glyfun provides functional enrichment analysis for glycoproteomics
data. It provides two sets of functions, answering different questions:
enrich_xxx(): “Which functions are enriched for proteins with
dysregulated glycosylation?”enrich_gc_xxx(): “Which functions are enriched for dyregulated
glycan traits?”gc is for “glycan-centric”, which means connecting glycan traits
(e.g. core-fucosylation) with functional annotations.
Both Over Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) are supported, with common databases including GO, KEGG, Reactome, WikiPathways, DO (Disease Ontology), and NCG (Network of Cancer Genes).
The glyfun package is not a core glycoverse package. You need to install it individually even if you have installed the meta-package glycoverse.
You can install the latest release of glyfun from r-universe (recommended):
# install.packages("pak")
pak::repo_add(glycoverse = "https://glycoverse.r-universe.dev")
pak::pkg_install("glyfun")
Or from GitHub:
pak::pkg_install("glycoverse/glyfun@*release")
Or install the development version (NOT recommended):
pak::pkg_install("glycoverse/glyfun")
Note: Tips and troubleshooting for the meta-package glycoverse are also applicable here: Installation of glycoverse.
glycoverse
glyfun integrates seamlessly with glydet (for calculating
site-specific derived traits and motif quantification) and glystats
(for differential expression analysis). As the final piece of the
glycoproteomics analytical puzzle, glyfun bridges the gap between raw
glycan quantification and biological insight, enabling researchers to
move beyond data processing to functional glycomining.
library(glycoverse)
library(glyfun)
exp <- auto_clean(real_experiment)
# GO ORA for proteins with dysregulated glycosylation
dea_res <- gly_limma(exp)
enrich_ora_go(dea_res)
# Glycan-centric GO ORA for derived traits
trait_exp <- derive_traits(exp)
trait_dea_res <- gly_limma(trait_exp)
enrich_gc_ora_go(trait_dea_res)