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Brief Bioinform. 2019 Jan 18;20(1):168-177. doi: 10.1093/bib/bbx091.

Pathway enrichment analysis approach based on topological structure and updated annotation of pathway.

Author information

1
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, People's Republic of China.
2
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, People's Republic of China.

Abstract

Pathway enrichment analysis has been widely used to identify cancer risk pathways, and contributes to elucidating the mechanism of tumorigenesis. However, most of the existing approaches use the outdated pathway information and neglect the complex gene interactions in pathway. Here, we first reviewed the existing widely used pathway enrichment analysis approaches briefly, and then, we proposed a novel topology-based pathway enrichment analysis (TPEA) method, which integrated topological properties and global upstream/downstream positions of genes in pathways. We compared TPEA with four widely used pathway enrichment analysis tools, including database for annotation, visualization and integrated discovery (DAVID), gene set enrichment analysis (GSEA), centrality-based pathway enrichment (CePa) and signaling pathway impact analysis (SPIA), through analyzing six gene expression profiles of three tumor types (colorectal cancer, thyroid cancer and endometrial cancer). As a result, we identified several well-known cancer risk pathways that could not be obtained by the existing tools, and the results of TPEA were more stable than that of the other tools in analyzing different data sets of the same cancer. Ultimately, we developed an R package to implement TPEA, which could online update KEGG pathway information and is available at the Comprehensive R Archive Network (CRAN): https://cran.r-project.org/web/packages/TPEA/.

PMID:
28968630
DOI:
10.1093/bib/bbx091

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