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Updated: Jan 27, 2026

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MGSEA - a multivariate Gene set enrichment analysis.

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  • 1Institute of Statistical Science, Academia Sinica, Taipei, Taiwan.

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Summary

Multivariate GSEA (MGSEA) captures combinatorial relationships across multiple omics data types for cancer subtype discovery. This method reveals subtype-specific biomarkers and oncogenic processes, advancing multi-omics analysis.

Keywords:
Gene set enrichment analysisMultimodal OMIC data

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Gene Set Enrichment Analysis (GSEA) identifies functional biomarker categories but typically uses single-platform data.
  • Existing GSEA extensions for multimodal omics data often fail to capture combinatorial feature relationships.

Purpose of the Study:

  • To propose Multivariate GSEA (MGSEA) for identifying gene set enrichment patterns from multimodal omics data.
  • To capture combinatorial relationships between feature scores from multiple platforms.

Main Methods:

  • Developed MGSEA to analyze combinatorial relations of gene set enrichment across multiple omics platforms.
  • Applied MGSEA to The Cancer Genome Atlas (TCGA) datasets (CNV, DNA methylation, mRNA expression) for breast cancer and glioblastoma multiforme (GBM) subtype delineation.
  • Validated findings using external datasets (METABRIC, REMBRANDT).

Main Results:

  • MGSEA successfully identified designed feature relations in simulated data.
  • Analysis of TCGA, METABRIC, and REMBRANDT datasets revealed distinct and overlapping patterns of enriched functional categories for breast cancer and GBM subtypes.
  • mRNA expression and CNV were identified as dominant data types for specific subtype biomarkers in breast cancer and GBM.
  • Distinct combinatorial patterns were linked to specific oncogenic processes like cell proliferation, invasion, metastasis, and immune responses.

Conclusions:

  • MGSEA effectively infers combinatorial relationships from multimodal omics data for cancer subtype delineation.
  • The method provides insights consistent with existing knowledge and uncovers novel aspects of cancer subtypes.
  • MGSEA is applicable to various genotype-phenotype association studies utilizing multimodal omics data.