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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, design-aware GSEA on RNA-seq counts, 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 five components:

• Core enrichment engines: ORA, GSEA, weighted ORA/GSEA, and GSON-aware variants.
• Design-aware and score-level GSEA: dsea() for enrichment on a count matrix plus a design (rank by the requested contrast, then GSEA), and dsea_scores()/ssgsea_scores() for per-sample gene set scores.
• 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.
• Design-aware GSEA: dsea() takes a count matrix or a DESeqDataSet plus a design from model.matrix() (rownames must match the sample names), variance-stabilises the counts (vst or rlog), ranks genes by the requested contrast, and runs the GSEA engine already in the package. It returns a plain data.frame – GSEA’s table plus padj and contrast – so it can be filtered and plotted like any other GSEA result. dsea_scores() and ssgsea_scores() are the other half: a gene set x sample score matrix for analyses that need samples rather than one contrast.
• DESeq2 adapters: as_ranked() and as_genes() turn DESeqResults and fitted DESeqDataSet objects into a ranking or a gene vector, so a dsea() analysis can start from an existing DESeq2 result; extra arguments are passed on to DESeq2::results(), so the contrast uses its c("factor", "numerator", "denominator") form. DESeq2 is suggested, not imported.
• 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 = )

Design-aware and score-level GSEA

• dsea()
• dsea_scores(), ssgsea_scores()
• as_ranked(), as_genes()

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

dsea() is the exception on the other side: it returns a plain data.frame rather than a gseaResult. dsea_scores() and ssgsea_scores() return a DseaScores object, an S3 list holding the gene set x sample score matrix ($scores) and the metadata it was built with ($meta).

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.