## Pathways / Gene set test

### Description

Tests the statistical significance of a set of genes simultaneously, not gene by gene as is the usual method of analysis.

### Parameters

- Gene list to test (KEGG, GO, current) [current]
- Use Benjamini and Hochberg's FDR (yes, no) [yes]
- Number of the most significant groups to visualize (4, 9, 16) [4]
- Width of the image (200...3200) [600]
- Height of the image (200...3200) [600]

### Details

This tool should be run on the normalized, unfiltered data set. Otherwise, the analysis will end with an error.

The test is run by using the current list of genes, so that their significance can be assessed or all KEGG pathways or GO groups can be tested for significance. If KEGG or GO is selected only the specified number of the most significant findings are visualized, while the result table will include the complete results. The user can select whether or not to apply multiple testing correction, using the method of Benjamini and Hochberg, which transforms the p-values into false discovery rates.

Note that gene sets consisting of only one gene are automatically removed from the analysis results.

### Output

A table summarizing the analysis results per gene set.

The table includes information about the size of the gene set, how many individual probes for that gene set were tested, the gene set representative Q statistic, the expected Q value based on an asymptotic distribution, the variance in the expected Q statistic, the unadjusted p-values and the ones adjusted for multiple testing. If you would like to know what are the genes listed as "tested", you can run the tool "Utilities/ Extract genes from GO term" on your original data set.

An image illustrating the results in a barplot.

The bars in the plot show the influence of each gene on the test statistic, which is calculated as the average of the bars. The solid reference line shows the expected influence, if the gene was not associated with the sample groups. The equidistant horizontal marks on the bars represent how far, in terms of standard deviations, from the expected influence the observed influence is. Red color bars signify a negative association to the groups (downregulation), whereas green bars correspond to a positive association (upregulation). The most significant pathway can be found in the top-left corner of the image, and the pathways are plotted according to decreasing significance to the right and then down.

For further information about the geneplot please see the manual of the GlobalTest package (page 19).

### References

This tool uses Bioconductor package globaltest. Please cite the articles:

Goeman, J. J., Oosting, J., Cleton-Jansen, A. M., Anninga, J. K., and van Houwelingen, J. C. (2005). Testing association of a pathway with survival using gene expression data. Bioinformatics, 21(9):1950–1957.

Goeman, J. J., van de Geer, S. A., de Kort, F., and van Houwelingen, J. C. (2004). A global test for groups of genes: testing association with a clinical outcome. Bioinformatics, 20(1):93–99.

Goeman, J. J., van de Geer, S. A., and van Houwelingen, J. C. (2006). Testing against a high-dimensional alternative. Journal of the Royal Statistical Society Series B-Statistical Methodology, 68(3):477–493.