Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis

We developed a joint latent variable model for integrative clustering. We call the resulting methodology iCluster. iCluster incorporates flexible modeling of the associations between different data types and the variance–covariance structure within data types in a single framework, while simultaneously reducing the dimensionality of the datasets. Likelihood-based inference is obtained through the Expectation–Maximization algorithm.

We demonstrate the iCluster algorithm using two examples of joint analysis of copy number and gene expression data, one from breast cancer and one from lung cancer. In both cases, we identified subtypes characterized by concordant DNA copy number changes and gene expression as well as unique profiles specific to one or the other in a completely automated fashion. In addition, the algorithm discovers potentially novel subtypes by combining weak yet consistent alteration patterns across data types. Availability: R code to implement iCluster can be downloaded at http://www.mskcc.org/mskcc/html/85130.cfm Contact: shenr@mskcc.org Supplementary information: Supplementary data are available at Bioinformatics online.