This paper introduces a novel signaling pathway impact analysis (SPIA) method that combines classical enrichment analysis with a new type of evidence to measure the actual perturbation of a given pathway under a specific condition. SPIA uses a bootstrap procedure to assess the significance of observed pathway perturbations, demonstrating that the evidence from perturbations is independent of pathway enrichment evidence. This allows for the calculation of a global pathway significance P-value, which combines both types of evidence. The method is evaluated using simulations and four real datasets, showing better specificity and sensitivity compared to existing pathway analysis methods. SPIA is implemented as an R package and is available online. The results highlight the importance of considering pathway topology and gene interactions in pathway analysis, providing a more comprehensive understanding of gene expression changes.This paper introduces a novel signaling pathway impact analysis (SPIA) method that combines classical enrichment analysis with a new type of evidence to measure the actual perturbation of a given pathway under a specific condition. SPIA uses a bootstrap procedure to assess the significance of observed pathway perturbations, demonstrating that the evidence from perturbations is independent of pathway enrichment evidence. This allows for the calculation of a global pathway significance P-value, which combines both types of evidence. The method is evaluated using simulations and four real datasets, showing better specificity and sensitivity compared to existing pathway analysis methods. SPIA is implemented as an R package and is available online. The results highlight the importance of considering pathway topology and gene interactions in pathway analysis, providing a more comprehensive understanding of gene expression changes.