Random Effects Model for Multiple Pathway Analysis with Applications to Type II Diabetes Microarray Data

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• 作者：Herbert Pang ; Inyoung Kim ; Hongyu Zhao
• 关键词：Diabetes ; Gene expression analysis ; Microarray ; Pathway tests ; Random pathway effects ; Score test
• 刊名：Statistics in Biosciences
• 出版年：2015
• 出版时间：October 2015
• 年：2015
• 卷：7
• 期：2
• 页码：167-186
• 全文大小：469 KB
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• 作者单位：Herbert Pang (1) (2)
  Inyoung Kim (3)
  Hongyu Zhao (4)
  
  1. Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, ?27705, USA
  2. School of Public Health, Li Ka Shing Faculty of Medicine, Hong Kong, China
  3. Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, VA, ?24061, USA
  4. Department of Biostatistics, Yale School of Public Health, and Department of Genetics, Yale University School of Medicine, New Haven, CT, ?06520, USA
  
• 刊物类别：Mathematics and Statistics
• 刊物主题：Statistics
  Statistics for Life Sciences, Medicine and Health Sciences
  Biostatistics
  Theoretical Ecology
  
• 出版者：Springer New York
• ISSN：1867-1772

文摘

Close to three percent of the world’s population suffer from diabetes. Despite the range of treatment options available for diabetes patients, not all patients benefit from them. Investigating how different pathways correlate with phenotype of interest may help unravel novel drug targets and discover a possible cure. Many pathway-based methods have been developed to incorporate biological knowledge into the study of microarray data. Most of these methods can only analyze individual pathways but cannot deal with two or more pathways in a model based framework. This represents a serious limitation because, like genes, individual pathways do not work in isolation, and joint modeling may enable researchers to uncover patterns not seen in individual pathway-based analysis. In this paper, we propose a random effects model to analyze two or more pathways. We also derive score test statistics for significance of pathway effects. We apply our method to a microarray study of Type II diabetes. Our method may eludicate how pathways crosstalk with each other and facilitate the investigation of pathway crosstalks. Further hypothesis on the biological mechanisms underlying the disease and traits of interest may be generated and tested based on this method. Keywords Diabetes Gene expression analysis Microarray Pathway tests Random pathway effects Score test