Journal article
Avoiding the pitfalls of gene set enrichment analysis with SetRank.
- Simillion C Interfaculty Bioinformatics Unit and SIB Swiss Institute of Bioinformatics, University of Bern, Baltzerstrasse 6, 3012, Berne, Switzerland. cedric.simillion@dkf.unibe.ch.
- Liechti R Vital-IT, SIB Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Genopode, 1015, Lausanne, Switzerland.
- Lischer HE Interfaculty Bioinformatics Unit and SIB Swiss Institute of Bioinformatics, University of Bern, Baltzerstrasse 6, 3012, Berne, Switzerland.
- Ioannidis V Vital-IT, SIB Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Genopode, 1015, Lausanne, Switzerland.
- Bruggmann R Interfaculty Bioinformatics Unit and SIB Swiss Institute of Bioinformatics, University of Bern, Baltzerstrasse 6, 3012, Berne, Switzerland. remy.bruggmann@bioinformatics.unibe.ch.
- 2017-03-06
Published in:
- BMC bioinformatics. - 2017
Algorithm
Functional genomics
GSEA
Gene set enrichment analysis
Pathway analysis
R package
Sample source bias
Web interface
Algorithms
Brain
Computational Biology
Databases, Genetic
Gene Expression Profiling
Genome, Human
Genomics
Humans
Models, Theoretical
Neoplasms
Reproducibility of Results
Sensitivity and Specificity
English
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.
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.
- Language
-
- English
- Open access status
- gold
- Identifiers
-
- DOI 10.1186/s12859-017-1571-6
- PMID 28259142
- Persistent URL
- https://sonar.ch/global/documents/278700
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