Meta-Analysis--Based Variable Selection for Gene Expression Data

• Activity Number: 306
• Type: Contributed
• Date/Time: Tuesday, August 5, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Quefeng Li*+ and Sijian Wang and Chiang-Ching Huang and Menggang Yu and Jun Shao
• Companies: Princeton University and University of Wisconsin and University of Wisconsin-Milwaukee and University of Wisconsin-Madison and University of Wisconsin-Madison
• Keywords: gene selection ; high dimension ; meta-analysis ; oracle property

Abstract

Recent advance in biotechnology and its wide applications have led to the generation of many high-dimensional gene expression data sets that can be used to address similar biological questions. Meta-analysis plays an important role in summarizing and synthesizing scientific evidence from multiple studies. When the dimensions of datasets are high, it is desirable to incorporate variable selection into meta-analysis to improve model interpretation and prediction. According to our knowledge, all existing methods conduct variable selection with meta-data in an "all-in-or-all-out'' fashion, i.e., a gene is either selected in all of studies or not selected in any study. However, due to data heterogeneity commonly exist in meta-data, including choices of biospecimens, study population, and measurement sensitivity, it is possible that a gene is important in some studies while unimportant in others. To this end, we propose a novel method called meta-lasso for variable selection with high-dimensional meta-data. Through a hierarchical decomposition on regression coefficients, our method not only borrows strength across multiple data sets to boost the power to i