Modelling Time Course Gene Expression Data with Finite Mixtures of Linear Additive Models

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2011 Nov 29

PMID 22121159

Citations 3

Authors

Bettina Grun

Theresa Scharl

Friedrich Leisch

Affiliations

Soon will be listed here.

Abstract

Unlabelled: A model class of finite mixtures of linear additive models is presented. The component-specific parameters in the regression models are estimated using regularized likelihood methods. The advantages of the regularization are that (i) the pre-specified maximum degrees of freedom for the splines is less crucial than for unregularized estimation and that (ii) for each component individually a suitable degree of freedom is selected in an automatic way. The performance is evaluated in a simulation study with artificial data as well as on a yeast cell cycle dataset of gene expression levels over time.

Availability: The latest release version of the R package flexmix is available from CRAN (http://cran.r-project.org/).

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