Work

Variable selection and regression analysis for graph-structured covariates with an application to genomics

Year: 2010

Type: article

Abstract: Graphs and networks are common ways of depicting information. In biology, many different biological processes are represented by graphs, such as regulatory networks, metabolic pathways and protein-protein interaction networks. This kind of a priori use of graphs is a useful supplement to the standard numerical data such as microarray gene expression data. In this paper, we consider the problem of regression analysis and variable selection when the covariates are linked on a graph. We study a graph-constrained regularization procedure and its theoretical properties for regression analysis to take into account the neighborhood information of the variables measured on a graph, where a smoothness penalty on the coefficients is defined as a quadratic form of the Laplacian matrix associated with the graph. We establish estimation and model selection consistency results and provide estimation bounds for both fixed and diverging numbers of parameters in regression models. We demonstrate by simulations and a real dataset that the proposed procedure can lead to better variable selection and prediction than existing methods that ignore the graph information associated with the covariates.

Source: The Annals of Applied Statistics

DOI: 10.1214/10-aoas332

Authors: Caiyan Li, Hongzhe Li

Institution: University of Pennsylvania

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: bronze

Funders: National Institutes of Health, National Cancer Institute, National Institute of Environmental Health Sciences

Awards: G105840718, Survival analysis methods in genetic studies, G9000076367