OCEAN: Flexible Feature Set Aggregation for Analysis of Multi-omics Data

TL;DR

OCEAN extends the SEA method to multi-omics data, enabling flexible analysis of all pairwise feature sets and introducing new error rates, demonstrated on cancer datasets.

Contribution

OCEAN provides a novel, flexible framework for multi-omics feature set analysis with tailored error rates, improving interpretability and testing power.

Findings

Effective analysis of copy number and gene expression data in cancer.

Introduction of new error rates aligned with data structure.

Demonstrated utility in breast and colon cancer datasets.

Abstract

Integrated analysis of multi-omics datasets holds great promise for uncovering complex biological processes. However, the large dimension 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 paper, 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 is demonstrated by analyzing copy number and gene expression data for breast and colon cancer.