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

Eran Segal; Michael Shapira; Aviv Regev; Dana Pe'er; David Botstein; Daphne Koller; Nir Friedman

doi:10.1038/ng1165

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

Eran Segal^*, Michael Shapira, Aviv Regev, Dana Pe'er, David Botstein, Daphne Koller, Nir Friedman

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

1335 Scopus citations

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.

Original language	English
Pages (from-to)	166-176
Number of pages	11
Journal	Nature Genetics
Volume	34
Issue number	2
DOIs	https://doi.org/10.1038/ng1165
State	Published - 1 Jun 2003

Bibliographical note

Funding Information:
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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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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AU - Koller, Daphne

AU - Friedman, Nir

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

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