Analysing microarray data using modular regulation analysis.

TitleAnalysing microarray data using modular regulation analysis.
Publication TypeJournal Article
Year of Publication2004
AuthorsR Curtis, K, Brand, MD
JournalBioinformatics
Volume20
Issue8
Pagination1272-84
Date Published2004 May 22
ISSN1367-4803
KeywordsAlgorithms, Base Sequence, Cluster Analysis, Deoxyglucose, Galactose, Gene Expression Profiling, Gene Expression Regulation, Molecular Sequence Data, Oligonucleotide Array Sequence Analysis, Pattern Recognition, Automated, Reproducibility of Results, RNA, Messenger, Saccharomyces cerevisiae, Saccharomyces cerevisiae Proteins, Sensitivity and Specificity, Sequence Alignment, Sequence Analysis, RNA
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.

DOI10.1093/bioinformatics/bth082
Alternate JournalBioinformatics
Citation Key10.1093/bioinformatics/bth082
PubMed ID14976028