Collaborative Research: Novel methods for pharmacogenomic data analysis using gene clusters

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Numerous pharmacogenomic studies have been conducted using microarrays to survey the whole genome and detect disease-associated genes. Genes have the inherent clustering structure. The goal of this study is to develop a systematic framework using principal component analysis (PCA) based methods to detect gene clusters differentially expressed and/or with joint predictive power. More specifically, the investigators will (1) develop novel methodology to detect gene clusters marginally differentially expressed; (2) develop penalization methodology to detect gene clusters with joint predictive power for the disease clinical outcomes of interest; and (3) conduct extensive numerical studies, and develop publicly available software. This study will greatly advance our understanding of the ?large p, small n? statistics as well as human genomics. Methodologies developed in this study can be applied in other areas including image processing, immunology, molecular dynamics, small-angle scattering, and information retrieval.Identification of genomic markers from analysis of pharmacogenomic data is a key step in understanding human genomics and personalized medicine. The proposed study has been motivated by the urgent need to overcome drawbacks of existing methods. It will feature novel statistical methods, rigorous theoretical development, extensive numerical studies, development of public software, and a direct impact on practical studies. The proposed study will enrich the family of high dimensional methodologies in general. In addition, analysis of breast cancer, colon cancer, and lymphoma microarray data will lead to a deeper understanding of the genomic mechanisms underlying those cancers. From educational and social prospective, the proposed study will foster more intensive collaborations among investigators from different institutions and background. It will promote teaching, training and learning at Yale University and at the University of North Carolina. Moreover, the investigators will attend statistical and genomic conferences and give presentations, which may promote interdisciplinary research among scientists from diverse fields.