NDLI: Modulated Modularity Clustering as an Exploratory Tool for Functional Genomic Inference

Content Provider	PubMed Central
Author	Stone, Eric A. Ayroles, Julien F.
Editor	Storey, John D.
Copyright Year	2009
Abstract	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.
Related Links	http://dx.doi.org/10.1371/journal.pgen.1000479
Starting Page	1000479
File Format	PDF
ISSN	15537404
e-ISSN	15537404
Journal	PLoS Genetics
Issue Number	5
Volume Number	5
Language	English
Publisher	Public Library of Science
Publisher Date	2009-05-01
Access Restriction	Open
Rights Holder	Public Library of Science
Subject Keyword	Genetics(clinical) Genetics Cancer Research Ecology, Evolution, Behavior and Systematics Molecular Biology Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Genetics Molecular Biology Cancer Research Ecology, Evolution, Behavior and Systematics Genetics (clinical)

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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