Functional Enrichment of Muscles via Knowledge Graph

Tests whether a set of muscles is enriched for specific anatomical or functional annotations (actions, nerves, body regions, spinal levels) compared to the full background set of muscles in the hypergraph.

Usage

mskEnrichKG(
  muscles,
  annotation_type = c("action", "nerve", "body_region", "spinal_level"),
  hub = NULL,
  hg = NULL
)

Arguments

muscles: Character or integer vector identifying muscles to test.
annotation_type: Character, one of "action", "nerve", "body_region", or "spinal_level". Partial matching is supported.
hub: A PhysioAnnotationHub object (NULL loads default).
hg: An MSKHypergraph object (NULL loads default).

Value

A data.frame with columns:

term: Annotation term
count: Number of query muscles with this term
background_count: Number of background muscles with this term
expected: Expected count under null
fold_enrichment: Ratio of observed to expected
p_value: P-value from hypergeometric test
significant: Logical, TRUE if p < 0.05

Clinical Validity

Enrichment analysis depends on the completeness and accuracy of the underlying knowledge graph annotations. P-values are not adjusted for multiple testing; consider applying Bonferroni or FDR correction when testing multiple annotation types simultaneously.

Examples

if (requireNamespace("PhysioAnnotationHub", quietly = TRUE)) {
  enrichment <- mskEnrichKG(c("Biceps Brachii", "Deltoid", "Trapezius"),
    annotation_type = "nerve")
  enrichment[enrichment$significant, ]
}
#>                     term count total_in_background   expected fold_enrichment
#> 1   Accessory Nerve (XI)     1                   2 0.02222222              45
#> 2         Axillary Nerve     1                   2 0.02222222              45
#> 3 Musculocutaneous Nerve     1                   3 0.03333333              30
#>      p_value significant
#> 1 0.02213961        TRUE
#> 2 0.02213961        TRUE
#> 3 0.03308581        TRUE