PathAct: a novel method for pathway analysis using gene expression profiles

We developed PathAct, a novel method for pathway analysis to investigate the biological and clinical implications of the geneexpression profiles. The advantage of PathAct in comparison with the conventional pathway analysis methods is that it canestimate pathway activity levels for individual patient quantitatively in the form of a pathway-by-sample matrix. This matrix canbe used for further analysis such as hierarchical clustering and other analysis methods. To evaluate the feasibility of PathAct,comparison with frequently used gene-enrichment analysis methods was conducted using two public microarray datasets. Thedataset #1 was that of breast cancer patients, and we investigated pathways associated with triple-negative breast cancer byPathAct, compared with those obtained by gene set enrichment analysis (GSEA). The dataset #2 was another breast cancer datasetwith disease-free survival (DFS) of each patient. Contribution by each pathway to prognosis was investigated by our method aswell as the Database for Annotation, Visualization and Integrated Discovery (DAVID) analysis. In the dataset #1, four out of the sixpathways that satisfied p < 0.05 and FDR < 0.30 by GSEA were also included in those obtained by the PathAct method. For thedataset #2, two pathways (“Cell Cycle” and “DNA replication”) out of four pathways by PathAct were commonly identified byDAVID analysis. Thus, we confirmed a good degree of agreement among PathAct and conventional methods. Moreover, severalapplications of further statistical analyses such as hierarchical cluster analysis by pathway activity, correlation analysis andsurvival analysis between pathways were conducted.