a two-stage random forest-based pathway analysis method两阶段随机与森林有关的通路分析方法.pdfVIP

A Two-Stage Random Forest-Based Pathway Analysis Method Ren-Hua Chung1,2*, Ying-Erh Chen3 1 Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli, Taiwan, 2 Center for Genetic Epidemiology and Statistical Genetics, John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, Florida, United States of America, 3 Department of Economics, North Carolina State University, Raleigh, North Carolina, United States of America Abstract Pathway analysis provides a powerful approach for identifying the joint effect of genes grouped into biologically-based pathways on disease. Pathway analysis is also an attractive approach for a secondary analysis of genome-wide association study (GWAS) data that may still yield new results from these valuable datasets. Most of the current pathway analysis methods focused on testing the cumulative main effects of genes in a pathway. However, for complex diseases, gene-gene interactions are expected to play a critical role in disease etiology. We extended a random forest-based method for pathway analysis by incorporating a two-stage design. We used simulations to verify that the proposed method has the correct type I error rates. We also used simulations to show that the method is more powerful than the original random forest-based pathway approach and the set-based test implemented in PLINK in the presence of gene-gene interactions. Finally, we applied the method to a breast cancer GWAS dataset and a lung cancer GWAS dataset and interesting pathways were identified that have implications for breast and lung cancers. Citation: Chung R-H, Chen Y-E (2012) A Two-Stage Random Forest-Based Pathway Analysis Method. PLoS ONE 7(5): e36662. doi:10.1371/journal.pone.0036662 Editor: Xi-Nian Zuo,