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Competitive Landscape

The goal is not to be the broadest enrichment product. It is to be a very fast, reproducible core engine with an eval framework.

Existing Tools And What They Optimize

GOATOOLS is a Python library and CLI for GO analysis. It parses OBO, GAF, GPAD, NCBI gene2go, and related formats; it propagates counts to parent terms by default; and it uses Fisher exact tests with many correction options.

g:Profiler and gprofiler2 provide a maintained web/API/R workflow across GO, pathways, disease annotations, and identifier namespaces. They use hypergeometric enrichment and offer g:SCS, Bonferroni, and FDR correction choices.

clusterProfiler is the dominant R/Bioconductor ecosystem tool for ORA and ranked-list workflows. It has GO-specific helpers and a universal enricher interface using user-provided TERM2GENE mappings.

topGO is important because it exposes topology-aware GO methods such as classic, elim, weight, weight01, lea, and parentchild with multiple statistics.

Ontologizer is important for parent-child and model-set ideas. Its papers frame the issue that GO terms are dependent through inheritance, so standard term-for-term Fisher tests can be biologically redundant.

Enrichr is a popular web/API ecosystem with many gene set libraries, fast interactive workflows, and p-value, z-score, combined-score, and adjusted p-value outputs.

PANTHER powers the GO Consortium enrichment service and is a strong reference point for user expectations around GO term enrichment and background selection.

MyGeneset.info is not a direct competitor. It is a useful public gene-set source and API for eval inputs because it exposes GO, MSigDB, Disease Ontology/OMIM, WikiPathways, Reactome, KEGG, and species filters.

Gene-Set-Informed Factor Models

A separate paradigm consumes gene sets without doing over-representation analysis at all. Instead of asking “is this set enriched in my ranked or thresholded gene list?”, these methods treat curated sets as priors on the latent structure of an expression matrix and let the model decide, per sample or per cell, which annotated programs are active.

f-scLVM (Buettner et al., 2017), implemented in the slalom package for R and Python, is the reference example. It is a sparse Bayesian factor analysis model for single-cell RNA-seq in which each annotated factor is initialized from a gene set (typically MSigDB or Reactome pathways): the set membership seeds the factor’s loading mask. The model then, jointly across all factors:

  • estimates a per-factor relevance so it can switch annotated factors on or off rather than forcing every pathway to explain variance;
  • refines the gene-set annotation itself, adding genes that load on a factor but were not in the input set and down-weighting members that do not;
  • infers additional unannotated sparse and dense factors to absorb residual structure (technical and biological) that no input set explains.

The output is interpretable per-cell factor activations attached to named pathways, which is why it is used to identify and annotate cell subpopulations rather than to rank pathways for a single contrast.

expiMap (Lotfollahi et al., 2023), implemented in the scArches ecosystem, is the deep-learning successor to the same idea. It is a variational autoencoder whose latent nodes are gene programs: a binary mask ties the decoder so that each latent dimension reconstructs only the genes of its annotated set, making the otherwise opaque latent space directly interpretable. It carries over every move f-scLVM makes — it learns a per-cell activity for each program, soft-prunes uninformative programs (group-lasso regularization on the decoder, the relevance analog), and refines membership by allowing the mask to admit a few extra genes per program. Its headline addition is a set of add-on de novo learnable nodes: unconstrained latent dimensions that capture biology the annotations miss, the neural-network counterpart of f-scLVM’s unannotated factors. Because it is built on scArches, its distinctive use case is reference mapping — projecting a new query dataset onto an existing atlas’s gene-program latent space and reading off which programs differ, including in perturbation and disease-response settings.

The trade-off between the two mirrors linear-vs-nonlinear generally: f-scLVM is a linear factor model that is fast, fully Bayesian, and easy to reason about; expiMap is a nonlinear VAE that scales to atlas-sized data and supports transfer learning, at the cost of interpretability being mediated by the architecture rather than read directly off loadings.

This matters to a gene-set engine for two reasons. First, it is a downstream consumer of exactly the libraries this project scores and curates, so set quality propagates into factor interpretability. Second, the “refine the annotation” step is the same concern the curated corpus encodes by hand: the nominal membership of a named set and the genes that actually behave like the set are not identical. Both f-scLVM and expiMap learn that gap from data (membership refinement); the curation gold standard records it from expert judgment (recovery_status / membership_gap). The two are complementary views of annotation noise, not competitors.

GSEA and ssGSEA (single-sample GSEA) sit between ORA and factor models: they are rank-based set-scoring methods that, in the ssGSEA case, also produce a per-sample per-set activity score. They share the “score named programs per sample” goal with f-scLVM but do not jointly model factors, switch sets off, or refine membership.

Connection To The Curated Corpus

The curated gold standard in curation/ is, structurally, a hand-built version of what these models estimate. A single non-GO gene set is not curated to one label; it is decomposed into multiple GO programs, each with a curator judgment. HALLMARK_INTERFERON_GAMMA_RESPONSE, for example, resolves into a response-to- type-II-interferon program, an antigen-processing/MHC-class-I program, an antiviral-defense program, and a nonspecific translation residual. That is a factor decomposition of the set, recorded with evidence rather than fit from an expression matrix, and the schema axes line up with the model concepts:

  • multiple associations per set ≈ the several latent program nodes one set seeds;
  • category (core_process … nonspecific / false_association) ≈ per-factor relevance / group-lasso pruning — the nonspecific translation term is the factor f-scLVM would drive toward zero relevance;
  • recovery_status: membership_gap ≈ data-driven membership refinement: expiMap’s reported addition of nine B-cell markers to its predefined BCR program is exactly a membership_gap correction, learned from data instead of curated;
  • series / contrasting poles ≈ a program’s activation contrasting across cell states.

Two consequences. First, the corpus is natural supervision and a benchmark for the membership-refinement step specifically: the models propose “gene X belongs in program P”; the gold standard holds independent, cited judgments of that same claim. Second, the alignment is only with membership_gap — the models refine set membership but never touch the ontology, so annotation_gap (GO too shallow) stays orthogonal, just as it does for the enrichment tools above.

The overlap is concrete, not aspirational: f-scLVM seeds factors from MSigDB Hallmark and Reactome and headlines a G2/M-checkpoint cell-cycle factor; expiMap trains on Reactome plus PanglaoDB marker sets and headlines interferon programs. The corpus already curates that family — HALLMARK_G2M_CHECKPOINT, HALLMARK_INTERFERON_GAMMA_RESPONSE, and the HAY_BONE_MARROW_* / DESCARTES_* cell-type marker sets — so the priors these models consume are the same objects this project scores. Note the scope line, though: this repo curates gene sets and their GO interpretations, not expression matrices. The in-scope way to bring a signature from one of these papers into the corpus is the existing LIT:DISEASE_ACTIVITY pattern (a derived gene list with the paper as identity, membership, and evidence), not hosting the underlying single-cell data.

Our Wedge

The core differentiators should be:

  • ontology-neutral input tables rather than GO-specific parsing in the hot path;
  • fast dense bitset scoring for repeated jobs;
  • first-class N x N term/set matrix mode;
  • explicit background semantics;
  • reproducible prep metadata;
  • benchmark and correctness evals as part of the repo.

What Not To Compete On Yet

We should not try to match all visualization, identifier conversion, web UI, or database coverage of mature ecosystems in the core crate. Those are wrapper-layer concerns.

The MVP should instead make it easy to prove:

  • this input produced this table;
  • this table used this closure policy;
  • these p-values match the reference implementation;
  • this workload scored faster than comparable tools.

Research Sources