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PubMed · 14976028

Analysing microarray data using modular regulation analysis.

R Keira Curtis·Martin D Brand
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Abstract

MOTIVATION: Microarray experiments measure complex changes in the abundance of many mRNAs under different conditions. Current analysis methods cannot distinguish between direct and indirect effects on expression, or calculate the relative importance of mRNAs in effecting responses. RESULTS: Application of modular regulation analysis to microarray data reveals and quantifies which mRNA changes are important for cellular responses. The mRNAs are clustered, and then we calculate how perturbations alter each cluster and how strongly those clusters affect an output response. The product of these values quantifies how an input changes a response through each cluster. Two published datasets are analysed. Two mRNA clusters transmit most of the response of yeast doubling time to galactose; one contains mainly galactose metabolic genes, and the other a regulatory gene. Analysis of the response of yeast relative fitness to 2-deoxy-D-glucose reveals that control is distributed between several mRNA clusters, but experimental error limits statistical significance.

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AlgorithmsBase SequenceCluster AnalysisDeoxyglucoseGalactoseGene Expression ProfilingGene Expression RegulationMolecular Sequence DataOligonucleotide Array Sequence AnalysisPattern Recognition, AutomatedRNA, MessengerReproducibility of ResultsSaccharomyces cerevisiaeSaccharomyces cerevisiae ProteinsSensitivity and SpecificitySequence AlignmentSequence Analysis, RNAComparative StudyEvaluation StudyJournal ArticleValidation Study

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R Keira Curtis, Martin D Brand. 2004-02-19. Analysing microarray data using modular regulation analysis.. https://doi.org/10.1093/bioinformatics%2Fbth082

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