How to decide which are the most pertinent overly-represented features during gene set enrichment analysis.
BACKGROUND
The search for enriched features has become widely used to characterize a set of genes or proteins. A key aspect of this technique is its ability to identify correlations amongst heterogeneous data such as Gene Ontology annotations, gene expression data and genome location of genes. Despite the rapid growth of available data, very little has been proposed in terms of formalization and optimization. Additionally, current methods mainly ignore the structure of the data which causes results redundancy. For example, when searching for enrichment in GO terms, genes can be annotated with multiple GO terms and should be propagated to the more general terms in the Gene Ontology. Consequently, the gene sets often overlap partially or totally, and this causes the reported enriched GO terms to be both numerous and redundant, hence, overwhelming the researcher with non-pertinent information. This situation is not unique, it arises whenever some hierarchical clustering is performed (e.g. based on the gene expression profiles), the extreme case being when genes that are neighbors on the chromosomes are considered.


RESULTS
We present a generic framework to efficiently identify the most pertinent over-represented features in a set of genes. We propose a formal representation of gene sets based on the theory of partially ordered sets (posets), and give a formal definition of target set pertinence. Algorithms and compact representations of target sets are provided for the generation and the evaluation of the pertinent target sets. The relevance of our method is illustrated through the search for enriched GO annotations in the proteins involved in a multiprotein complex. The results obtained demonstrate the gain in terms of pertinence (up to 64% redundancy removed), space requirements (up to 73% less storage) and efficiency (up to 98% less comparisons).


CONCLUSIONS
The generic framework presented in this article provides a formal approach to adequately represent available data and efficiently search for pertinent over-represented features in a set of genes or proteins. The formalism and the pertinence definition can be directly used by most of the methods and tools currently available for feature enrichment analysis.
Barriot
Roland
R
CBiB, Bordeaux Bioinformatics Center, Université Victor Segalen Bordeaux 2, 146 Rue Léo Saignat, 33076 Bordeaux, France. rbarriot@esat.kuleuven.be
Sherman
David J
DJ
Dutour
Isabelle
I
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D044962:Data Compression / Q000379:methods*; D003628:Database Management Systems; D030541:Databases, Genetic / Q000706:statistics & numerical data; D030562:Databases, Protein / Q000706:statistics & numerical data; D004526:Efficiency; D020869:Gene Expression Profiling / Q000379:methods* / Q000706:statistics & numerical data; D007257:Information Theory; D010363:Pattern Recognition, Automated; D011506:Proteins / Q000145:classification / Q000235:genetics / Q000378:metabolism; D013329:Structure-Activity Relationship; D009626:Terminology as Topic; D014941:Work Simplification
D011506:Proteins