Cargando…
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 (i) the pre-specified maximum degrees of freedom for the spli...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2012
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3259441/ https://www.ncbi.nlm.nih.gov/pubmed/22121159 http://dx.doi.org/10.1093/bioinformatics/btr653 |
Sumario: | 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 (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/). Contact: Bettina.Gruen@jku.at |
---|