Further investigation is needed to reveal the mechanism and epidemiologic conclusions

very significant. The average betweenness of the pathways identified by sub-SPIA and Pathiways is also the largest. Both the average degree and betweenness actually reflect the hub characteristic of the identified pathways; the two observations just mentioned demonstrate that the pathways identified by sub-SPIA and Pathiways Digitoxin biological activity pubmed ID:http://www.ncbi.nlm.nih.gov/pubmed/19667973 generally play important roles in the entire system. The average clustering coefficient generally reflects the degree of the closeness of the identified pathways. For both datasets, the average clustering coefficient of pathways identified by sub-SPIA is about 0.38, which is the largest obtained of the five methods. NA means there was no significant subpathways were found. doi:10.1371/journal.pone.0132813.t003 relative sparseness of the entire pathway network, this indicates that the pathways identified by sub-SPIA are more closely related than those identified by other methods. In other words, they interact closely with each other to fulfill various biological functions. Discussion To PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/19665744 identify significant pathways, we combine the subpathway analysis and the SPIA in a method we call sub-SPIA. Compared with the original SPIA, sub-SPIA dramatically improves the resolution for identifying significant pathways because subpathway analysis focuses on a local region in a pathway. From Tables 1 and 2, we see that the p-value of pathways identified by sub-SPIA is much smaller than for SPIA. Furthermore, the flexibility of the minimal-spanning tree makes it possible to capture various subpathways with complicated topologies. These two factors make sub-SPIA more sensitive than SPIA, allowing sub-SPIA to identify more potential pathways associated with specific cancers or diseases. Note that sub-SPIA misses a f