OCEAN: Flexible Feature Set Aggregation for Analysis of Multi-omics Data
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866914988992495616 |
|---|---|
| author | Ebrahimpoor, Mitra Menezes, Renee Xu, Ningning Goeman, Jelle J. |
| author_facet | Ebrahimpoor, Mitra Menezes, Renee Xu, Ningning Goeman, Jelle J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19523 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | OCEAN: Flexible Feature Set Aggregation for Analysis of Multi-omics Data Ebrahimpoor, Mitra Menezes, Renee Xu, Ningning Goeman, Jelle J. Methodology 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. |
| title | OCEAN: Flexible Feature Set Aggregation for Analysis of Multi-omics Data |
| topic | Methodology |
| url | https://arxiv.org/abs/2410.19523 |