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Mol Cell Proteomics. 2019 Aug 9;18(8 suppl 1):S153-S168. doi: 10.1074/mcp.TIR118.001251. Epub 2019 Jun 26.

MOGSA: Integrative Single Sample Gene-set Analysis of Multiple Omics Data.

Author information

1
‡Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany.
2
§Bavarian Biomolecular Mass Spectrometry Center (BayBioMS), TUM, Freising, Germany.
3
¶Department of Data Science, Division of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215.
4
‖La Jolla Institute for Allergy and Immunology, 9420 Athena Circle, La Jolla, California 92037.
5
‡Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany amin.gholami@roche.com.
6
‡Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany kuster@tum.de.
7
¶Department of Data Science, Division of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215 aedin@jimmy.harvard.edu.
8
**Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02215.

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 demonstrate three use-cases of MOGSA. First, we show how to remove a source of noise (technical or biological) in integrative MOGSA of NCI60 transcriptome and proteome data. Second, we apply MOGSA to discover similarities and differences in mRNA, protein and phosphorylation profiles of a small study of stem cell lines and assess the influence of each data type or feature on the total gene-set score. Finally, we apply MOGSA to cluster analysis and show that three molecular subtypes are robustly discovered when copy number variation and mRNA data of 308 bladder cancers from The Cancer Genome Atlas are integrated using MOGSA. MOGSA is available in the Bioconductor R package "mogsa."

KEYWORDS:

Bioinformatics software; Computational Biology; Mass Spectrometry; Metabolomics; Multi-omics integration; RNA SEQ; gene set analysis; single sample; tumor subtype

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