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Article

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

1
Department of Biological Sciences, School of Science, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
2
Wisdom Lake Academy of Pharmacy, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
*
Author to whom correspondence should be addressed.
Biomolecules 2022, 12(7), 906; https://doi.org/10.3390/biom12070906
Submission received: 5 May 2022 / Revised: 21 June 2022 / Accepted: 24 June 2022 / Published: 28 June 2022

Abstract

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.
Keywords: topology-based pathway analysis; Bayesian network; network reconstruction; gene expression topology-based pathway analysis; Bayesian network; network reconstruction; gene expression

Share and Cite

MDPI and ACS Style

Wang, Y.; Li, J.; Huang, D.; Hao, Y.; Li, B.; Wang, K.; Chen, B.; Li, T.; Liu, X. Comparing Bayesian-Based Reconstruction Strategies in Topology-Based Pathway Enrichment Analysis. Biomolecules 2022, 12, 906. https://doi.org/10.3390/biom12070906

AMA Style

Wang Y, Li J, Huang D, Hao Y, Li B, Wang K, Chen B, Li T, Liu X. Comparing Bayesian-Based Reconstruction Strategies in Topology-Based Pathway Enrichment Analysis. Biomolecules. 2022; 12(7):906. https://doi.org/10.3390/biom12070906

Chicago/Turabian Style

Wang, Yajunzi, Jing Li, Daiyun Huang, Yang Hao, Bo Li, Kai Wang, Boya Chen, Ting Li, and Xin Liu. 2022. "Comparing Bayesian-Based Reconstruction Strategies in Topology-Based Pathway Enrichment Analysis" Biomolecules 12, no. 7: 906. https://doi.org/10.3390/biom12070906

APA Style

Wang, Y., Li, J., Huang, D., Hao, Y., Li, B., Wang, K., Chen, B., Li, T., & Liu, X. (2022). Comparing Bayesian-Based Reconstruction Strategies in Topology-Based Pathway Enrichment Analysis. Biomolecules, 12(7), 906. https://doi.org/10.3390/biom12070906

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