MMC), seeks community structure in graphical data. MMC modulates the connection strengths of edges in a weighted graph to maximize an objective function (called modularity) that quantifies community structure. The result of this maximization is a clustering through which tightly-connected groups of vertices emerge. Our application is to systems genetics, and we quantitatively compare MMC both to the hierarchical clustering method most commonly employed and to three popular spectral clustering approaches. We further validate MMC through analyses of human and Drosophila melanogaster expression data, demonstrating that the clusters we obtain are biologically meaningful. We show MMC to be effective and suitable to applications of large scale. In light of these features, we advocate MMC as a standard tool for exploration and hypothesis generation.Author Summary: Systems genetic approaches integrate classical methods with transcriptional profiling and other modern assays to make inference at the network level. It is customary to partition the genes entering such an analysis into clusters destined for independent interrogation, but there is a danger of facilitating a hypothesis that is falsely self-fulfilling. Motivated by the dual issues of scale and subjectivity, we present a new clustering method designed to elicit transcriptional modules from gene expression profiles that is both effective and automatic. Modulated modularity clustering (MMC) seeks community structure in graphical data—in this case, a graph of genes connected by edges whose weights reflect the degree to which transcriptional profiles correlate. MMC modifies this graph to make communities stand out and returns the clustering that describes this community structure. We begin with a numerical study to show that MMC is able to recover community structure from simulated data. We then demonstrate similar success on biological data by obtaining human and Drosophila gene clusters that, in each case, are intuitive and biologically meaningful. We advocate the use of MMC as an exploratory tool for functional genomic inference. A Web server for MMC is available at http://mmc.gnets.ncsu.edu.