Modelling time course gene expression data with finite mixtures of linear additive models

Authors

Friedrich Leisch, University of WollongongFollow
Theresa Scharl
Bettina Grun, University of WollongongFollow

RIS ID

48410

Publication Details

Grun, B., Scharl, T. & Leisch, F. (2011). Modelling time course gene expression data with finite mixtures of linear additive models. Bioinformatics, 28 (2), 222-228.

Abstract

Summary: 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 (1) the pre-specified maximum degrees of freedom for the splines is less crucial than for unregularized estimation and that (2) 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 data set of gene expression levels over time.