Limits...
Model-based gene set analysis for Bioconductor.

Bauer S, Robinson PN, Gagneur J - Bioinformatics (2011)

Bottom Line: 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.It is released under the conditions of the Artistic license 2.0. peter.robinson@charite.de; julien.gagneur@embl.de.

View Article: PubMed Central - PubMed

Affiliation: Institute for Medical Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany.

ABSTRACT

Unlabelled: 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.

Show MeSH
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).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3117381&req=5

Figure 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).

Mentions: The plot displays the marginal probabilities of the 10 most likely sets (Fig. 1). MGSA infers changes in activity for the PHO4 transcription factor (posterior=0.9995±2×10−4). Allele variation in the transporter PHO84 affects cellular phosphate levels and regulation of the whole PHO pathway (Gagneur et al., 2009). These transcriptional changes are known to be mediated by the transcription factor PHO4, which MGSA precisely identified.Fig. 1.


Model-based gene set analysis for Bioconductor.

Bauer S, Robinson PN, Gagneur J - Bioinformatics (2011)

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).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117381&req=5

Figure 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).
Mentions: The plot displays the marginal probabilities of the 10 most likely sets (Fig. 1). MGSA infers changes in activity for the PHO4 transcription factor (posterior=0.9995±2×10−4). Allele variation in the transporter PHO84 affects cellular phosphate levels and regulation of the whole PHO pathway (Gagneur et al., 2009). These transcriptional changes are known to be mediated by the transcription factor PHO4, which MGSA precisely identified.Fig. 1.

Bottom Line: 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.It is released under the conditions of the Artistic license 2.0. peter.robinson@charite.de; julien.gagneur@embl.de.

View Article: PubMed Central - PubMed

Affiliation: Institute for Medical Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany.

ABSTRACT

Unlabelled: 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.

Show MeSH