Over representation analysis can be performed on a list of pathways to determine pathways contain the most changed expression, taking into consideration the number of genes/proteins on the pathway that were measured in the experiment and the number of those that are differentially expressed. For the statistical analysis a criterion to determine which genes/proteins are of interest can be provided by the user, which can for example be based on a minimum p value and if desired a minimum change between experimental groups by calculating the Z score and sorted accordingly. The function calculatePathwayStatistics, takes the address of the folder containing the pathways to be analysed and a criteria (for eg. P Value < 0.05) on the basis of which the Z score should be calculated.

Z score is calculated by dividing the number of genes/proteins/metabolites matching the above mentioned criteria by the total number of genes/proteins/metabolites present in the dataset and also found in the pathways.
In the working directory you get a folder named contents and a hyperlinked html page index. The html page is divided into 3 sections: The top left panel contains information regarding the number of gene/proteins found in the pathways, gene/protein meeting the criterion set by the user, the criteria for calculating Z Score is also displayed. The address of the dataset, pathway directory and the gene database used for mapping is shown next. After that there is a table containing the clickable list of pathways analysed, the next column gives the number of genes/proteins present in the statistical analysis table that were also found on that particular pathway, the column next to that gives the number of genes within that total number that meet the criteria for Z Score Calculation and the last column gives the resulting Z Score for that pathway.

R code:

xml.rpc(server, "PathVisio.calculatePathwayStatistics","/home/anwesha/pathways", "/home/anwesha/Results/data.txt.pgex", "/home/anwesha/PathVisio-Data/gene-databases", "[pvalue]"<"0.05", "/home/anwesha/Results")

Python code:

server.PathVisio.calculatePathwayStatistics("PathVisio.calculatePathwayStatistics","/home/anwesha/pathways", "/home/anwesha/Results/data.txt.pgex", "/home/anwesha/PathVisio-Data/gene-databases", "[pvalue]"<"0.05", "/home/anwesha/Results")