Evidence map›Paper›PMID 26489510›Full record

ArticleBMC bioinformatics2015

Comparative study on gene set and pathway topology-based enrichment methods.

Michaela Bayerlová, Klaus Jung, Frank Kramer, Florian Klemm, Annalen Bleckmann, Tim Beißbarth

Abstract readComparative Study
In one paragraph

Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

53 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  5. A practical guide to functional enrichment analysis.Frontiers in bioinformatics · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Michaela BayerlováDepartment of Medical Statistics, University Medical Center Göttingen, 37099, Göttingen, Germany. Michaela.Bayerlova@med.uni-goettingen.de.
Klaus JungDepartment of Medical Statistics, University Medical Center Göttingen, 37099, Göttingen, Germany. Klaus.Jung@ams.med.uni-goettingen.de.
Frank KramerDepartment of Medical Statistics, University Medical Center Göttingen, 37099, Göttingen, Germany. Frank.Kramer@med.uni-goettingen.de.
Florian KlemmDepartment of Hematology and Medical Oncology, University Medical Center Göttingen, 37099, Göttingen, Germany. Florian.Klemm@med.uni-goettingen.de.
Annalen BleckmannDepartment of Medical Statistics, University Medical Center Göttingen, 37099, Göttingen, Germany. A.Bleckmann@med.uni-goettingen.de.
Tim BeißbarthDepartment of Medical Statistics, University Medical Center Göttingen, 37099, Göttingen, Germany. Tim.Beissbarth@ams.med.uni-goettingen.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEnrichment 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.

methodsWe 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.

resultsIn 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.

conclusionsWe 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.

Indexed as

Gene ExpressionMetabolic Networks and PathwaysModels, GeneticAlgorithmsComputer SimulationFeedback, PhysiologicalHumans

Identifiers

PMID26489510
PMCPMC4618947

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.