Abstract Details
Activity Number:
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543
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Type:
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Contributed
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Date/Time:
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Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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Abstract #317244
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View Presentation
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Title:
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Joint Modeling of Multiplatform Omics Data with the Application to a Lung Adenocarcinoma Study
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Author(s):
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Xuebei An* and Jianhua Hu and Kim-Anh Do
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Companies:
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MD Anderson Cancer Center and MD Anderson Cancer Center and MD Anderson Cancer Center
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Keywords:
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high-dimensional data ;
integrated model ;
variable selection ;
SCAD
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Abstract:
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Genomics and proteomics data are massively produced on patient tissue samples to facilitate comprehensive understanding of biological mechanism behind a disease. We propose a statistical framework to jointly model high-throughput omics data produced from different platforms and to discover association between these genetic variables and a disease phenotype. The information of a disease phenotype (e.g. group information) can be delivered via the model parameterization. A likelihood based estimation procedure is developed, with SCAD regularization used to impose sparsity constraints on the parameters representing effects of genetic variables. Extensive simulation studies demonstrate the promise of the new method in terms of biomarker detection accuracy, comparing to conventional regression methods with penalization, such as LASSO and SCAD. We also illustrate the applicability of the proposed method using a TCGA lung adenocarcinoma data set. The mRNA data and RPPA protein expression data are used to explore associations with smoking status of lung adenocarcinoma patients.
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Authors who are presenting talks have a * after their name.
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