Regulatory networks inferred from microarray data sets provide an estimated blueprint of the functional interactions taking place under the assayed experimental conditions. In each of these experiments, the gene expression pathway exerts a finely tuned control simultaneously over all genes relevant to the cellular state. This renders most pairs of those genes significantly correlated, and therefore, the challenge faced by every method that aims at inferring a molecular regulatory network from microarray data, lies in distinguishing direct from indirect interactions. A straightforward solution to this problem would be to move directly from bivariate to multivariate statistical approaches. However, the daunting dimension of typical microarray data sets, with a number of genes p several orders of magnitude larger than the number of samples n, precludes the application of standard multivariate techniques and confronts the biologist with sophisticated procedures that address this situation. We have introduced a new way to approach this problem in an intuitive manner, based on limited-order partial correlations, and in this chapter we illustrate this method through the R package qpgraph, which forms part of the Bioconductor project and is available at its Web site (1).
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This work is supported by the Spanish Ministerio de Ciencia e Innovación (MICINN) [TIN2008-00556/TIN] and the ISCIII COMBIOMED Network [RD07/0067/0001]. R.C. is a research fellow of the “Ramon y Cajal” program from the Spanish MICINN [RYC-2006-000932]. A.R. acknowledges support from the Ministero dell’Università e della Ricerca [PRIN-2007AYHZWC].
Butte AJ, Tamayo P, Slonim D et al (2000) Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proc Natl Acad Sci U S A 97:12182–12186.PubMedCrossRefGoogle Scholar
Basso K, Margolin AA, Stolovitzky G et al (2005) Reverse engineering of regulatory networks in human B cells. Nat Genet 37:382–390.PubMedCrossRefGoogle Scholar
Faith JJ, Hayete B, Thaden JT et al (2007) Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol 5:e8.PubMedCrossRefGoogle Scholar
Falcon S, Gentleman R (2007) Using GOstats to test gene lists for GO term association. Bioinformatics 23:257–258.PubMedCrossRefGoogle Scholar
Covert MW, Knight EM, Reed JL et al (2004) Integrating high-throughput and computational data elucidates bacterial networks. Nature 429:92–96.PubMedCrossRefGoogle Scholar
Gama-Castro S, Jimenez-Jacinto V, Peralta-Gil M et al (2008) RegulonDB (version 6.0): gene regulation model of Escherichia coli K-12 beyond transcription, active (experimental) annotated promoters and Textpresso navigation. Nucleic Acids Res 36:D120–124.PubMedCrossRefGoogle Scholar