Comparative study on gene set and pathway topology-based enrichment methods.
BACKGROUND
Enrichment analysis is a popular approach to identify pathways or sets of genes which are significantly enriched in the context of differentially expressed genes. The traditional gene set enrichment approach considers a pathway as a simple gene list disregarding any knowledge of gene or protein interactions. In contrast, the new group of so called pathway topology-based methods integrates the topological structure of a pathway into the analysis.


METHODS
We comparatively investigated gene set and pathway topology-based enrichment approaches, considering three gene set and four topological methods. These methods were compared in two extensive simulation studies and on a benchmark of 36 real datasets, providing the same pathway input data for all methods.


RESULTS
In the benchmark data analysis both types of methods showed a comparable ability to detect enriched pathways. The first simulation study was conducted with KEGG pathways, which showed considerable gene overlaps between each other. In this study with original KEGG pathways, none of the topology-based methods outperformed the gene set approach. Therefore, a second simulation study was performed on non-overlapping pathways created by unique gene IDs. Here, methods accounting for pathway topology reached higher accuracy than the gene set methods, however their sensitivity was lower.


CONCLUSIONS
We conducted one of the first comprehensive comparative works on evaluating gene set against pathway topology-based enrichment methods. The topological methods showed better performance in the simulation scenarios with non-overlapping pathways, however, they were not conclusively better in the other scenarios. This suggests that simple gene set approach might be sufficient to detect an enriched pathway under realistic circumstances. Nevertheless, more extensive studies and further benchmark data are needed to systematically evaluate these methods and to assess what gain and cost pathway topology information introduces into enrichment analysis. Both types of methods for enrichment analysis require further improvements in order to deal with the problem of pathway overlaps.
Department of Medical Statistics, University Medical Center Göttingen, 37099, Göttingen, Germany. Michaela.Bayerlova@med.uni-goettingen.de.
Michaela
M
Bayerlová
Department of Medical Statistics, University Medical Center Göttingen, 37099, Göttingen, Germany. Klaus.Jung@ams.med.uni-goettingen.de.
Klaus
K
Jung
Department of Medical Statistics, University Medical Center Göttingen, 37099, Göttingen, Germany. Frank.Kramer@med.uni-goettingen.de.
Frank
F
Kramer
Department of Hematology and Medical Oncology, University Medical Center Göttingen, 37099, Göttingen, Germany. Florian.Klemm@med.uni-goettingen.de.
Florian
F
Klemm
Department of Medical Statistics, University Medical Center Göttingen, 37099, Göttingen, Germany. A.Bleckmann@med.uni-goettingen.de.|Department of Hematology and Medical Oncology, University Medical Center Göttingen, 37099, Göttingen, Germany. A.Bleckmann@med.uni-goettingen.de.
Annalen
A
Bleckmann
Department of Medical Statistics, University Medical Center Göttingen, 37099, Göttingen, Germany. Tim.Beissbarth@ams.med.uni-goettingen.de.
Tim
T
Beißbarth
D000465:Algorithms; D003198:Computer Simulation; D025461:Feedback, Physiological; D015870:Gene Expression; D006801:Humans; D053858:Metabolic Networks and Pathways; D008957:Models, Genetic