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Comparing Bayesian-Based Reconstruction Strategies in Topology-Based Pathway Enrichment Analysis

The development of high-throughput omics technologies has enabled the quantification of vast amounts of genes and gene products in the whole genome. Pathway enrichment analysis (PEA) provides an intuitive solution for extracting biological insights from massive amounts of data. Topology-based pathwa...

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Autores principales: Wang, Yajunzi, Li, Jing, Huang, Daiyun, Hao, Yang, Li, Bo, Wang, Kai, Chen, Boya, Li, Ting, Liu, Xin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9313337/
https://www.ncbi.nlm.nih.gov/pubmed/35883462
http://dx.doi.org/10.3390/biom12070906
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author Wang, Yajunzi
Li, Jing
Huang, Daiyun
Hao, Yang
Li, Bo
Wang, Kai
Chen, Boya
Li, Ting
Liu, Xin
author_facet Wang, Yajunzi
Li, Jing
Huang, Daiyun
Hao, Yang
Li, Bo
Wang, Kai
Chen, Boya
Li, Ting
Liu, Xin
author_sort Wang, Yajunzi
collection PubMed
description The development of high-throughput omics technologies has enabled the quantification of vast amounts of genes and gene products in the whole genome. Pathway enrichment analysis (PEA) provides an intuitive solution for extracting biological insights from massive amounts of data. Topology-based pathway analysis (TPA) represents the latest generation of PEA methods, which exploit pathway topology in addition to lists of differentially expressed genes and their expression profiles. A subset of these TPA methods, such as BPA, BNrich, and PROPS, reconstruct pathway structures by training Bayesian networks (BNs) from canonical biological pathways, providing superior representations that explain causal relationships between genes. However, these methods have never been compared for their differences in the PEA and their different topology reconstruction strategies. In this study, we aim to compare the BN reconstruction strategies of the BPA, BNrich, PROPS, Clipper, and Ensemble methods and their PEA and performance on tumor and non-tumor classification based on gene expression data. Our results indicate that they performed equally well in distinguishing tumor and non-tumor samples (AUC > 0.95) yet with a varying ranking of pathways, which can be attributed to the different BN structures resulting from the different cyclic structure removal strategies. This can be clearly seen from the reconstructed JAK-STAT networks by different strategies. In a nutshell, BNrich, which relies on expert intervention to remove loops and cyclic structures, produces BNs that best fit the biological facts. The plausibility of the Clipper strategy can also be partially explained by intuitive biological rules and theorems. Our results may offer an informed reference for the proper method for a given data analysis task.
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spelling pubmed-93133372022-07-26 Comparing Bayesian-Based Reconstruction Strategies in Topology-Based Pathway Enrichment Analysis Wang, Yajunzi Li, Jing Huang, Daiyun Hao, Yang Li, Bo Wang, Kai Chen, Boya Li, Ting Liu, Xin Biomolecules Article The development of high-throughput omics technologies has enabled the quantification of vast amounts of genes and gene products in the whole genome. Pathway enrichment analysis (PEA) provides an intuitive solution for extracting biological insights from massive amounts of data. Topology-based pathway analysis (TPA) represents the latest generation of PEA methods, which exploit pathway topology in addition to lists of differentially expressed genes and their expression profiles. A subset of these TPA methods, such as BPA, BNrich, and PROPS, reconstruct pathway structures by training Bayesian networks (BNs) from canonical biological pathways, providing superior representations that explain causal relationships between genes. However, these methods have never been compared for their differences in the PEA and their different topology reconstruction strategies. In this study, we aim to compare the BN reconstruction strategies of the BPA, BNrich, PROPS, Clipper, and Ensemble methods and their PEA and performance on tumor and non-tumor classification based on gene expression data. Our results indicate that they performed equally well in distinguishing tumor and non-tumor samples (AUC > 0.95) yet with a varying ranking of pathways, which can be attributed to the different BN structures resulting from the different cyclic structure removal strategies. This can be clearly seen from the reconstructed JAK-STAT networks by different strategies. In a nutshell, BNrich, which relies on expert intervention to remove loops and cyclic structures, produces BNs that best fit the biological facts. The plausibility of the Clipper strategy can also be partially explained by intuitive biological rules and theorems. Our results may offer an informed reference for the proper method for a given data analysis task. MDPI 2022-06-28 /pmc/articles/PMC9313337/ /pubmed/35883462 http://dx.doi.org/10.3390/biom12070906 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Yajunzi
Li, Jing
Huang, Daiyun
Hao, Yang
Li, Bo
Wang, Kai
Chen, Boya
Li, Ting
Liu, Xin
Comparing Bayesian-Based Reconstruction Strategies in Topology-Based Pathway Enrichment Analysis
title Comparing Bayesian-Based Reconstruction Strategies in Topology-Based Pathway Enrichment Analysis
title_full Comparing Bayesian-Based Reconstruction Strategies in Topology-Based Pathway Enrichment Analysis
title_fullStr Comparing Bayesian-Based Reconstruction Strategies in Topology-Based Pathway Enrichment Analysis
title_full_unstemmed Comparing Bayesian-Based Reconstruction Strategies in Topology-Based Pathway Enrichment Analysis
title_short Comparing Bayesian-Based Reconstruction Strategies in Topology-Based Pathway Enrichment Analysis
title_sort comparing bayesian-based reconstruction strategies in topology-based pathway enrichment analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9313337/
https://www.ncbi.nlm.nih.gov/pubmed/35883462
http://dx.doi.org/10.3390/biom12070906
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