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Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data

Nature Genetics volume 34, pages 166–176 (2003)Cite this article

Abstract

Much of a cell's activity is organized as a network of interacting modules: sets of genes coregulated to respond to different conditions. We present a probabilistic method for identifying regulatory modules from gene expression data. Our procedure identifies modules of coregulated genes, their regulators and the conditions under which regulation occurs, generating testable hypotheses in the form 'regulator X regulates module Y under conditions W'. We applied the method to a Saccharomyces cerevisiae expression data set, showing its ability to identify functionally coherent modules and their correct regulators. We present microarray experiments supporting three novel predictions, suggesting regulatory roles for previously uncharacterized proteins.

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Figure 1: Overview of the module networks algorithm and evaluation procedure.
Figure 2: Regulation programs represent context-specific and combinatorial regulation.
Figure 3: The respiration and carbon regulation module (55 genes).
Figure 4: Enrichment of annotations and motif binding sites in modules and in predicted targets of regulators.
Figure 5: Global view and higher order organization of modules.
Figure 6: Microarray experiments testing functional predictions for putative regulators.
Figure 7: Regulatory components allowing inference of regulation from expression data.

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Acknowledgements

We thank L. Garwin, M. Scott, G. Simchen and L. Stryer for their useful comments on earlier versions of this manuscript and A. Kaushal, T. Pham, A. Tanay and R. Yelensky for technical help with software and visualization. E.S., D.K. and N.F. were supported by a National Science Foundation grant under the Information Technology Research program. E.S. was also supported by a Stanford Graduate Fellowship. M.S was supported by the Stanford University School of Medicine Dean's Fellowship. A.R. was supported by the Colton Foundation. D.P. was supported by an Eshkol Fellowship. N.F. was also supported by an Alon Fellowship, by the Harry & Abe Sherman Senior Lectureship in Computer Science and by the Israeli Ministry of Science.

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Author notes

  1. Aviv Regev

    Present address: Bauer Center for Genomics Research, Harvard University, Cambridge, Massachusetts, USA

  2. Eran Segal, Aviv Regev and Dana Pe'er: These authors contributed equally to this manuscript.

Authors and Affiliations

  1. Computer Science Department, Stanford University, Stanford, 94305, California, USA

    Eran Segal & Daphne Koller

  2. Department of Genetics, Stanford University School of Medicine, Stanford, 94305, California, USA

    Michael Shapira & David Botstein

  3. Department of Cell Research and Immunology, Tel Aviv University & Computer Science Department, Weizmann Institute, Israel

    Aviv Regev

  4. School of Computer Science & Engineering, Hebrew University, Jerusalem, 91904, Israel

    Dana Pe'er & Nir Friedman

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Correspondence to Eran Segal or Daphne Koller.

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Segal, E., Shapira, M., Regev, A. et al. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet 34, 166–176 (2003). https://doi.org/10.1038/ng1165

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