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10. Additional file 2 of CTpathway: a CrossTalk-based pathway enrichment analysis method for cancer research

Author

Liu, Haizhou, Yuan, Mengqin, Mitra, Ramkrishna, Zhou, Xu, Long, Min, Lei, Wanyue, Zhou, Shunheng, Huang, Yu-e, Hou, Fei, Eischen, Christine M., and Jiang, Wei

Abstract

Additional file 2: Figure S1. Crosstalk effect consideration detects genes known to be associated with LUAD. Figure S2. CGC genes are in top of the RS rank list, but not top of the |log2FC| rank list. Figure S3. The proportion of risk genes, overlooked by other methods. Figure S4. Comparison of DEG proportions between pathways specifically identified by CTpathway and non-specific pathways. Figure S5. Significant pathways in data sets with a small number of DEGs, identified by CTpathway. Figure S6. Comparison of enrichment result (P-value) of target pathways for TCGA cancer stages (I, II, III and IV), obtained by different methods. Figure S7. Enrichment result (RR value) comparison of target pathways for B cell by different methods. Figure S8. An enrichment map constructed from early-stage COAD data set. Figure S9. The publicly available CTpathway web tool.

Published
2022
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19. Pathway enrichment analysis approach based on topological structure and updated annotation of pathway.

Author

Yang, Qian, Wang, Shuyuan, Dai, Enyu, Zhou, Shunheng, Liu, Dianming, Liu, Haizhou, Meng, Qianqian, Jiang, Bin, and Jiang, Wei

Subjects
TOPOLOGY, ANNOTATIONS, CANCER risk factors, NEOPLASTIC cell transformation, GENE expression
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 However, most of the existing approaches use the outdated pathway information and neglect the complex gene interactions in pathway.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/. [ABSTRACT FROM AUTHOR]

Published
2019
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