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


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


BACKGROUND
Supplementary data are available at Bioinformatics online.
Scharl
Theresa
T
Department of Statistics and Probability Theory, Vienna University of Technology, Wiedner Hauptstr. 8-10, A-1040 Vienna, Austria. theresa.scharl@ci.tuwien.ac.at
Grü
Bettinan
B
Leisch
Friedrich
F
D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D004926:Escherichia coli / Q000235:genetics; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D016014:Linear Models; D015233:Models, Statistical