Model-based gene set analysis for Bioconductor

Abstract

Gene Ontology and other forms of gene-category analysis play a major role in the evaluation of high-throughput experiments in molecular biology. Single-category enrichment analysis procedures such as Fisher's exact test tend to flag large numbers of redundant categories as significant, which can complicate interpretation. We have recently developed an approach called model-based gene set analysis (MGSA), that substantially reduces the number of redundant categories returned by the gene-category analysis. In this work, we present the Bioconductor package mgsa, which makes the MGSA algorithm available to users of the R language. Our package provides a simple and flexible application programming interface for applying the approach.

Availability: The mgsa package has been made available as part of Bioconductor 2.8. It is released under the conditions of the Artistic license 2.0.

Contact: peter.robinson@charite.de; julien.gagneur@embl.de.

Fig. 1.

Transcription factor target set enrichment. The posterior probability is shown for the 10 transcription factors with highest marginal probabilities. Categories whose posterior is above 0.5 are interpreted to be ‘active’ according to the MGSA model (Bauer et al., 2010).