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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2018-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC5784953?pdf=render |
| _version_ | 1828486426425032704 |
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| author | Ivana Ihnatova Vlad Popovici Eva Budinska |
| author_facet | Ivana Ihnatova Vlad Popovici Eva Budinska |
| author_sort | Ivana Ihnatova |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-11T09:32:54Z |
| format | Article |
| id | doaj.art-4fe18090041345a6914e82496597657b |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-11T09:32:54Z |
| publishDate | 2018-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-4fe18090041345a6914e82496597657b2022-12-22T01:12:58ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-01131e019115410.1371/journal.pone.0191154A critical comparison of topology-based pathway analysis methods.Ivana IhnatovaVlad PopoviciEva BudinskaOne 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.http://europepmc.org/articles/PMC5784953?pdf=render |
| spellingShingle | Ivana Ihnatova Vlad Popovici Eva Budinska A critical comparison of topology-based pathway analysis methods. PLoS ONE |
| 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 |
| url | http://europepmc.org/articles/PMC5784953?pdf=render |
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