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A critical comparison of topology-based pathway analysis methods
One of the aims of high-throughput gene/protein profiling experiments is the identification of biological processes altered between two or more conditions. Pathway analysis is an umbrella term for a multitude of computational approaches used for this purpose. While in the beginning pathway analysis...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5784953/ https://www.ncbi.nlm.nih.gov/pubmed/29370226 http://dx.doi.org/10.1371/journal.pone.0191154 |
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author | Ihnatova, Ivana Popovici, Vlad Budinska, Eva |
author_facet | Ihnatova, Ivana Popovici, Vlad Budinska, Eva |
author_sort | Ihnatova, Ivana |
collection | PubMed |
description | One of the aims of high-throughput gene/protein profiling experiments is the identification of biological processes altered between two or more conditions. Pathway analysis is an umbrella term for a multitude of computational approaches used for this purpose. While in the beginning pathway analysis relied on enrichment-based approaches, a newer generation of methods is now available, exploiting pathway topologies in addition to gene/protein expression levels. However, little effort has been invested in their critical assessment with respect to their performance in different experimental setups. Here, we assessed the performance of seven representative methods identifying differentially expressed pathways between two groups of interest based on gene expression data with prior knowledge of pathway topologies: SPIA, PRS, CePa, TAPPA, TopologyGSA, Clipper and DEGraph. We performed a number of controlled experiments that investigated their sensitivity to sample and pathway size, threshold-based filtering of differentially expressed genes, ability to detect target pathways, ability to exploit the topological information and the sensitivity to different pre-processing strategies. We also verified type I error rates and described the influence of overexpression of single genes, gene sets and topological motifs of various sizes on the detection of a pathway as differentially expressed. The results of our experiments demonstrate a wide variability of the tested methods. We provide a set of recommendations for an informed selection of the proper method for a given data analysis task. |
format | Online Article Text |
id | pubmed-5784953 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-57849532018-02-09 A critical comparison of topology-based pathway analysis methods Ihnatova, Ivana Popovici, Vlad Budinska, Eva PLoS One Research Article One of the aims of high-throughput gene/protein profiling experiments is the identification of biological processes altered between two or more conditions. Pathway analysis is an umbrella term for a multitude of computational approaches used for this purpose. While in the beginning pathway analysis relied on enrichment-based approaches, a newer generation of methods is now available, exploiting pathway topologies in addition to gene/protein expression levels. However, little effort has been invested in their critical assessment with respect to their performance in different experimental setups. Here, we assessed the performance of seven representative methods identifying differentially expressed pathways between two groups of interest based on gene expression data with prior knowledge of pathway topologies: SPIA, PRS, CePa, TAPPA, TopologyGSA, Clipper and DEGraph. We performed a number of controlled experiments that investigated their sensitivity to sample and pathway size, threshold-based filtering of differentially expressed genes, ability to detect target pathways, ability to exploit the topological information and the sensitivity to different pre-processing strategies. We also verified type I error rates and described the influence of overexpression of single genes, gene sets and topological motifs of various sizes on the detection of a pathway as differentially expressed. The results of our experiments demonstrate a wide variability of the tested methods. We provide a set of recommendations for an informed selection of the proper method for a given data analysis task. Public Library of Science 2018-01-25 /pmc/articles/PMC5784953/ /pubmed/29370226 http://dx.doi.org/10.1371/journal.pone.0191154 Text en © 2018 Ihnatova et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Ihnatova, Ivana Popovici, Vlad Budinska, Eva A critical comparison of topology-based pathway analysis methods |
title | A critical comparison of topology-based pathway analysis methods |
title_full | A critical comparison of topology-based pathway analysis methods |
title_fullStr | A critical comparison of topology-based pathway analysis methods |
title_full_unstemmed | A critical comparison of topology-based pathway analysis methods |
title_short | A critical comparison of topology-based pathway analysis methods |
title_sort | critical comparison of topology-based pathway analysis methods |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5784953/ https://www.ncbi.nlm.nih.gov/pubmed/29370226 http://dx.doi.org/10.1371/journal.pone.0191154 |
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