Avoiding the pitfalls of gene set enrichment analysis with SetRank.

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Version: Final published version
Serval ID
serval:BIB_AD1953959961
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Avoiding the pitfalls of gene set enrichment analysis with SetRank.
Journal
BMC Bioinformatics
Author(s)
Simillion C., Liechti R., Lischer H.E., Ioannidis V., Bruggmann R.
ISSN
1471-2105 (Electronic)
ISSN-L
1471-2105
Publication state
Published
Issued date
2017
Peer-reviewed
Oui
Volume
18
Number
1
Pages
151
Language
english
Abstract
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.
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.
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.

Keywords
Algorithms, Brain/metabolism, Computational Biology/methods, Databases, Genetic, Gene Expression Profiling, Genome, Human, Genomics, Humans, Models, Theoretical, Neoplasms/genetics, Reproducibility of Results, Sensitivity and Specificity, Algorithm, Functional genomics, GSEA, Gene set enrichment analysis, Pathway analysis, R package, Sample source bias, Web interface
Pubmed
Web of science
Open Access
Yes
Create date
14/03/2017 11:23
Last modification date
20/08/2019 15:17
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