title: Avoiding the pitfalls of gene set enrichment analysis with SetRank.
abstract: BACKGROUND
  The purpose of gene set enrichment analysis (GSEA) is to find general trends in the huge lists of genes or proteins generated by many functional genomics techniques and bioinformatics analyses.
  
  
  RESULTS
  Here we present SetRank, an advanced GSEA algorithm which is able to eliminate many false positive hits. The key principle of the algorithm is that it discards gene sets that have initially been flagged as significant, if their significance is only due to the overlap with another gene set. The algorithm is explained in detail and its performance is compared to that of other methods using objective benchmarking criteria. Furthermore, we explore how sample source bias can affect the results of a GSEA analysis.
  
  
  CONCLUSIONS
  The benchmarking results show that SetRank is a highly specific tool for GSEA. Furthermore, we show that the reliability of results can be improved by taking sample source bias into account. SetRank is available as an R package and through an online web interface.
authors.affiliation: Interfaculty Bioinformatics Unit and SIB Swiss Institute of Bioinformatics, University of Bern, Baltzerstrasse 6, 3012, Berne, Switzerland. cedric.simillion@dkf.unibe.ch.|Department of Clinical Research, University of Bern, Murtenstrasse 35, 3008, Berne, Switzerland. cedric.simillion@dkf.unibe.ch.
authors.forename: Cedric
authors.initials: C
authors.lastname: Simillion
authors.affiliation: Vital-IT, SIB Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Genopode, 1015, Lausanne, Switzerland.
authors.forename: Robin
authors.initials: R
authors.lastname: Liechti
authors.affiliation: Interfaculty Bioinformatics Unit and SIB Swiss Institute of Bioinformatics, University of Bern, Baltzerstrasse 6, 3012, Berne, Switzerland.|Present Address: URPP Evolution in Action; Institute of Evolutionary Biology and Environmental Studies (IEU), University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland.
authors.forename: Heidi E L
authors.initials: HE
authors.lastname: Lischer
authors.affiliation: Vital-IT, SIB Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Genopode, 1015, Lausanne, Switzerland.|SIB Technology, SIB Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Genopode, 1015, Lausanne, Switzerland.
authors.forename: Vassilios
authors.initials: V
authors.lastname: Ioannidis
authors.affiliation: Interfaculty Bioinformatics Unit and SIB Swiss Institute of Bioinformatics, University of Bern, Baltzerstrasse 6, 3012, Berne, Switzerland. remy.bruggmann@bioinformatics.unibe.ch.
authors.forename: Rémy
authors.identifier: http://orcid.org/0000-0003-4733-7922
authors.initials: R
authors.lastname: Bruggmann
mesh_terms: D000465:Algorithms; D001921:Brain / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D015894:Genome, Human; D023281:Genomics; D006801:Humans; D008962:Models, Theoretical; D009369:Neoplasms / Q000235:genetics; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Algorithm; Functional genomics; GSEA; Gene set enrichment analysis; Pathway analysis; R package; Sample source bias; Web interface