2.8.2 Gene set enrichment analysis
Gene set enrichment analysis (GSEA) was run in clusterProfiler (v3.18.1)
(Yu et al., 2012) for each tissue separately to determine if sets
of genes from the same gene ontology (GO) term/functional category
showed significant, concordant differences between current-day and
elevated CO2 conditions. Unweighted GSEA was run using
the DESeq2 log2 fold-change values of all genes and the annotated GO
terms as the ‘gene sets’. A minimum and maximum gene set size of 15 and
500, respectively, was used. GSEA determines if genes from the same
functional category are significantly more likely to occur at the top or
bottom of the log2 fold-change list and therefore whether these
functional categories are up- or down-regulated at elevated
CO2, respectively. P-values were adjusted for multiple
comparisons using the Benjamini-Hochberg method and a significance
threshold of padj < 0.05 was used. The GSEA results were
imported into Cytoscape (v3.8.2) (Shannon et al., 2003) where
EnrichmentMap (v3.3.1) (Merico et al., 2010) was used to create a
network to visualise the functional enrichment results. All significant
functional categories were included in the network as a circular node.
Functional categories with > 0.25 similarity were linked by
edges. Similar functional categories were manually grouped into clusters
and labelled.