Tabular result
After running GSEA or GSEA Preranked, this page will be opened.
If you land in a window containing a table as this one below, it means that your analysis is complete and you're now seeing the result as a table.
Analysis result
The actual analysis (GSEA or GSEA Preranked) result is visible at the center as a table of textual and numerical content.
Hereafter there's a a description of each column (field) of the table.
Term
It's the name of the gene set described in the current row. The three dots means that it's truncated for visibility reasons, stay still on it with your pointer to see the full term.
ES
It's the Enrichment Score calculated for that specific gene set. It represents, to put it simply, how much the genes of this gene set are present in the top (positive enrichment score) or bottom (negative enrichment score) of the ranking list - whether this list was give (GSEA Preranked) or computed (GSEA).
The official description of it is this one.
[...] the enrichment score (ES), which reflects the degree to which a gene set is overrepresented at the top or bottom of a ranked list of genes. GSEA calculates the ES by walking down the ranked list of genes, increasing a running-sum statistic when a gene is in the gene set and decreasing it when it is not. The magnitude of the increment depends on the correlation of the gene with the phenotype. The ES is the maximum deviation from zero encountered in walking the list. A positive ES indicates gene set enrichment at the top of the ranked list; a negative ES indicates gene set enrichment at the bottom of the ranked list.
NES
It's t the Normalized Enrichment score for that specific gene set. It's a statistic of the ES and retains the meaning of the enrichment score, but it gets adjusted so that it can be used to compare different gene sets.
The GSEA website describe it as here reported.
The normalized enrichment score (NES) is the primary statistic for examining gene set enrichment results. By normalizing the enrichment score, GSEA accounts for differences in gene set size and in correlations between gene sets and the expression dataset; therefore, the normalized enrichment scores (NES) can be used to compare analysis results across gene sets.
NOM p-val
It's the NOMinal p-value regarding that specific gene set. It measures the staticial significance of the ES, however it's not adjusted to any specificity of the gene set.
The GSEA website gives this definition.
The nominal p value estimates the statistical significance of the enrichment score for a single gene set. However, when you are evaluating multiple gene sets, you must correct for gene set size and multiple hypothesis testing. Because the p value is not adjusted for either, it is of limited value when comparing gene sets.
FDR q-val
It's the False Discovery Rate q-value regarding that specific gene set. It gives a measure of how likely the normalized enrichment score is of being a false positive.
Here reported is the in-depth description of it from the official GSEA website.
The false discovery rate (FDR) is the estimated probability that a gene set with a given NES represents a false positive finding. For example, an FDR of 25% indicates that the result is likely to be valid 3 out of 4 times. The [official software, Ed.] GSEA analysis report highlights enrichment gene sets with an FDR of less than 25% as those most likely to generate interesting hypotheses and drive further research, but provides analysis results for all analyzed gene sets. In general, given the lack of coherence in most expression datasets and the relatively small number of gene sets being analyzed, an FDR cutoff of 25% is appropriate.
Mind that GSEACompass doesn't have a pre-defined cut-off for the FDR.
FWER p-val
It's the FamilyWise-Error Rate of the specific gene set. It has the same meaning of the FDR q-val, the estimated probability that the corresponding NES is a false-positive, being nevertheless more conservative.
The official GSEA website gives this brief description of it.
A more conservatively estimated probability that the normalized enrichment score represents a false positive finding. Because the goal of GSEA is to generate hypotheses, the GSEA team recommends focusing on the FDR statistic.
Gene %
As shown in the gseapy official documentation, it's computed as the position in the ranking list of the gene corresponding to the ES of this gene divided by the number of genes in the ranking list.
Tag %
As shown in the gseapy official documentation, it's computed as the number of the genes in the leading edge (before the ES if the ES is positive, after the ES if it's negative) divided by the number of genes in that specific gene set.
The official GSEA documentation gives this, slightly different, definition.
The percentage [in GSEACompass reported as a fraction, Ed.] of gene hits before (for positive ES) or after (for negative ES) the peak in the running enrichment score. This gives an indication of the percentage of genes contributing to the enrichment score.
Lead_genes
It's the collection of Leading genes of that specific gene set. It's the set containing all those genes found before the running sum peak (which by definition is the enrichment score), if the ES is positive; after the peak if the ES is negative.
The official definition is this one.
The leading-edge subset in a gene set are those genes that appear in the ranked list at or before the point at which the running sum reaches its maximum deviation from zero. The leading-edge subset can be interpreted as the core that accounts for the gene set’s enrichment signal.
The three dots means that the list is truncated for visibility reasons, stay still on this field with your pointer to see the full list.
Visualization of the table
GSEACompass has some visualization functions too.
Columns rearrangment: If you want to, you can drag-and-drop the columns as you wish, in order to rearrange them.
Column rearrangment Column hiding: You can also hide those columns not useful for your purposes, simply click on the grey drop-down menu button
Column visibilityin the bottom-left part of the window, a popup as this one will appear and you'll be able to show and hide any column.
The column hiding menu The hiding function doesn't affect the exporting function: despite being hidden, the columns will be exported (according to selection, obviously).
Navigation through the table
In order to navigate in the table, which may contain even hundreds of rows, you can use these graphical elements:
Scroll bar: It's, as usual, in the far right and can be used to go up and down the window;
Table headers: If you click on any of the table headers, it will trigger the reordering of the rows, first ascending then descending;
Page switching buttons: They're located in the bottom right of the window, use them to change the current shown page of the table;
Number of shown rows: Use these drop-down button to shown more or less rows in each page of the table, they can be found in the top-left part of the window.
Selection of rows and columns
To clarify and facilitate the process of selecting rows and columns (whom usefulness is described in the pages Plot the data and Export the data), checkboxes have been added along rows and columns so as to select and deselect them.
It's worth noting that columns and rows can be selected a the same time.
Moreover, there's a Deselect all button in the top-center part of the window, it simply deselect everything.
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