ArticleScientific reports2014
Annotation enrichment analysis: an alternative method for evaluating the functional properties of gene sets.
Article in Scientific reports, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
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- Contribution of brain pericytes in blood-brain barrier formation and maintenance: a transcriptomic study of cocultured human endothelial cells derived from hematopoietic stem cells.Fluids and barriers of the CNS · 2020Article
- Simultaneous Enrichment Analysis of all Possible Gene-sets: Unifying Self-Contained and Competitive Methods.Briefings in bioinformatics · 2020Review
- Targeting redox heterogeneity to counteract drug tolerance in replicatingScience translational medicine · 2019Article
- Assessment of network module identification across complex diseases.Nature methods · 2019Article
- Analysis of the common bean (Phaseolus vulgaris L.) transcriptome regarding efficiency of phosphorus use.PloS one · 2019Article
- Data on genome annotation and analysis of earthwormData in brief · 2018Article
- SLALOM, a flexible method for the identification and statistical analysis of overlapping continuous sequence elements in sequence- and time-series data.BMC bioinformatics · 2018Article
- Genomic and Genotypic Characterization ofFrontiers in microbiology · 2018Article
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Abstract
Gene annotation databases (compendiums maintained by the scientific community that describe the biological functions performed by individual genes) are commonly used to evaluate the functional properties of experimentally derived gene sets. Overlap statistics, such as Fishers Exact test (FET), are often employed to assess these associations, but don't account for non-uniformity in the number of genes annotated to individual functions or the number of functions associated with individual genes. We find FET is strongly biased toward over-estimating overlap significance if a gene set has an unusually high number of annotations. To correct for these biases, we develop Annotation Enrichment Analysis (AEA), which properly accounts for the non-uniformity of annotations. We show that AEA is able to identify biologically meaningful functional enrichments that are obscured by numerous false-positive enrichment scores in FET, and we therefore suggest it be used to more accurately assess the biological properties of gene sets.
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