localEnrichment

Local Enrichment Analysis Tools for Omics Data

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📦 Overview

localEnrichment provides flexible tools for performing Over-Representation Analysis (ORA) and Quantitative Enrichment Analysis (QEA) on omics data, using local, user-defined mappings (e.g. pathways, chemical classes, ontologies).

Unlike other enrichment packages, it is designed to work without relying on online databases, allowing reproducible, standalone analyses for metabolomics, transcriptomics, or proteomics data.

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🧭 Installation

You can install the development version directly from GitHub:

# install.packages("devtools")
devtools::install_github("aspresearch/localEnrichment")

Quickstart

library(localEnrichment)

# Example metadata and sets
meta <- list(
  mapping_name = "chemical_classes",
  feature_id_type = "HMDB_ID",
  set_source = "MEGA",
  version = "v2025.1",
  description = "Chemical classes mapping"
)

sets <- data.frame(
  set_id = c("CL001", "CL002", "CL003"),
  set_name = c("Amino Acids", "Biogenic Amines", "Lipids"),
  feature_ids = c(
    "HMDB0000161;HMDB0000191;HMDB0000243;HMDB0000284",
    "HMDB0001432;HMDB0001448;HMDB0001820",
    "HMDB0000221;HMDB0000562;HMDB0000625;HMDB0000933"
  )
)

Eset <- EnrichmentSet(sets, meta)

# Perform Over-Representation Analysis (ORA)
selected <- c("HMDB0000161", "HMDB0000284", "HMDB0001820")
res <- ora_test(Eset, selected)
plot_enrichment(res, type = "bar")

Main features

EnrichmentSet() — class to handle enrichment mappings and metadata

ora_test() — over-representation analysis using Fisher’s test

qea_test() — quantitative enrichment analysis

plot_enrichment() — bar or dot plots for enriched sets

plot_qea_profile() — enrichment profiles (ES curves) for QEA results

filter_sets_by_features() — reduce large mappings to relevant sets

prepare_features_list() — filter and extract feature lists for enrichment

Authors

Alex Sanchez-Pla (asanchez@ub.edu)

License

MIT + file LICENSE