Multivariate dependence and genetic networks inference

A. A. Margolin, K. Wang, A. Califano, I. Nemenman

Research output: Contribution to journalArticle

27 Scopus citations

Abstract

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. Howevercellular pathways are highly cooperativeoften requiring the joint effect of many molecules. Few methods have been proposed to explicitly identify such higher-order interactionspartially 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 regimeswhen the joint probability distribution cannot be reliably estimated. Analysis of microarray data from human B cells reveals that third-order statisticsbut not second-order onesuncover relationships between genes that interact in a pathway to cooperatively regulate a common set of targets.

Original languageEnglish (US)
Pages (from-to)428-440
Number of pages13
JournalIET Systems Biology
Volume4
Issue number6
DOIs
StatePublished - Nov 1 2010
Externally publishedYes

ASJC Scopus subject areas

  • Biotechnology
  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Cell Biology

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    Margolin, A. A., Wang, K., Califano, A., & Nemenman, I. (2010). Multivariate dependence and genetic networks inference. IET Systems Biology, 4(6), 428-440. https://doi.org/10.1049/iet-syb.2010.0009