Journal article
Integrative analysis of high-throughput cancer studies with contrasted penalization
Genetic epidemiology, Vol.38(2), pp.144-151
02/2014
DOI: 10.1002/gepi.21781
PMCID: PMC4355402
PMID: 24395534
Abstract
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.
Details
- Title: Subtitle
- Integrative analysis of high-throughput cancer studies with contrasted penalization
- Creators
- Xingjie Shi - Shanghai University of Finance and EconomicsJin Liu - Division of Epidemiology and Biostatistics, UIC School of Public Health, Chicago, Illinois, United States of AmericaJian Huang - University of IowaYong Zhou - Shanghai University of Finance and EconomicsBenChang ShiaShuangge Ma - Yale University
- Resource Type
- Journal article
- Publication Details
- Genetic epidemiology, Vol.38(2), pp.144-151
- DOI
- 10.1002/gepi.21781
- PMID
- 24395534
- PMCID
- PMC4355402
- NLM abbreviation
- Genet Epidemiol
- ISSN
- 0741-0395
- eISSN
- 1098-2272
- Grant note
- R01 CA142774 / NCI NIH HHS CA142774 / NCI NIH HHS R21 CA165923 / NCI NIH HHS CA165923 / NCI NIH HHS
- Language
- English
- Date published
- 02/2014
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257623202771
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