Gene Set Enrichment Analysis GSEA is a bioinformatics tool that determines whether a set of genes (e.g. a gene ontology (GO) group or a pathway) shows statistically significant, concordant differences between two experimental groups (1,2). Briefly, the goal of GSEA is to determine whether the genes belonging to a gene set are randomly distributed throughout the ranked (by expression) list of all the genes that should be taken into consideration (e.g. gene model), or are primarily found at the top or at the bottom of the list.

Prerequisites

To run GSEA, your project has to contain at least one categorical factor with exactly two levels (e.g. Treated and Control). If you are running GSEA on RNA-seq data, note that some common normalisation transformations, such as fragments/reads per kilobase of transcript per million mapped reads (FPKM/RPKM) or transcripts per million (TPM) are not considered suitable for GSEA (for more information, please see GSEA documentation). Instead, you should use an approach such as DESeq2 normalisation, trimmed means of M (TMM), or geometric mean.

Running GSEA

To launch GSEA, select the data node with normalised data and then go to Biological interpretation > GSEA (Figure 1).

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Use the first dialog (Figure 2) to specify the gene sets. You can run GSEA on pathways (currently based on Kyoto Encyclopedia of Genes and Genomse Genomes (KEGG) pathways) or on other gene set databases. When using the KEGG option, the KEGG database (i.e. the species) is automatically set, based on the upstream nodes. The Gene set size options option allows you to restrict your analysis on gene sets of certain size (i.e. number of genes).

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If you select Gene set database, two additional options will appear. Genome build will be detected automatically, based on the upstream nodes. The gene sets that are available for that build are listed in the drop down list (Figure 3). Custom databases will be labeled by their name as specified in the Library file management, while GO database will be labeled by the release date (as seen in Figure 3).

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Once your choices are made, push Next to proceed.

In the second part of the set up (Figure 4) pick the experimental factor for GSEA (three are available in this example: Condition, Stim, Numeric). The dialog will list only the factors with two categories; if your project contains additional factors, which have a single category or more than two categories, a warning message will be displayed at the top. 

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If the warning message is displayed, click on the details link to see which factors are unavailable and why (learn more about unavailable factors (an example is shown in Figure 5).

Select the experimental factor that you want to run GSEA on and push Next.

The third dialog is Define comparisons (Figure 6). The box on the left side displays the categories of the selected factor (shown as Factor). Use the arrow buttons (>) to move one of the factors to the Denominator box (that factor should be interpreted as the reference category) and the other factor to the Numerator box. Confirm your selection by pushing the Add comparison button and the comparison will be added to the Comparisons table (Figure 6). 

Low value filter is turned on by default and will remove all the genes with the lowest average coverage of 1.0 or below; if a filter feature task was performed before this task, the default low-value filter is set to None (for details please see the GSA chapter) .

Push Finish to launch GSEA with the default settings.

Alternatively, click on the Configure icon to access the advanced options (Figure 7). Number of data permutations (needed to calculate the normalised enrichment scores) can be controlled using the Permutations option. Low value filter is turned on by default and will remove all the genes with the lowest average coverage of 1.0 or below (for details please see the GSA chapter) Permutation is to randomly permute the group assignment across a given gene. For each permutation, a random order is computed, that order is used to compute the score for each gene. Finally, if you start your project by importing a count matrix (i.e. as opposed to generating the count matrix using Partek Flow), you need to specify whether the expression values were log transformed before the import (use the Data has been log transformed with base drop down).

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GSEA Results

When the task completes, double click on the GSEA task node (Figure 8) to view the report.

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The report consists of two parts: the GSEA result table on the right and the filter panel on the left (Figure 9).

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The comparison (i.e. Denominator vs. Numerator) is given at the top of the GSEA table. To download the table to your local computer as a text file, use the Download link in the bottom right. Each row of the table corresponds to one gene set and the gene sets are ranked by the P-value, ascending (lowest values at the top). The carrets icon () in the column headers are used for sorting. The columns of the table are as follows.

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Click on the View enrichment report icon () to open a new Data viewer session with the per-gene set report. The current gene set is in the title, at the top of the canvas (Enrichment profile). To quickly switch to another gene set, use the Configuration Axis > Content > Data card in the menu (on the left)drop-down list. The individual plots are as follows (Figure 11; from top to bottom).

• Enrichment score. The algorithm walks down the ranked list of all the genes in the model, increasing the running sum (y axis) each time when a gene in the current gene set is encountered. Conversely, the running-sum is decreased each time a gene not in the current gene set is encountered. The magnitude of the increment depends on the correlation of the gene with the experimental factor. The enrichment score is then the maximum deviation from zero encountered in the random walk (the summit of the curve).
• Gene set hits. Each column shows the location of a gene from the current gene set, within the ranked list of all the genes in the model.
• Rank metric. The plot shows the value of the ranking metric (y axis) as you move down the ranked list of all the genes in the model (x axis). The ranking metric measures a gene’s correlation with a phenotype. A positive value of the metric indicates correlation with the first category (Numerator) and a negative value indicates correlation with the second category (Denominator). 

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Click on the View extra details plot () to open a gene set-specific report page (Figure 12).

Leading edge genes: it is a subset of genes that contribute most to the ES. For a positive ES, the leading edge subset is the set of members that appear in the ranked list prior to the peak score. For a negative ES, it is the set of genes that appear subsequent to the peak score.

References

1. Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545-15550. doi:10.1073/pnas.0506580102
2. Mootha VK, Lindgren CM, Eriksson KF, et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003;34(3):267-273. doi:10.1038/ng1180