Interpreting Enrichment Results

Contents

• ORA vs GSEA: the statistics
• The background universe (ORA)
• Multiple-testing correction
• Reading GSEA output
• Reducing redundant terms
• Significance vs relevance
• Reproducibility checklist
• Publication table template
• Common misinterpretations

ORA vs GSEA: the statistics

ORA asks: among my k hits (out of a background of N genes), are more in gene set S (size K) than expected by chance? This is a hypergeometric / Fisher's exact test. It depends entirely on the threshold used to define hits and on the background N. Good when there is a clear, strong hit list.

GSEA asks: walking down the fully ranked list of all tested genes, is gene set S concentrated near the top (or bottom)? It uses a weighted Kolmogorov– Smirnov-like running sum; significance comes from permutations. No arbitrary threshold; sensitive to coordinated, modest shifts across many genes. Better when effects are broad/subtle or when a hit list would be very short or very long.

Rule of thumb: a discrete hit list → ORA; a ranked table with per-gene scores → GSEA. They answer different questions and can legitimately disagree.

The background universe (ORA)

The background (the "domain" / universe) is the set of genes that could have appeared as a hit. For RNA-seq that is the set of expressed/tested genes, not all ~20,000 protein-coding genes. Using too large a background makes ordinary housekeeping categories look significant — the most common way ORA results mislead.

• Enrichr's online API uses fixed per-library backgrounds and largely ignores a custom one. If the background matters for your claim, use g:Profiler (domain_scope='custom', background=...) or gseapy gp.enrich() with an explicit background.
• The background should use the same ID namespace as the query and the library.

Multiple-testing correction

• Benjamini–Hochberg (FDR) — default for Enrichr/gseapy (Adjusted P-value, FDR q-val). Controls expected false-discovery proportion. Use < 0.05.
• g:SCS — g:Profiler's default; accounts for the correlated structure of GO and overlapping terms; generally stricter and more appropriate than BH for ontology hierarchies.
• Bonferroni — very conservative; only when you have few, independent tests.

FDR is computed within a library/run. Running many libraries multiplies the total tests, so report per-library FDR and avoid cherry-picking the one library that produced a hit.

Reading GSEA output

• NES (normalized enrichment score) — the headline metric; normalized for set size so it is comparable across sets. Sign = direction (positive = enriched at the top of your ranking, e.g., up in the test condition).
• FDR q-val — significance; filter on this (< 0.05, or < 0.25 for exploratory hypothesis generation, the GSEA convention).
• Leading-edge genes (Lead_genes) — the subset of genes that drive the signal (those before the running-sum peak). Report these; they are the concrete biology and are useful for overlap/redundancy analysis.

Reducing redundant terms

GO and large pathway sets return many overlapping terms describing the same biology. Don't list 40 near-duplicates. Options: - Enrichment map — graph with terms as nodes and edges weighted by gene overlap (Jaccard/overlap coefficient); cluster it and label clusters. gseapy: gp.enrichment_map(...); render with networkx (see the networkx skill). - Leading-edge / gene overlap clustering — group terms sharing most genes; keep one representative per group. - Parent terms / semantic similarity — collapse child GO terms to a parent; REVIGO-style reduction by semantic similarity. - Report a representative term per cluster plus the count of related terms.

Significance vs relevance

• Check the overlap count, not just the p-value. "Term enriched, padj=0.01" with 2 genes out of a 1500-gene set is rarely meaningful.
• Watch gene-set size: tiny sets reach significance with few genes; huge, generic sets ("metabolic process") are uninformative — the min_size/max_size filters (15–500) exist for this reason.
• A very short ORA input (<10 genes) is underpowered; a very long one (>2000) loses specificity — prefer GSEA in both extremes.

Reproducibility checklist

• Record exact library names and versions/date (Enrichr/GO libraries drift).
• Record the background used (or state the default).
• For GSEA, record permutation_num, seed, min_size, max_size, weight, and the ranking metric (e.g., DESeq2 stat).
• State the organism and gene-ID namespace.
• Save the full results table, not just the filtered top hits.

Publication table template

Report a compact, reviewer-friendly table:

For ORA use Odds Ratio + Overlap (k/K); for GSEA use NES + leading-edge size. Note method, library version, background, and correction in the legend.

Common misinterpretations

• "Enriched pathway X" does not mean pathway X is activated — ORA is direction-agnostic unless you split up/down lists; GSEA NES sign gives direction.
• Overlapping significant GO terms are not independent findings.
• Absence of enrichment ≠ absence of biology (power, annotation gaps, wrong background, or ID mismatch can all hide real signal).
• Don't compare raw ES across gene sets — use NES.