Most existing pathway analysis methods focus on either the number of differentially expressed genes observed in a given pathway (enrichment analysis methods), or on the correlation between the pathway genes and the class of the samples (functional class scoring methods). Both approaches treat pathways as simple sets of genes, disregarding the complex gene interactions that these pathways are built to describe.

More recently, biological annotations have started to include descriptions of gene interactions in the form of gene signaling networks, such as KEGG (Ogata et al., 1999), BioCarta ( and Reactome (Joshi-Tope et al., 2005). This richer type of annotations have opened the possibility of an automatic analysis aimed to identify the gene signaling networks that are relevant in a given condition, and perhaps even the specific signals or signal perturbations involved. This approach is not well suited for a systems biology approach that aims to account for system-level dependencies and interactions, as well as identify perturbations and modifications at the pathway or organism level (Stelling, 2004).

Advaita’s products are based on Impact Analysis method that leverages the information about type, function, position and interaction between genes in a given pathway.  Impact Analysis combines the evidence obtained from the classical enrichment analysis with a novel type of evidence, which measures the actual perturbation on a given pathway under a given condition.  We illustrate the capabilities of the novel method on four real datasets.  The results obtained on these data show that Impact Analysis has better specificity and more sensitivity than several widely used pathway analysis methods.