Choosing a Similarity Method

This guide helps you select the right term-level similarity method and groupwise aggregation strategy for your use case.

Term-level methods

IC-based methods (Resnik, Lin, JC, SimRel)

Use these when you have a representative annotation corpus (e.g., a well-curated GAF file for your organism).

IC-based methods rely on annotation frequencies to determine how informative each term is. They work well when:

• Your annotation corpus is large and representative of the biological domain you’re studying.

• You want scores that reflect functional specificity relative to known biology.

• You need well-studied methods with established benchmarks in the literature.

Trade-offs: scores depend on the annotation corpus. Different GAF versions or organisms produce different IC distributions and therefore different similarity scores. If your corpus is small or biased toward well-studied genes, IC estimates may be unreliable for less-annotated terms.

Method	Range	Best for
resnik	>= 0	Raw IC of shared ancestor; good for ranking, not comparing across datasets
lin	[0, 1]	Normalized; most widely used IC-based method
jc	>= 0	Distance-derived; sensitive to small IC differences
simrel	[0, 1]	Like Lin but penalizes shallow (uninformative) MICAs

Topological methods (Wang)

Use Wang when annotation data may be biased or unavailable.

Wang similarity uses only the GO graph structure (edge types and path weights) to compute similarity. It assigns weights of 0.8 for is_a edges and 0.6 for part_of edges, decaying as paths get longer.

Best for:

• Organisms with sparse or biased annotations.

• Comparing results across different annotation versions without IC-induced variation.

• When you want a stable, corpus-independent baseline.

Trade-off: does not capture functional specificity from real annotation data.

Hybrid methods (GraphIC, TopoICSim)

Use these when you want both IC and topology.

• GraphIC: combines IC values with graph-structural features.

• TopoICSim: weights topology-aware paths by IC, producing a normalized score in [0, 1]. It is more discriminating than purely IC-based methods for terms with similar IC but different graph positions.

Best for:

• Situations where both graph structure and annotation specificity matter.

• Getting a single method that balances multiple sources of information.

Quick decision table

Use case	Recommended method
General-purpose, well-annotated organism	lin
Ranking by shared function (no normalization needed)	resnik
Sparse or biased annotations	wang
Maximum discrimination between similar terms	topoicsim
Penalize shallow common ancestors	simrel
Corpus-independent baseline	wang

Groupwise strategy selection

When comparing genes (or term sets), individual pairwise term similarities must be aggregated into a single score. The choice of groupwise strategy affects what aspect of functional overlap you measure.

Strategy	Key	What it measures	Best for
BMA	bma	Average of best matches from both directions	Balanced comparison; most common
Maximum	max	Highest pairwise similarity	“Do these genes share any function?”
Average	avg	Mean of all pairwise similarities	Overall functional overlap
SimGIC	simgic	IC-weighted Jaccard of shared ancestor sets	IC-aware set overlap; good for clustering
Hausdorff	hausdorff	Worst-case best-match	Worst-case guarantee; conservative

General recommendation: start with BMA (bma). It is the most widely used strategy and provides a balanced view of functional similarity. Use SimGIC when you want IC-weighted set overlap (especially useful for clustering and enrichment-adjacent tasks). Use MAX for a permissive “any shared function” signal.

Namespace guidance

The Gene Ontology has three namespaces. Choose based on the biological question:

• BP (Biological Process): largest namespace with the most annotations. Best for characterizing overall gene function and pathway involvement. Most commonly used in literature benchmarks.

• MF (Molecular Function): describes biochemical activity (e.g., kinase activity, binding). More specific and smaller than BP. Best for molecular-level questions.

• CC (Cellular Component): describes subcellular localization (e.g., nucleus, membrane). Smallest namespace. Best for localization-related analyses.

If unsure, start with BP – it provides the broadest functional characterization and the most statistical power due to its size.