Sparse Integrative Clustering of Multiple Omics Data Sets

High resolution microarrays and second-generation sequencing platforms are powerful tools to investigate genome-wide alterations in DNA copy number, methylation, and gene expression associated with a disease. An integrated genomic profiling approach measuring multiple omics data types simultaneously in the same set of biological samples would render an integrated data resolution that would not be available with any single data type. In a previous publication (Shen et al., 2009), we proposed a latent variable regression with a lasso constraint (Tibshirani, 1996) for joint modeling of multiple omics data types to identify common latent variables that can be used to cluster patient samples into biologically and clinically relevant disease subtypes. The resulting sparse coefficient vectors (with many zero elements) can be used to reveal important genomic features that have significant contributions to the latent variables. In this study, we consider a combination of lasso, fused lasso (Tibshirani et al., 2005) and elastic net (Zou & Hastie, 2005) penalties and use an iterative ridge regression to compute the sparse coefficient vectors. In model selection, a uniform design (Fang & Wang, 1994) is used to seek “experimental” points that scattered uniformly across the search domain for efficient sampling of tuning parameter combinations. We compared our method to sparse singular value decomposition (SVD) and penalized Gaussian mixture model (GMM) using both real and simulated data sets. The proposed method is applied to integrate genomic, epigenomic, and transcriptomic data for subtype analysis in breast and lung cancer data sets.