Enrichment analysis for Canopus-predicted terms

This function performs enrichment analysis for Canopus-predicted terms on a given list of features.

Usage

CanopusLevelEnrichmentAnal(
  mmo,
  list_test,
  pthr = 0.1,
  sig = TRUE,
  term_level = "NPC_pathway",
  representation = "greater"
)

Arguments

mmo

The mmo object with sirius annotation and normalized data

list_test

A vector containing names of features to analyze

pthr

The threshold for adjusted p-value to be considered significant (default: 0.1)

sig

A logical value indicating whether to return only significant terms (default: TRUE)

term_level

The level of term to use for enrichment analysis Options are 'NPC_pathway', 'NPC_superclass', 'NPC_class', 'ClassyFire_superclass', 'ClassyFire_class', 'ClassyFire_subclass', 'ClassyFire_level5', or 'ClassyFire_most_specific' (default: 'NPC_pathway')

representation

The representation type for enrichment analysis. Options are 'greater' for overrepresentation (default: 'greater')

Value

A data frame containing the enrichment results, including term level, term name, subset count, total count, fold enrichment, p-value, and adjusted p-value (FDR)

Examples

if (FALSE) {
# Perform enrichment analysis for a list of features using NPC_pathway level
sig_terms <- CanopusLevelEnrichmentAnal(
 mmo, list_test = c("feature1", "feature2"), pthr = 0.1, 
 sig = TRUE, term_level = 'NPC_pathway', representation = 'greater'
)
# Perform enrichment analysis for a list of features using ClassyFire_class level and return all terms
all_terms <- CanopusLevelEnrichmentAnal(
 mmo, list_test = c("feature1", "feature2"), pthr = 0.1, 
 sig = FALSE, term_level = 'ClassyFire_class', representation = 'greater'
)
}