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

Overview of attention for article published in BMC Bioinformatics, October 2015
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Title
Comparative study on gene set and pathway topology-based enrichment methods
Published in
BMC Bioinformatics, October 2015
DOI 10.1186/s12859-015-0751-5
Pubmed ID
Authors

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

Abstract

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. 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. 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. 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.

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Geographical breakdown

Country Count As %
United States 2 1%
Australia 1 <1%
France 1 <1%
Denmark 1 <1%
Luxembourg 1 <1%
Unknown 135 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 21%
Researcher 30 21%
Student > Master 20 14%
Student > Bachelor 15 11%
Professor > Associate Professor 8 6%
Other 17 12%
Unknown 21 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 39 28%
Agricultural and Biological Sciences 35 25%
Computer Science 18 13%
Medicine and Dentistry 5 4%
Engineering 5 4%
Other 15 11%
Unknown 24 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 January 2016.
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#18,436,183
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Outputs from BMC Bioinformatics
#6,321
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Outputs of similar age
#203,842
of 283,300 outputs
Outputs of similar age from BMC Bioinformatics
#119
of 136 outputs
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