Meta-analysis for pathway enrichment analysis and biomarker detection when combining multiple genomic studies

This thesis focuses on applying meta-analysis methods for combining genomic studies on biomarker detection and pathway enrichment analysis. DNA microarray technology has been maturely developed in the past decade and led to an explosion on publicly available microarray data sets. However, the noisy nature of DNA microarray technology results in low reproducibility across microarray studies. Therefore, it is of interest to apply meta-analysis to microarray data to increase the reliability and robustness of results from individual studies. Currently most meta-analysis methods for combining genomic studies focus on biomarker detection, and meta-analysis for pathway analysis has not been systematically pursued. We investigated two natural approaches of meta-analysis for pathway enrichment (MAPE) by combining statistical significance across studies at the gene level (MAPE_G) or at the pathway level (MAPE_P). Simulation results showed increased statistical power of both approaches and their complementary advantages under different scenarios. We also developed an integrated method (MAPE_I) that incorporates advantages of both approaches. Applications to real data on drug response of a breast cancer cell line, lung and prostate cancer tissues were evaluated to compare the performance of the different methods. MAPE_P has the general advantage of not requiring gene matching across studies. When MAPE_G and MAPE_P show complementary advantages, the integrated version MAPE_I is recommended. A software package named MetaPath, was implemented to perform the MAPE analysis. In addition to developing MAPE methods, we also applied meta-analysis approach to chemotherapy research to discover robust biomarkers and multi-drug response genes, which have prognostic value and the potential of identifying new therapeutic targets