For multiple high-dimensional problems, it is desired to conduct the integrative analysis of multiple independent datasets. Under a few important scenarios, it can be expected that the estimates of multiple datasets are “similar” in certain aspects, which may include magnitude, sparsity structure, sign, and others. The existing approaches do not have a mechanism promoting such similarity. In our study, we conduct the integrative analysis of multiple independent datasets. Penalization techniques are developed to explicitly promote similarity. The consistency properties are rigorously established. Numerical studies, including simulation and data analysis, show that the proposed approach has significant advantages over the existing benchmark.