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enrichit: C++ Implementations of Functional Enrichment Analysis

enrichit provides C++ implementations of functional enrichment analysis methods and S4 result classes used by the clusterProfiler family. It supports ORA, GSEA, weighted enrichment, network-based enrichment, multilayer network workflows, and multi-omics aggregation and contribution analysis.

Installation

You can install the development version of enrichit from GitHub using devtools:

# install.packages("devtools")
devtools::install_github("YuLab-SMU/enrichit")

Main components

enrichit organizes its functions around four components:

• Core enrichment engines: ORA, GSEA, weighted ORA/GSEA, and GSON-aware variants.
• Network-aware enrichment: single-layer nsea() and multi-layer mnsea() workflows based on Random Walk with Restart.
• Multi-omics integration: early fusion at the feature level and late fusion at the pathway level.
• Contribution and topology outputs: contribution tables and topology-aware extraction helpers for visualization in enrichplot.

Implementation and features

• Implementation: Algorithms are implemented in C++ via Rcpp, with sparse network propagation using RcppEigen.
• ORA: standard hypergeometric ORA with optional weighted ORA through Wallenius’ noncentral hypergeometric distribution.
• GSEA: multilevel, permutation, and adaptive strategies for ranked enrichment analysis.
• GSON support: native ora_gson() and gsea_gson() interfaces for structured gene set collections.
• NSEA: nsea() and nsea_gson() for network-ranked enrichment on a single graph, including mode = "signed" for bidirectional propagation.
• Multi-layer topology fusion: mnsea() and mnsea_gson() for multiplex or heterogeneous network propagation across multiple layers.
• Multi-omics early fusion: aggregate_omics(), harmonize_ids(), and select_features_for_ora() for feature-level integration before enrichment.
• Multi-omics late fusion: aggregate_enrichment() for pathway-level aggregation of multiple enrichment results.
• Contribution tracing: get_omics_contribution(), classify_omics_pattern(), and get_mnsea_contribution() for contribution summaries.
• Topology-aware extraction: extract_mnsea_subnetwork() for pathway-specific node/edge tables that can be passed to downstream visualization packages.
• Bayesian term selection: bayes_enrich() and bayes_summary() for posterior-based term prioritization.

Main APIs

Classical enrichment

• ora(), ora_gson()
• gsea(), gsea_gson()
• gseaScores()

Weighted enrichment

• ora(..., weight = )
• ora_gson(..., weight = )
• gsea(..., weight = )
• gsea_gson(..., weight = )

Network-aware enrichment

• prepare_network()
• nsea(), nsea_gson()
• prepare_multilayer_network()
• propagate_multilayer()
• collapse_multilayer_scores()
• mnsea(), mnsea_gson()

Multi-omics integration

• aggregate_omics()
• harmonize_ids()
• select_features_for_ora()
• aggregate_enrichment()

Explanation helpers

• get_omics_contribution()
• classify_omics_pattern()
• get_mnsea_contribution()
• extract_mnsea_subnetwork()

Result Objects

The package returns the following S4 result classes:

• enrichResult for ORA-like workflows
• gseaResult for ranked enrichment workflows
• nseaResult for single-network propagation plus enrichment
• mnseaResult for multi-layer propagation, collapsed scores, and cached explanation tables

These classes are used across the clusterProfiler family:

• enrichit handles core computation, algorithm implementation, and contribution data preparation
• clusterProfiler provides high-level biological interpretation workflows and general enrichment analysis interfaces
• enrichplot handles visualization
• gson provides a structured gene set resource layer for managing and exchanging gene set collections across the family
• knowledge-base-oriented downstream packages such as DOSE, ReactomePA, meshes, and MicrobiomeProfiler provide domain-specific annotation and interpretation layers

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.