Gene expression studies have been playing a pivotal role for the research on many complexdiseases. With the high dimensionality and noisy nature of data, the analysis of gene expressionstudies, despite many promising findings, is still often unsatisfactory. In recent omics studies, aprominent trend is to conduct multidimensional studies, where gene expressions are profiledalong with their regulators (methylation, copy number variation, microRNA, and others). In aseries of studies, we have developed assisted analysis techniques, which use regulatorinformation to assist the regression, clustering, and other analysis of gene expression data. Theassisted analysis differs from the analysis of gene expression data only and integrated analysisin multiple aspects. Numerical and statistical investigations show promising performance of theassisted analysis.