Journal Article

Mixtures of regression models for time course gene expression data: evaluation of initialization and random effects

Theresa Scharl, Bettina Grün and Friedrich Leisch

in Bioinformatics

Volume 26, issue 3, pages 370-377
Published in print February 2010 | ISSN: 1367-4803
Published online December 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp686
Mixtures of regression models for time course gene expression data: evaluation of initialization and random effects

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Summary: Finite mixture models are routinely applied to time course microarray data. Due to the complexity and size of this type of data, the choice of good starting values plays an important role. So far initialization strategies have only been investigated for data from a mixture of multivariate normal distributions. In this work several initialization procedures are evaluated for mixtures of regression models with and without random effects in an extensive simulation study on different artificial datasets. Finally, these procedures are also applied to a real dataset from Escherichia coli.

Availability: The latest release versions of R packages flexmix, gcExplorer and kernlab are always available from CRAN (http://cran.r-project.org/).

Contact: theresa.scharl@ci.tuwien.ac.at

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5233 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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