GO Gene Set Enrichment Analysis

Performs Gene Ontology (GO) Gene Set Enrichment Analysis (GSEA) on glycoproteins with dysregulated glycosylation.

Usage

enrich_gsea_go(
  dea_res,
  rank_by = "signed_log10p",
  aggr = "median",
  orgdb = "org.Hs.eg.db",
  ont = "MF",
  p_adj_method = "BH",
  p_cutoff = 0.05,
  min_gs_size = 10,
  max_gs_size = 500,
  exponent = 1,
  eps = 1e-10,
  seed = FALSE
)

Arguments

dea_res

Differential analysis result. Can be one of:

• Result from glystats::gly_limma() (two groups), glystats::gly_ttest(), or glystats::gly_wilcox(), called on an glyexp::experiment() of "traitproteomics" type.

• A tibble with the following columns:

  • protein: Uniprot ID of proteins

  • trait: A glycosylation trait (e.g. "TFc" for proportion of core-fucosylated glycans)

  • site: The glycosylation site.

  • p_val: p-values, preferably adjusted p-values

  • log2fc: log2 of fold change

rank_by

Criteria for ranking proteins. One of the following:

• "log2fc": log2 fold change with signs

• "abs_log2fc": absolute log2 fold change

• "log10p": negative log10 p-value

• "signed_log10p" (default): log10 p-value with signs of log2 fold change

• "log2fc_log10p": log2 fold change multiplied by negative log10 p-value

aggr

Aggregation method for combining multiple scores across different traits and sites for the same protein. One of "median", "mean", or "max". Defaults to "median".

orgdb

An OrgDb object. Passed to OrgDb of downstream enrichment function.

ont

Ontology type. Passed to ont of clusterProfiler::enrichGO(). "BP", "MF", "CC", or "ALL". Defaults to "MF".

p_adj_method

P-value adjustment method. One of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". Passed to pAdjustMethod of downstream enrichment function. Defaults to "BH".

p_cutoff

P-value cutoff to filter significant terms. Passed to pvalueCutoff of downstream enrichment function. Defaults to 0.05.

min_gs_size

Minimal size of each gene set for analyzing. Gene sets with fewer genes than this threshold will be excluded. Passed to minGSSize of downstream enrichment function. Defaults to 10.

max_gs_size

Maximum size of each gene set for analyzing. Gene sets with more genes than this threshold will be excluded. Passed to maxGSSize of downstream enrichment function. Defaults to 500.

exponent

Weight of each step. Passed to exponent of clusterProfiler::gseGO(). Defaults to 1.

eps

Epsilon for calculating p-values. Passed to eps of clusterProfiler::gseGO(). Defaults to 1e-10.

seed

Logical indicating whether to set a random seed for reproducibility. Passed to seed of clusterProfiler::gseGO(). Defaults to FALSE.

Value

A clusterProfiler gseaResult object. It can be readily converted to a tibble with tibble::as_tibble(), or visualized with clusterProfiler functions like clusterProfiler::ridgeplot().

How it ranks proteins

GSEA requires a ranked list of proteins as input. This function ranks proteins based on the median absolute log2 fold change across all traits and sites. This reflects the overall glycosylation dysregulation degree of each glycoprotein. You can use rank_by to specify other ranking criteria, such as p-values or signed log2 fold changes. You can also use aggr to specify how to aggregate multiple scores for the same protein across different traits and sites.

Common usage pattern

A common pattern of using this function is:

# 1. Perform differential analysis with `glystats`.
dea_res <- gly_ttest(exp)

# 2. Use this function.
go_res <- enrich_gsea_go(dea_res)  # or `enrich_gsea_xxx()` functions

See also

clusterProfiler::gseGO()