CEA: Combination-based gene set functional enrichment analysis

• Last updated: February 28, 2018

Introduction#

Functional enrichment analysis is one of the fundamental and challenging tasks in bioinformatics. Most current enrichment analysis approaches individually evaluate the functional terms and often output a list of enriched terms with high similarity and redundancy, which is hard for the downstream studies to extract the underlying biological interpretation. In this paper, we proposed a novel statistical framework to assess the performance of combination-based enrichment analysis. Using this framework, we formulated the enrichment analysis as a multi-objective combinatorial optimization problem and developed the CEA (Combination-based Enrichment Analysis) method. We tested the effectiveness of CEA on four published microarray datasets. Enriched functional terms identified by CEA not only involve crucial biological processes of related diseases, but also have very lower redundancy and can serve as a preferable representation for the enriched terms found by traditional single-term-based methods. CEA is an efficient computational tool for functional enrichment analysis and provides an effective benchmark to evaluate the existing combination-based methods in a comprehensive perspective. CEA has been implemented in the R package CopTea, publicly available at GitHub (http://github.com/wulingyun/CopTea/). The open source codes, original datasets and additional materials are available at http://doc.aporc.org/wiki/CEA.

Reference#

• Duanchen Sun, Yin-Liang Liu, Xiang-Sun Zhang, Ling-Yun Wu. CEA: Combination-based gene set functional enrichment analysis. In submission, 2018.

Software#

R package#

The CEA method has been implemented in R package CopTea, which can be found at GitHub

Additional Materials#

• Supplementary_Materials.pdf
• CEA code and data.zip

---

Category: Supplementary Software