Multiple Testing of Mix‐and‐Match Feature Sets in Multi‐Omics
Mitra Ebrahimpoor, Renée Menezes, Ningning Xu, Jelle J. Goeman
TL;DR
The paper introduces OCEAN, a new method for analyzing multi-omics data that helps identify important biological features while managing multiple testing issues.
Contribution
OCEAN extends simultaneous enrichment analysis to multi-omics data and introduces new error rates for two-way feature set testing.
Findings
OCEAN enables testing of all possible two-way feature sets from paired genomics datasets.
The method was successfully applied to copy number and gene expression data in breast and colon cancer.
New error rates improve the interpretation of results in multi-omics analysis.
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.
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Taxonomy
TopicsGenomic variations and chromosomal abnormalities · Gene expression and cancer classification · Bioinformatics and Genomic Networks