Joint Linked Component Analysis for Multiview Data

Recent technological advances have led to increased availability of multiple sources of highcontent data. In particular, multiview data refers to different types of variables collected from the same set of individuals. One typical example is the Roadmap Epigenomics Project (Kundaje et al., 2015) which integrates information about histone marks, DNA methylation, DNA accessibility and RNA expression to infer high-resolution maps of regulatory elements annotated jointly across a total of 127 reference epigenomes spanning diverse cell and tissue types. Another example is the data used in NCI-DREAM drug sensitivity prediction challenge (Costello et al. (2014)) which contains gene expression (GE), RNA, DNA methylation (MET), copy number variation (CNV), protein abundance (RPPA) and exome sequence (EX) measurements for 53 human breast cancer cell lines. The prevalence of multiview data has motivated research on uncovering associations between different data views.