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Post-Processing

GO enrichment routinely returns many related terms. The scoring engine should stay simple and complete; reduction should be a composable post-processing layer that consumes enrichment TSV plus closure/annotation metadata.

Standard Families

Significance cutoff is the first reduction step. PANTHER-style APIs expose a cutoff parameter; when FDR correction is used, the cutoff is applied to FDR, otherwise to p-value. Our eval configs mirror this with max_p_adjust: 0.05.

GO slims are a curated reduction strategy. Instead of pruning enriched terms after scoring, project annotations or results onto a smaller subset of broad terms. This is useful for overviews, less useful when the user wants precise mechanistic terms.

Slim-descendant filters are a related report-ranking strategy. Instead of projecting results onto slim terms, keep candidate terms that are themselves in a slim or descend from a slim term, often with a target-size guard. This is useful for “largest changed term” summaries where root-like terms such as broad molecular function categories would otherwise dominate.

Antislim filters invert that idea: exclude a slim region and its descendants from a report. This is useful when a known broad ontology branch is technically correct but distracts from the biological question being reviewed.

Ancestor-descendant pruning uses the closure graph. A simple policy is: for each query, if a term has a significant descendant with equal or better adjusted p-value, hide the ancestor. This is easy to explain, but it can over-prune when a broad parent captures a coherent process and many children are sparse.

Gene-overlap pruning compares term gene sets. If two enriched terms share nearly the same query-overlap genes, keep one representative. Representative selection can rank by adjusted p-value, specificity, overlap size, or information content.

GO Trimming-style approaches remove redundant parent terms based on graph paths and result statistics after enrichment has already been run.

REVIGO-style approaches cluster terms by semantic similarity and keep representatives. This is more flexible than strict ancestor-descendant pruning because related terms need not sit on the same direct path.

Topology-aware scoring methods such as topGO elim, weight, weight01, and parentchild, or Ontologizer-style parent-child/model-set methods, are not just post-processing. They change the p-values by accounting for the GO graph during scoring. We should support them later as alternate statistical models, not as TSV filters.

Proposed Composable Reducers

Reducers should take a full result table and emit:

  • a reduced table;
  • a mapping from hidden term to representative term;
  • a reason code;
  • parameters and input file digests in metadata.

Candidate reducer modes:

ModeKeepHide
ancestor-dominatedmore specific descendantancestor when descendant has p-adjust <= ancestor p-adjust times tolerance
ancestor-dominated-strictmore significant termancestor only when descendant has equal or better p-adjust
overlap-jaccardbest ranked representativeterms whose overlap gene Jaccard exceeds threshold
semantic-clustercluster representativeterms in same semantic-similarity cluster
go-slim-projectslim termnon-slim terms after projection
slim-descendant-includeterms in or below selected slim termsterms outside the selected slim scope
slim-descendant-excludeterms outside selected slim terms and descendantsterms in or below selected slim terms

The tolerance is important. Sometimes a general parent has a slightly better p-value because it adds many relevant genes. A practical rule is not binary dominance, but dominance with a margin:

hide ancestor A if descendant D is significant and
  p_adjust(D) <= p_adjust(A) * tolerance

With tolerance = 1.0, the descendant must be at least as significant. With tolerance = 2.0, a slightly worse but much more specific descendant can still represent the signal.

PANTHER Behavior

PANTHER’s public service exposes Fisher or binomial tests, FDR/Bonferroni/none correction, and a cutoff parameter. Public documentation emphasizes cutoff-filtered result tables and GO-slim datasets. I did not find evidence that PANTHER applies a dynamic ancestor-descendant redundancy-pruning pass to complete GO over-representation outputs. For our purposes, treat PANTHER as a reference for cutoff filtering and GO-slim-style summaries, not as a specific redundancy-pruning algorithm.

For eval tables, keep the unpruned significant TSV as the canonical result:

max_p_adjust: 0.05
post_processing: none

Then create optional reduced views:

post_processing:
  method: ancestor-dominated
  p_adjust_tolerance: 2.0
  prefer: specificity

This avoids losing terms before diffing or debugging.

Sources