Your browser doesn't support javascript.
loading
Comparative study on gene set and pathway topology-based enrichment methods.
Bayerlová, Michaela; Jung, Klaus; Kramer, Frank; Klemm, Florian; Bleckmann, Annalen; Beißbarth, Tim.
Afiliación
  • Bayerlová M; Department of Medical Statistics, University Medical Center Göttingen, 37099, Göttingen, Germany. Michaela.Bayerlova@med.uni-goettingen.de.
  • Jung K; Department of Medical Statistics, University Medical Center Göttingen, 37099, Göttingen, Germany. Klaus.Jung@ams.med.uni-goettingen.de.
  • Kramer F; Department of Medical Statistics, University Medical Center Göttingen, 37099, Göttingen, Germany. Frank.Kramer@med.uni-goettingen.de.
  • Klemm F; Department of Hematology and Medical Oncology, University Medical Center Göttingen, 37099, Göttingen, Germany. Florian.Klemm@med.uni-goettingen.de.
  • Bleckmann A; Department of Medical Statistics, University Medical Center Göttingen, 37099, Göttingen, Germany. A.Bleckmann@med.uni-goettingen.de.
  • Beißbarth T; Department of Hematology and Medical Oncology, University Medical Center Göttingen, 37099, Göttingen, Germany. A.Bleckmann@med.uni-goettingen.de.
BMC Bioinformatics ; 16: 334, 2015 Oct 22.
Article en En | MEDLINE | ID: mdl-26489510
ABSTRACT

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.
Asunto(s)

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Expresión Génica / Redes y Vías Metabólicas / Modelos Genéticos Idioma: En Revista: BMC Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2015 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Expresión Génica / Redes y Vías Metabólicas / Modelos Genéticos Idioma: En Revista: BMC Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2015 Tipo del documento: Article