Multiple Types Of Omics Data Research Articles

Integrated enrichment analysis and pathway-centered visualization of metabolomics, proteomics, transcriptomics, and genomics data by using the InCroMAP software.

In systems biology, the combination of multiple types of omics data, such as metabolomics, proteomics, transcriptomics, and genomics, yields more information on a biological process than the analysis of a single type of data. Thus, data from different omics platforms is usually combined in one experimental setup to obtain insight into a biological process or a disease state. Particularly high accuracy metabolomics data from modern mass spectrometry instruments is currently more and more integrated into biological studies. Reflecting this trend, we extended InCroMAP, a data integration, analysis and visualization tool for genomics, transcriptomics, and proteomics data. Now, the tool is able to perform an integrated enrichment analysis and pathway-based visualization of multi-omics data and thus, it is suitable for the evaluation of comprehensive systems biology studies.

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 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.

Abstract P2-06-06: Integrating multiple molecular profiles with pathways to learn sub-type specific interactions with PARADIGM

Abstract High-dimensional omics profiling provides a detailed molecular view of individual breast cancers. We extended the Paradigm algorithm, a pathway analysis method for combining multiple omics data types with curated interactions on 7132 genes, to learn the strength and direction of curated interactions. Using only a single dataset, the 568 samples from the TCGA breast cancer cohort, we found strong high-throughput support for the majority of curated interactions. Gene set enrichment found that targets of the strongest interactions were significantly enriched for apoptosis and cell morphogenesis, and that strong regulators were significantly associated with phosphorylation. Within the TCGA breast cancer cohort the interactions some of the strongest support centered around ER alpha, IL23, GREB1, AKT1, and p53. We also assessed different strength between breast cancer subtypes, and found interactions associated with the MYC pathway and the ER alpha network to be among the most differential between basal and luminal A subtypes. Finally, we extended the method to assess the contextual dependence of interactions and found significant correlation between FOXM1 and JUN in regulation of CDK1. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P2-06-06.

A Sparse PLS for Variable Selection when Integrating Omics Data

Recent biotechnology advances allow for multiple types of omics data, such as transcriptomic, proteomic or metabolomic data sets to be integrated. The problem of feature selection has been addressed several times in the context of classification, but needs to be handled in a specific manner when integrating data. In this study, we focus on the integration of two-block data that are measured on the same samples. Our goal is to combine integration and simultaneous variable selection of the two data sets in a one-step procedure using a Partial Least Squares regression (PLS) variant to facilitate the biologists' interpretation. A novel computational methodology called ;;sparse PLS" is introduced for a predictive analysis to deal with these newly arisen problems. The sparsity of our approach is achieved with a Lasso penalization of the PLS loading vectors when computing the Singular Value Decomposition. Sparse PLS is shown to be effective and biologically meaningful. Comparisons with classical PLS are performed on a simulated data set and on real data sets. On one data set, a thorough biological interpretation of the obtained results is provided. We show that sparse PLS provides a valuable variable selection tool for highly dimensional data sets.