Subpathway Analysis Based On Minimum Spanning Tree

Pathway analysis is a common approach to gain insight from biological experiments. Signaling-pathway impact analysis(SPIA) is one such method and combines both the classical enrichment analysis and the actual perturbation on a given pathway. Because this method focuses on a single pathway, its resolution generally is not very high because the differentially expressed genes may be enriched on a local region of the pathway. In the present work, to identify cancer-related pathways, we incorporated a recent subpathway analysis method into the SPIA method to form the “sub-SPIA method.” The original subpathway analysis uses the k-clique structure to define a subpathway. However, it is not sufficiently flexible to capture subpathways with complex structure and usually results in many overlapping subpathways. We therefore propose using the minimal spanning tree structure to find a subpathway. Upon applying the sub-SPIA method to colorectal cancer and lung cancer datasets, our results show that it can identify more significant pathways associated with each specific cancer than can SPIA. Based on the entire pathway network in the Kyoto Encyclopedia of Genes and Genomes, we find that the pathways identified by sub-SPIA are more highly connected than those identified by other methods.