01934nas a2200193   4500008004100000245012000041210006900161300001300230490000700243520110800250653010701358653001901465653008801484100001501572700001801587700001501605700001401620856010601634       2006                        eng d00amaSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments0 amaSigPro a method to identify significantly differential express  a1096-1020 v223 aMOTIVATION: 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.10a*Algorithms Computer Simulation Gene Expression/*physiology Gene Expression Profiling/*methods *Models10aGenetic Models10aStatistical Oligonucleotide Array Sequence Analysis/*methods *Software Time Factors1 aConesa, A.1 aNueda, M., J.1 aFerrer, A.1 aTalon, M.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16481333