Journal article
Integrative analysis of multiple cancer genomic datasets under the heterogeneity model
Statistics in medicine, Vol.32(20), pp.3509-3521
09/10/2013
DOI: 10.1002/sim.5780
PMCID: PMC3743947
PMID: 23519988
Abstract
In the analysis of cancer studies with high-dimensional genomic measurements, integrative analysis provides an effective way of pooling information across multiple heterogeneous datasets. The genomic basis of multiple independent datasets, which can be characterized by the sets of genomic markers, can be described using the homogeneity model or heterogeneity model. Under the homogeneity model, all datasets share the same set of markers associated with responses. In contrast, under the heterogeneity model, different studies have overlapping but possibly different sets of markers. The heterogeneity model contains the homogeneity model as a special case and can be much more flexible. Marker selection under the heterogeneity model calls for bi-level selection to determine whether a covariate is associated with response in any study at all as well as in which studies it is associated with responses. In this study, we consider two minimax concave penalty-based penalization approaches for marker selection under the heterogeneity model. For each approach, we describe its rationale and an effective computational algorithm. We conduct simulations to investigate their performance and compare with the existing alternatives. We also apply the proposed approaches to the analysis of gene expression data on multiple cancers.
Details
- Title: Subtitle
- Integrative analysis of multiple cancer genomic datasets under the heterogeneity model
- Creators
- Jin Liu - Department of Biostatistics, School of Public Health, Yale University, 60 College Street, New Haven, CT 06520, U.S.AJian HuangShuangge Ma
- Resource Type
- Journal article
- Publication Details
- Statistics in medicine, Vol.32(20), pp.3509-3521
- DOI
- 10.1002/sim.5780
- PMID
- 23519988
- PMCID
- PMC3743947
- NLM abbreviation
- Stat Med
- ISSN
- 0277-6715
- eISSN
- 1097-0258
- Publisher
- England
- Grant note
- R01 CA142774 / NCI NIH HHS CA142774 / NCI NIH HHS R21 CA165923 / NCI NIH HHS R01 CA152301 / NCI NIH HHS CA152301 / NCI NIH HHS CA165923 / NCI NIH HHS
- Language
- English
- Date published
- 09/10/2013
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9983985833102771
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