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Abstract:
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Advances in high throughput technologies have facilitated the collection of multiple types of omics measurements, including genomics, epigenomics, proteomics, metabolomics and more. In fact, in many cases, it is now cheaper to collect multiple types of omics measurements for a single subject than to recruit new subjects and obtain additional samples. The ultimate goal is to integrate these disparate, yet related omics data sets to gain new insights into biology and human diseases. However, despite significant progress, integrative analysis methods for multiple types of omics data are still in their infancy. In this roundtable, we will discuss various modes of omics data integration and discuss statistical methods for integrative analysis of multiple types of omics measurements. PROPOSED SPONSOR: Statistics in Genomics and Genetics
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