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