Genes | 卷:8 |
Pathway Enrichment Analysis with Networks | |
Jinmao Wei1  Lu Liu2  Jianhua Ruan3  | |
[1] College of Computer and Control Engineering, Nankai University, 94 Weijin Road, Tianjin 300071, China; | |
[2] College of Information Technology and Engineering, Marshall University, 1 John Marshall Dr, Huntington, WV 25755, USA; | |
[3] Department of Computer Science, The University of Texas at San Antonio, 1 Utsa Cir, San Antonio, TX 78249, USA; | |
关键词: pathway; protein–protein interaction network; enrichment analysis; gene sets; random walk with restart; | |
DOI : 10.3390/genes8100246 | |
来源: DOAJ |
【 摘 要 】
Detecting associations between an input gene set and annotated gene sets (e.g., pathways) is an important problem in modern molecular biology. In this paper, we propose two algorithms, termed NetPEA and NetPEA’, for conducting network-based pathway enrichment analysis. Our algorithms consider not only shared genes but also gene–gene interactions. Both algorithms utilize a protein–protein interaction network and a random walk with a restart procedure to identify hidden relationships between an input gene set and pathways, but both use different randomization strategies to evaluate statistical significance and as a result emphasize different pathway properties. Compared to an over representation-based method, our algorithms can identify more statistically significant pathways. Compared to an existing network-based algorithm, EnrichNet, our algorithms have a higher sensitivity in revealing the true causal pathways while at the same time achieving a higher specificity. A literature review of selected results indicates that some of the novel pathways reported by our algorithms are biologically relevant and important. While the evaluations are performed only with KEGG pathways, we believe the algorithms can be valuable for general functional discovery from high-throughput experiments.
【 授权许可】
Unknown