KEGG Over Representation Analysis

Performs KEGG pathway Over-Representation Analysis (ORA) on glycoproteins with dysregulated glycosylation.

Usage

enrich_ora_kegg(
  dea_res,
  dea_p_cutoff = 0.05,
  dea_log2fc_cutoff = c(-1, 1),
  organism = "hsa",
  universe = NULL,
  p_adj_method = "BH",
  p_cutoff = 0.05,
  q_cutoff = 0.2,
  min_gs_size = 10,
  max_gs_size = 500
)

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

dea_p_cutoff

P-value cutoff for statistical significance. Defaults to 0.05. For glystats result input, adjusted p-values are used.

dea_log2fc_cutoff

Log2 fold change cutoff statistical significance. A length-2 numeric vector, being negative and positive boundaries, respectively. For example, c(-1, 1) means "log2fc < -1 or log2fc > 1", and c(-Inf, 1) means "log2fc > 1". Defaults to c(-1, 1).

organism

KEGG organism code. Passed to organism of clusterProfiler::enrichKEGG(). Defaults to "hsa" (Homo sapiens). Common codes: "hsa" (human), "mmu" (mouse), "rno" (rat).

universe

Background genes Uniprot IDs, directly passed to universe of downstream enrichment function. If NULL (default), all genes in the data will be used. Another common pattern is to use all detected proteins as backgroud genes. You can use detected_universe() to help you.

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.

q_cutoff

Q-value (FDR) cutoff to filter significant terms. Passed to qvalueCutoff of downstream enrichment function. Defaults to 0.2.

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.

Value

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

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_gc_ora_go(dea_res)  # or other glyfun functions

See also

clusterProfiler::enrichKEGG()