MOGSA: integrative single sample gene-set analysis of multiple omics data.

MOGSA: integrative single sample gene-set analysis of multiple omics data. Mol Cell Proteomics. 2019 Jun 26;: Authors: Meng C, Basunia A, Peters B, Moghaddas Gholami A, Kuster B, Culhane AC Abstract Gene-set analysis (GSA) summarizes individual molecular measurements to more interpretable pathways or gene-sets and has become an indispensable step in the interpretation of large-scale omics data. However, GSA methods are limited to the analysis of single omics data. Here, we introduce a new computation method termed multi-omics gene-set analysis (MOGSA), a multivariate single sample gene-set analysis method that integrates multiple experimental and molecular data types measured over the same set of samples. The method learns a low dimensional representation of most variant correlated features (genes, proteins, etc.) across multiple omics data sets, transforms the features onto the same scale and calculates an integrated gene-set score from the most informative features in each data type. MOGSA does not require filtering data to the intersection of features (gene IDs), therefore, all molecular features, including those that lack annotation may be included in the analysis. Using simulated data, we demonstrate that integrating multiple diverse sources of molecular data increases the power to discover subtle changes in gene-sets and may reduce the impact of unreliable information in any single data type. Using real experimental data, we de...
Source: Molecular and Cellular Proteomics : MCP - Category: Molecular Biology Authors: Tags: Mol Cell Proteomics Source Type: research