02714nas a2200253   4500008004100000245009200041210006900133300001300202490000700215520168800222653010801910653001202018653001902030653005802049653012102107100001802228700001502246700002302261700002202284700001902306700001402325700001502339856010602354       2007                        eng d00aDiscovering gene expression patterns in time course microarray experiments by ANOVA-SCA0 aDiscovering gene expression patterns in time course microarray e  a1792-8000 v233 aMOTIVATION: Designed microarray experiments are used to investigate the effects that controlled experimental factors have on gene expression and learn about the transcriptional responses associated with external variables. In these datasets, signals of interest coexist with varying sources of unwanted noise in a framework of (co)relation among the measured variables and with the different levels of the studied factors. Discovering experimentally relevant transcriptional changes require methodologies that take all these elements into account. RESULTS: In this work, we develop the application of the Analysis of variance-simultaneous component analysis (ANOVA-SCA) Smilde et al. Bioinformatics, (2005) to the analysis of multiple series time course microarray data as an example of multifactorial gene expression profiling experiments. We denoted this implementation as ASCA-genes. We show how the combination of ANOVA-modeling and a dimension reduction technique is effective in extracting targeted signals from data by-passing structural noise. The methodology is valuable for identifying main and secondary responses associated with the experimental factors and spotting relevant experimental conditions. We additionally propose a novel approach for gene selection in the context of the relation of individual transcriptional patterns to global gene expression signals. We demonstrate the methodology on both real and synthetic datasets. AVAILABILITY: ASCA-genes has been implemented in the statistical language R and is available at http://www.ivia.es/centrodegenomica/bioinformatics.htm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.10aAlgorithms *Analysis of Variance Computational Biology/*methods Computer Simulation Data Interpretation10aGenetic10aGenetic Models10aStatistical Gene Expression Profiling/*methods Models10aStatistical Oligonucleotide Array Sequence Analysis/*methods Principal Component Analysis Time Factors Transcription1 aNueda, M., J.1 aConesa, A.1 aWesterhuis, J., A.1 aHoefsloot, H., C.1 aSmilde, A., K.1 aTalon, M.1 aFerrer, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1751925001934nas 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