PATHiWAYS is a microarray data analysis web tool for the interpretation of changes in expression levels in a pathway-based context. Specifically, this tool allows the user to identify the stimulus-response subpathways that are significantly more activated in a control/case experiment. PATHiWAYS identifies all the stimulus-response subpathways of KEGG signaling pathways, calculates the probability of activation of each one of them in a case/control dataset and performs a multilevel analysis for the identification of significantly differencially activated between two conditions.

Here you can find detailed information describing the implemented methods, a tutorial, worked examples and news related with PATHiWAYS.

How to cite PATHiWAYS

Sebastian-Leon, Patricia, et al. “Understanding disease mechanisms with models of signaling pathway activities.” BMC systems biology 8.1 (2014): 121. link to the article

Sebastián-León P, Carbonell J, Salavert F, Sanchez R, Medina I, Dopazo J. Inferring the functional effect of gene expression changes in signaling pathways. Nucleic Acids Res. 2013;41:W213-W217 link to the article

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But… why PATHiWAYS?

Nowadays, the most of the tools available provide a measure of the impact of a determined condition in the whole pathway. In the last year some R tools appear performing this kind of analysis, but taking into account determined substructures into the pathway.

PATHiWAYS web-tool not only consider substructures into the pathway but identify stimulus-response subpathways with a determined functional consequence.

Also, PATHiWAYS performs the impact analysis taking into account the internal structure of the pathway, that is, it takes into account the internal relationships between genes in the pathway (activation or repression relationships).

Finally, PATHiWAYS provide an comprehensible graphical representation in the pathway map of the stimulus-response subpathways significantly different between two conditions.

…more PATHi

The aim of PATHiPRED web tool is to provide a classifier that takes advantage of the activity values of signal transduction along the elementary components of signaling pathways. PATHiPRED differentiates between two classes or a continuous variable using activation probabilities of stimulus-response sub-pathways calculated by PATHiWAYS methodology. PATHiPRED performs a SVM modelling with cross-validation and the results include the prediction model, the statistical parameters that assess the goodness of the model, the confusion matrix of the prediction, the activation probabilities matrix, the mechanism-based biomarkers and pathways' graphs with the selected sub-pathways highlighted. Moreover, the prediction model obtained can be applied to a new dataset within PATHiPRED web tool.