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CHAPTER 4. THE FUNCTIONAL CLASSIFICATION
4.7. GENE SET ENRICHMENT ANALYSIS
group” and leave in the results ”only overrepresented groups”. If checked, overrepresented groups are
searched using the FA algorithm from Section 4.6 preliminary to GSEA. To obtain statistically significant
results, the ”P-value threshold” should be set up close to zero. In the case of multiple groups testing
(the most frequent case), you should use the ”Control False Discovery Rate” option, so the ”P-value
threshold” will control the rate of false positives in the results.
Figure 4.21 GSEA user interface
4.7.2
GSEA example
Here we analyze a sample data set using an ”Expression” column. We choose ”SwissProt keywords” as
a functional category. We set ”Weight genes power” to 1 and left other parameters as they appear by
default.
The enrichment result is pre-filtered by p-value, and you can clear the filter to see all lines. The figure
below shows the enrichment output and detailed view for the ”Transit peptide” keyword.
This result shows that genes associated with the ”Transit peptide” function have low expression
values in our experiment.
4.7.3
Enrichment Analysis theoretical basis
The Gene Set Enrichment Analysis (GSEA) algorithm is designed to detect the enrichment of input gene
set with genes from certain molecular classification groups (or groups given by user). It is assumed
that the input set contains genes differently expressed between two conditions (phenotypes, cell strains,
before/after treatment etc). The input set D is a list of N genes with a value connected to each gene that
reflects the difference in expression - it can be fold change, correlation with phenotype or other. Let’s
name these values ”expression difference”. The GSEA algorithm obtains a list of genes L by ranging
the set D according to the expression difference (descending sort). L is a set of N genes {g i} ordered
by values {r i}. For each group S of the given category, the algorithm accounts for whether hit genes
of group S tend to be located at the top of the sorted list L, at the bottom or distributed randomly in L.
While working the GSEA computes the Enrichment Score ES(S) that ranges in [-1,1] and p-value that
ranges in [0,1]. ES is the maximum deviation from zero of the so called running sum Équation 4.7.1,
[26].
As we can see, ES depends on the parameter p that ranges in [0,1]. If p=0 the ES resembles a
Kolmogorov-Smirnov statistics [27]. If p>0 the algorithm takes into account the value of expression
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