Perform Enrichment Analysis for a Single Gene Set
enrichment(
input_genes,
genes_by_term = pathfindR.data::kegg_genes,
term_descriptions = pathfindR.data::kegg_descriptions,
adj_method = "bonferroni",
enrichment_threshold = 0.05,
sig_genes_vec,
background_genes
)The set of gene symbols to be used for enrichment analysis. In the scope of this package, these are genes that were identified for an active subnetwork
List that contains genes for each gene set. Names of this list are gene set IDs (default = kegg_genes)
Vector that contains term descriptions for the gene sets. Names of this vector are gene set IDs (default = kegg_descriptions)
correction method to be used for adjusting p-values. (default = 'bonferroni')
adjusted-p value threshold used when filtering enrichment results (default = 0.05)
vector of significant gene symbols. In the scope of this package, these are the input genes that were used for active subnetwork search
vector of background genes. In the scope of this package,
the background genes are taken as all genes in the PIN
(see enrichment_analyses)
A data frame that contains enrichment results
p.adjust for adjustment of p values. See
run_pathfindR for the wrapper function of the pathfindR
workflow. hyperg_test for the details on hypergeometric
distribution-based hypothesis testing.
enrichment(
input_genes = c('PER1', 'PER2', 'CRY1', 'CREB1'),
sig_genes_vec = 'PER1',
background_genes = unlist(pathfindR.data::kegg_genes)
)
#> ID Term_Description Fold_Enrichment p_value
#> hsa04710 hsa04710 Circadian rhythm 1049.0938 6.795970e-13
#> hsa04713 hsa04713 Circadian entrainment 409.4024 5.608253e-08
#> adj_p non_Signif_Snw_Genes
#> hsa04710 2.432957e-10 PER2, CRY1, CREB1
#> hsa04713 2.007755e-05 PER2, CREB1