Sparse integrative clustering of multiple omics data sets

Abstract

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 this study, we use penalized latent variable regression methods 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. We consider lasso (Tibshirani, 1996), elastic net (Zou and Hastie, 2005), and fused lasso (Tibshirani et al., 2005) methods to induce sparsity in the coefficient vectors, revealing important genomic features that have significant contributions to the latent variables. An iterative ridge regression is used to compute the sparse coefficient vectors. In model selection, a uniform design (Fang and 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.

Integrative and regularized principal component analysis of multiple sources of data

“…We build up five numerical examples based on those in [4, 7]. In these examples, for each type of data, clustering information is associated with a small number of functional variants (viewed as features), based on which one cluster can be distinguished from the other two, while the other two are still inseparable.…”

“…It is known that these genome-wide alterations are related to tumorigenesis and treatment response in a complex way [2]. As a result, integrative analysis of these alterations may lead to greater power in detecting tumor subtypes with important biological therapeutic differences [3, 4], as opposed to separate analysis on each data type, followed by a manual or ad hoc integration; this point has been observed in many studies [4, 5, 6, 7, 8]. …”

Statistical contributions to bioinformatics: Design, modelling, structure learning and integration

“…Lanckriet et al (2004) propose a two stage approach, first computing a kernel representation for data in each platform and subsequently combining kernels across platforms in a classification model. Mo et al (2013) and Shen et al (2013) proposed a clustering model “iCluster”, which uses a joint latent variable model to cluster samples into tumor subtypes. Through applications to breast and lung cancer data, iCluster identified potential novel tumor subtypes Similarly, Lock et al (2013) proposed an additive decomposition of variation approach consisting of low-rank approximations capturing joint variation across and within platforms, while using orthogonality constraints to ensure that patterns within and across platforms are unrelated.…”

MultiDataSet: an R package for encapsulating multiple data sets with application to omic data integration

“…Several R/Bioconductor packages have implemented methods to integrate and visualize biological data: PMA [20–22], mixOmics [23–25], made4 [26, 27], RGCCA [28, 29], omicade4 [30, 31], CNAmet [32, 33], RTopper [34, 35], iClusterPlus [36, 37] and STATegRa [38] among others. Each of these packages implements a different strategy to face the integration analysis.…”