Integrative Analysis of High-throughput Cancer Studies With Contrasted Penalization

Xingjie Shi, Jin Liu, Jian Huang, Yong Zhou, Benchang Shia, Shuangge Ma

研究成果: 雜誌貢獻文章

13 引文 斯高帕斯(Scopus)

摘要

In cancer studies with high-throughput genetic and genomic measurements, integrative analysis provides a way to effectively pool and analyze heterogeneous raw data from multiple independent studies and outperforms "classic" meta-analysis and single-dataset analysis. When marker selection is of interest, the genetic basis of multiple datasets can be described using the homogeneity model or the heterogeneity model. In this study, we consider marker selection under the heterogeneity model, which includes the homogeneity model as a special case and can be more flexible. Penalization methods have been developed in the literature for marker selection. This study advances from the published ones by introducing the contrast penalties, which can accommodate the within- and across-dataset structures of covariates/regression coefficients and, by doing so, further improve marker selection performance. Specifically, we develop a penalization method that accommodates the across-dataset structures by smoothing over regression coefficients. An effective iterative algorithm, which calls an inner coordinate descent iteration, is developed. Simulation shows that the proposed method outperforms the benchmark with more accurate marker identification. The analysis of breast cancer and lung cancer prognosis studies with gene expression measurements shows that the proposed method identifies genes different from those using the benchmark and has better prediction performance.

原文英語
頁(從 - 到)144-151
頁數8
期刊Genetic Epidemiology
38
發行號2
DOIs
出版狀態已發佈 - 二月 2014
對外發佈Yes

ASJC Scopus subject areas

  • Genetics(clinical)
  • Epidemiology

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