Penalized integrative analysis of high-dimensional omics data

With omics data, results generated from single-dataset analysis are often unsatisfactory. Integrative analysis methods conduct the joint analysis of data from multiple independent studies or on multiple correlated responses, can effectively increase power, and outperform single-dataset analysis and meta-analysis. In this chapter, we review the penalized integrative analysis methods under both the homogeneity and heterogeneity models. Computation using the coordinate descent approach is described. We also discuss several important extensions. The analysis of a genome-wide association study demonstrates the applicability of reviewed methods.