Integrative Analysis of Cancer Diagnosis Studies with Composite Penalization.

ABSTRACT: In cancer diagnosis studies, high-throughput gene profiling has been extensively conducted, searching for genes whose expressions may serve as markers. Data generated from such studies have the "large d, small n" feature, with the number of genes profiled much larger than the sample size. Penalization has been extensively adopted for simultaneous estimation and marker selection. Because of small sample sizes, markers identified from the analysis of single datasets can be unsatisfactory. A cost-effective remedy is to conduct integrative analysis of multiple heterogeneous datasets. In this article, we investigate composite penalization methods for estimation and marker selection in integrative analysis. The proposed methods use the minimax concave penalty (MCP) as the outer pen

SUBMITTER: Liu J 

PROVIDER: S-EPMC3933169 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature