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
)

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 clusterProfiler::enrichGO(). 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

Passed to pAdjustMethod of clusterProfiler::enrichGO().

p_cutoff

Passed to pvalueCutoff of clusterProfiler::enrichGO().

q_cutoff

Passed to qvalueCutoff of clusterProfiler::enrichGO().

Value

A list with two elements:

• tidy_result: A tibble with enrichment results containing the following columns:

  • id: Term ID

  • description: Term description

  • gene_ratio: Ratio of genes in the term to total genes in the input

  • bg_ratio: Ratio of genes in the term to total genes in the background

  • rich_factor: Proportion of the term's total background genes found in the input

  • fold_enrichment: Ratio of gene_ratio to bg_ratio (magnitude of enrichment)

  • z_score: Directional trend of regulation (positive for up, negative for down)

  • p_val: Raw p-value from hypergeometric test

  • p_adj: Adjusted p-value

  • q_val: Q-value (FDR)

  • gene_id: Gene IDs in the term (separated by "/")

  • count: Number of genes in the term

• raw_result: The raw clusterProfiler enrichResult object

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()