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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...

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Autores principales: Bayerlová, Michaela, Jung, Klaus, Kramer, Frank, Klemm, Florian, Bleckmann, Annalen, Beißbarth, Tim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4618947/
https://www.ncbi.nlm.nih.gov/pubmed/26489510
http://dx.doi.org/10.1186/s12859-015-0751-5
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author Bayerlová, Michaela
Jung, Klaus
Kramer, Frank
Klemm, Florian
Bleckmann, Annalen
Beißbarth, Tim
author_facet Bayerlová, Michaela
Jung, Klaus
Kramer, Frank
Klemm, Florian
Bleckmann, Annalen
Beißbarth, Tim
author_sort Bayerlová, Michaela
collection PubMed
description 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0751-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-46189472015-10-25 Comparative study on gene set and pathway topology-based enrichment methods Bayerlová, Michaela Jung, Klaus Kramer, Frank Klemm, Florian Bleckmann, Annalen Beißbarth, Tim BMC Bioinformatics Research Article 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0751-5) contains supplementary material, which is available to authorized users. BioMed Central 2015-10-22 /pmc/articles/PMC4618947/ /pubmed/26489510 http://dx.doi.org/10.1186/s12859-015-0751-5 Text en © Bayerlová et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Bayerlová, Michaela
Jung, Klaus
Kramer, Frank
Klemm, Florian
Bleckmann, Annalen
Beißbarth, Tim
Comparative study on gene set and pathway topology-based enrichment methods
title Comparative study on gene set and pathway topology-based enrichment methods
title_full Comparative study on gene set and pathway topology-based enrichment methods
title_fullStr Comparative study on gene set and pathway topology-based enrichment methods
title_full_unstemmed Comparative study on gene set and pathway topology-based enrichment methods
title_short Comparative study on gene set and pathway topology-based enrichment methods
title_sort comparative study on gene set and pathway topology-based enrichment methods
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4618947/
https://www.ncbi.nlm.nih.gov/pubmed/26489510
http://dx.doi.org/10.1186/s12859-015-0751-5
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