Survival models with preclustered gene groups as covariates

BMC Bioinformatics

Research Article

Marcus Schmidt¹  Michel Lang²  Jörg Rahnenführer²  Kai Kammers²  Jan G Hengstler³ 

[1] Department of Obstetrics and Gynecology, Mainz University, Medical School, Mainz, Germany;Department of Statistics, TU Dortmund University, Dortmund, Germany;Leibniz Research Centre for Working Environment and Human Factors (IfADo), TU Dortmund University, Dortmund, Germany;

关键词: Gene Ontology;    Prediction Performance;    Lasso;    Prognostic Index;    Genomic Covariates;   

DOI  :  10.1186/1471-2105-12-478

 received in 2011-06-20, accepted in 2011-12-16,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundAn important application of high dimensional gene expression measurements is the risk prediction and the interpretation of the variables in the resulting survival models. A major problem in this context is the typically large number of genes compared to the number of observations (individuals). Feature selection procedures can generate predictive models with high prediction accuracy and at the same time low model complexity. However, interpretability of the resulting models is still limited due to little knowledge on many of the remaining selected genes. Thus, we summarize genes as gene groups defined by the hierarchically structured Gene Ontology (GO) and include these gene groups as covariates in the hazard regression models. Since expression profiles within GO groups are often heterogeneous, we present a new method to obtain subgroups with coherent patterns. We apply preclustering to genes within GO groups according to the correlation of their gene expression measurements.ResultsWe compare Cox models for modeling disease free survival times of breast cancer patients. Besides classical clinical covariates we consider genes, GO groups and preclustered GO groups as additional genomic covariates. Survival models with preclustered gene groups as covariates have similar prediction accuracy as models built only with single genes or GO groups.ConclusionsThe preclustering information enables a more detailed analysis of the biological meaning of covariates selected in the final models. Compared to models built only with single genes there is additional functional information contained in the GO annotation, and compared to models using GO groups as covariates the preclustering yields coherent representative gene expression profiles.

【 授权许可】

CC BY   
© Kammers et al; licensee BioMed Central Ltd. 2011