maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments

TitlemaSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments
Publication TypeJournal Article
Year of Publication2006
AuthorsConesa A, Nueda MJ, Ferrer A, Talon M
Journal TitleBioinformatics
Volume22
Issue9
Pages1096-102
Keywords*Algorithms Computer Simulation Gene Expression/*physiology Gene Expression Profiling/*methods *Models, Genetic Models, Statistical Oligonucleotide Array Sequence Analysis/*methods *Software Time Factors
AbstractMOTIVATION: Multi-series time-course microarray experiments are useful approaches for exploring biological processes. In this type of experiments, the researcher is frequently interested in studying gene expression changes along time and in evaluating trend differences between the various experimental groups. The large amount of data, multiplicity of experimental conditions and the dynamic nature of the experiments poses great challenges to data analysis. RESULTS: In this work, we propose a statistical procedure to identify genes that show different gene expression profiles across analytical groups in time-course experiments. The method is a two-regression step approach where the experimental groups are identified by dummy variables. The procedure first adjusts a global regression model with all the defined variables to identify differentially expressed genes, and in second a variable selection strategy is applied to study differences between groups and to find statistically significant different profiles. The methodology is illustrated on both a real and a simulated microarray dataset.
URLhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16481333