Evolving technologies have made it cost-effective to rapidly collect diverse types of biological data. Data from genomics, transcriptomics, epigenomics, and other types of omics technologies offer an opportunity to investigate and to understand underlying biological processes. For example, in health research, this type of data may be used for understanding molecular variations among patients with the same type of disease. One approach towards analyzing this data is the application of clustering techniques. Traditionally, clustering based on distance measures or model-based approaches that incorporate a measure of probability has been used on a single type of omics data. Patient similarity networks, where patients are clustered based on multiple omics data types, have recently been highlighted as a powerful tool to capture heterogeneity underlying a patient population. First part of this presentation will focus on methods for clustering one type of omics data. Next, the presentation will move to integrating multiple omics data types and applying clustering techniques for uncovering patient similarity networks, using some ongoing research work with follicular lymphoma as an example.