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Multivariate dependence and genetic networks inference

Multivariate dependence and genetic networks inference

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  • Author(s): A.A. Margolin 1, 2 ; K. Wang 2, 3 ; A. Califano 2 ; I. Nemenman 2, 4
    • View affiliations
    • Affiliations: 1: Cancer Program, The Broad Institute of Harvard and MIT, Cambridge, USA
      2: Joint Centers for Systems Biology, Columbia University Medical Center, New York, USA
      3: Joint Centers for Systems Biology, Oncology Research Unit, San Diego, USA
      4: Departments of Physics and Biology, Emory University, Computational and Life Sciences Strategic Initiative, Atlanta, USA
  • Source: Volume 4, Issue 6, November 2010, p. 428 – 440
    DOI:  10.1049/iet-syb.2010.0009 , Print ISSN 1751-8849, Online ISSN 1751-8857
© The Institution of Engineering and Technology
Published

A critical task in systems biology is the identification of genes that interact to control cellular processes by transcriptional activation of a set of target genes. Many methods have been developed that use statistical correlations in high-throughput data sets to infer such interactions. However, cellular pathways are highly cooperative, often requiring the joint effect of many molecules. Few methods have been proposed to explicitly identify such higher-order interactions, partially due to the fact that the notion of multivariate statistical dependence itself remains imprecisely defined. The authors define the concept of dependence among multiple variables using maximum entropy techniques and introduce computational tests for their identification. Synthetic network results reveal that this procedure uncovers dependencies even in undersampled regimes, when the joint probability distribution cannot be reliably estimated. Analysis of microarray data from human B cells reveals that third-order statistics, but not second-order ones, uncover relationships between genes that interact in a pathway to cooperatively regulate a common set of targets.

Inspec keywords: cellular biophysics; maximum entropy methods; genetics; correlation methods; higher order statistics; molecular biophysics

Other keywords: multivariate dependence; maximum entropy techniques; statistical correlations; human B cells; control cellular processes; cellular pathways; transcriptional activation; gene identification; systems biology; genetic networks inference; microarray data; third-order statistics; synthetic network; joint probability distribution; target genes

Subjects: Probability theory, stochastic processes, and statistics; Biomolecular structure, configuration, conformation, and active sites; Physics of subcellular structures; Biomolecular dynamics, molecular probes, molecular pattern recognition

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