gwSPIA: Improved Signaling Pathway Impact Analysis With Gene Weights

Gene set analysis using signaling pathway has become a popular downstream analysis following differential expression analysis. From a biological point of view, only some portions of a pathway are expected to be altered; however, a few approaches using the different importance of genes in signaling pathways, which encompass the constitutive functional nonequivalent roles of genes in real pathways, have been proposed and none of them tries to associate the importance of genes with the related disease. In this paper, we developed an extended method of signaling pathway impact analysis (SPIA), called gwSPIA, by incorporating three signaling pathway-based gene weight merits that reflect the importance of genes from different aspects and attempt to associate the importance of genes with the related diseases. By applying the gwSPIA to the gene expression data sets in comparison with other seven methods in three measures, sensitivity, prioritization, and specificity, we show that the gwSPIA ranks in the second place in both sensitivity and prioritization. Furthermore, the specificity of the gwSPIA is better than SPIA, which is lower than 25%. The results also suggest that the gene weight used in the gwSPIA can reflect the association between the genes and the related diseases. The R package of the gwSPIA can be accessed from https://github.com/sterding/gwSPIA.