TY - JOUR
T1 - Pathway enrichment analysis with networks
AU - Liu, Lu
AU - Wei, Jinmao
AU - Ruan, Jianhua
N1 - Funding Information:
Acknowledgments: This research was supported by funds from Marshall University to L.L., NSF (IIS-1218201, ABI-1565076) and NIH (SC3GM086305, U54CA217297, G12MD007591) to J.R., and National Natural Science Foundation of China (Grant No. 61772288) to J.W.
PY - 2017/10
Y1 - 2017/10
N2 - 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.
AB - 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.
KW - Enrichment analysis
KW - Gene sets
KW - Pathway
KW - Protein–protein interaction network
KW - Random walk with restart
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U2 - 10.3390/genes8100246
DO - 10.3390/genes8100246
M3 - Article
AN - SCOPUS:85030570825
VL - 8
JO - Genes
JF - Genes
SN - 2073-4425
IS - 10
M1 - 246
ER -