Mixtures of Regression Models for Time Course Gene Expression Data: Evaluation of Initialization and Random Effects

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2009 Dec 31

PMID 20040587

Citations 12

Authors

Theresa Scharl

Bettinan Gru

Friedrich Leisch

Affiliations

Soon will be listed here.

Abstract

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/).

Supplementary Information: Supplementary data are available at Bioinformatics online.

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