Biological Profiling of Gene Groups utilizing Gene Ontology

TL;DR

This paper introduces a statistical framework for identifying significantly enriched Gene Ontology terms in gene groups, addressing multiple testing issues and controlling false discovery rates, demonstrated on microarray data.

Contribution

The authors present a novel analytical method for GO enrichment analysis that accurately controls false positives and is robust to gene group variations.

Findings

Effective control of false discovery rate in GO enrichment

Robustness of the method to gene group composition

Comparison shows improved performance over previous approaches

Abstract

Increasingly used high throughput experimental techniques, like DNA or protein microarrays give as a result groups of interesting, e.g. differentially regulated genes which require further biological interpretation. With the systematic functional annotation provided by the Gene Ontology the information required to automate the interpretation task is now accessible. However, the determination of statistical significant e.g. molecular functions within these groups is still an open question. In answering this question, multiple testing issues must be taken into account to avoid misleading results. Here we present a statistical framework that tests whether functions, processes or locations described in the Gene Ontology are significantly enriched within a group of interesting genes when compared to a reference group. First we define an exact analytical expression for the expected number of false positives that allows us to calculate adjusted p-values to control the false discovery rate. Next, we demonstrate and discuss the capabilities of our approach using publicly available microarray data on cell-cycle regulated genes. Further, we analyze the robustness of our framework with respect to the exact gene group composition and compare the performance with earlier approaches. The software package GOSSIP implements our method and is made freely available at http://gossip.gene-groups.net/