maSigPro: A method to identify significantly differential expression profiles in time-course microarray experiments. Motivation: 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. Availability: The method has been implemented in the statistical language R and is freely available from the Bioconductor contributed packages repository and from

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  1. Mayrink, Vinícius Diniz; Gonçalves, Flávio B.: Identifying atypically expressed chromosome regions using RNA-Seq data (2020)
  2. Baghfalaki, T.; Ganjali, M.; Berridge, D.: Generalized estimating equations by considering additive terms for analyzing time-course gene sets data (2018)
  3. Angelini, Claudia; De Canditiis, Daniela; Pensky, Marianna: Bayesian methods for time course microarray analysis: from genes’ detection to clustering (2012) ioport
  4. Sinha, Anshu; Markatou, Marianthi: A platform for processing expression of short time series (PESTS) (2011) ioport
  5. Wang, Junbai; Tian, Tianhai: Quantitative model for inferring dynamic regulation of the tumour suppressor gene p53 (2010) ioport
  6. Angelini, Claudia; de Canditiis, Daniela; Pensky, Marianna: Bayesian models for two-sample time-course microarray experiments (2009)
  7. Marot, Guillemette; Foulley, Jean-Louis; Jaffrézic, Florence: A structural mixed model to shrink covariance matrices for time-course differential gene expression studies (2009)
  8. Papana, Ariadni; Ishwaran, Hemant: Gene hunting with forests for multigroup time course data (2009)
  9. Hulshizer, Randall; Blalock, Eric M.: (Post hoc )pattern matching: Assigning significance to statistically defined expression patterns in single channel microarray data (2007) ioport
  10. Conesa, Ana; Nueda, María José; Ferrer, Alberto; Talón, Manuel: Masigpro: A method to identify significantly differential expression profiles in time-course microarray experiments (2006) ioport