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Multiple Testing of Mix-and-Match Feature Sets in Multi-Omics.

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Statistics in medicine 2026 Vol.45(1-2) p. e70367
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Ebrahimpoor M, Menezes R, Xu N, Goeman JJ

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Integrated analysis of multi-omics datasets holds great promise for uncovering complex biological processes.

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BibTeX ↓ RIS ↓
APA Ebrahimpoor M, Menezes R, et al. (2026). Multiple Testing of Mix-and-Match Feature Sets in Multi-Omics.. Statistics in medicine, 45(1-2), e70367. https://doi.org/10.1002/sim.70367
MLA Ebrahimpoor M, et al.. "Multiple Testing of Mix-and-Match Feature Sets in Multi-Omics.." Statistics in medicine, vol. 45, no. 1-2, 2026, pp. e70367.
PMID 41567102
DOI 10.1002/sim.70367

Abstract

Integrated analysis of multi-omics datasets holds great promise for uncovering complex biological processes. However, the large dimensionality of omics data poses significant interpretability and multiple testing challenges. Simultaneous enrichment analysis (SEA) was introduced to address these issues in single-omics analysis, providing an in-built multiple testing correction and enabling simultaneous feature set testing. In this article, we introduce OCEAN, an extension of SEA to multi-omics data. OCEAN is a flexible approach to analyze potentially all possible two-way feature sets from any pair of genomics datasets. We also propose two new error rates which are in line with the two-way structure of the data and facilitate interpretation of the results. The power and utility of OCEAN are demonstrated by analyzing copy number and gene expression data for breast and colon cancer.

MeSH Terms

Humans; Breast Neoplasms; Genomics; Colonic Neoplasms; Gene Expression Profiling; Female; Data Interpretation, Statistical; DNA Copy Number Variations; Computer Simulation; Models, Statistical; Multiomics