Waldron L, Pintilie M, Tsao M-S, Shepherd FA, Huttenhower C, Jurisica I: Optimized application of penalized regression methods to diverse genomic data. Bioinformatics 2011, 27:3399–3406.
Abstract
Motivation: Penalized regression methods have been adopted widely for high-dimensional feature selection and prediction in many bioinformatic and biostatistical contexts. While their theoretical properties are well-understood, specific methodology for their optimal application to genomic data has not been determined.
Results: Through simulation of contrasting scenarios of correlated high-dimensional survival data, we compared the LASSO, Ridge and Elastic Net penalties for prediction and variable selection. We found that a 2D tuning of the Elastic Net penalties was necessary to avoid mimicking the performance of LASSO or Ridge regression. Furthermore, we found that in a simulated scenario favoring the LASSO penalty, a univariate pre-filter made the Elastic Net behave more like Ridge regression, which was detrimental to prediction performance. We demonstrate the real-life application of these methods to predicting the survival of cancer patients from microarray data, and to classification of obese and lean individuals from metagenomic data. Based on these results, we provide an optimized set of guidelines for the application of penalized regression for reproducible class comparison and prediction with genomic data.
Availability and Implementation: A parallelized implementation of the methods presented for regression and for simulation of synthetic data is provided as the pensim R package, available at http://cran.r-project.org/web/packages/pensim/index.html.
Figure 1
(A) Methodology for model selection and validation of high-dimensional data. Objectives include both feature selection and outcome prediction, e.g. for patient survival given tumor gene expression data. A nearly unbiased assessment of prediction accuracy for small samples sizes is obtained by repeating all steps of model selection in each iteration of the cross-validation. Variable selection and model conditioning are achieved within the training sets by an optional, permissive univariate pre-filter followed by repeated cross-validation for parameter tuning. These steps are detailed in Section 4. (B) Over-fitting occurs in spite of tuning the models by cross-validation, as evidenced by reduced prediction accuracy in simulated test sets compared to resubstitution of training data.