protti (version 0.9.0)

calculate_kegg_enrichment: Perform KEGG pathway enrichment analysis

Description

Analyses enrichment of KEGG pathways associated with proteins in the fraction of significant proteins compared to all detected proteins. A Fisher's exact test is performed to test significance of enrichment.

Usage

calculate_kegg_enrichment(
  data,
  protein_id,
  is_significant,
  pathway_id = pathway_id,
  pathway_name = pathway_name,
  plot = TRUE,
  plot_cutoff = "adj_pval top10"
)

Value

A bar plot displaying negative log10 adjusted p-values for the top 10 enriched pathways. Bars are coloured according to the direction of the enrichment. If plot = FALSE, a data frame is returned.

Arguments

data

a data frame that contains at least the input variables.

protein_id

a character column in the data data frame that contains the protein accession numbers.

is_significant

a logical column in the data data frame that indicates if the corresponding protein has a significantly changing peptide. The input data frame may contain peptide level information with significance information. The function is able to extract protein level information from this.

pathway_id

a character column in the data data frame that contains KEGG pathway identifiers. These can be obtained from KEGG using fetch_kegg.

pathway_name

a character column in the data data frame that contains KEGG pathway names. These can be obtained from KEGG using fetch_kegg.

plot

a logical value indicating whether the result should be plotted or returned as a table.

plot_cutoff

a character value indicating if the plot should contain the top 10 most significant proteins (p-value or adjusted p-value), or if a significance cutoff should be used to determine the number of GO terms in the plot. This information should be provided with the type first followed by the threshold separated by a space. Example are plot_cutoff = "adj_pval top10", plot_cutoff = "pval 0.05" or plot_cutoff = "adj_pval 0.01". The threshold can be chosen freely.

Examples

# \donttest{
# Load libraries
library(dplyr)

set.seed(123) # Makes example reproducible

# Create example data
kegg_data <- fetch_kegg(species = "eco")

if (!is.null(kegg_data)) { # only proceed if information was retrieved
  data <- kegg_data %>%
    group_by(uniprot_id) %>%
    mutate(significant = rep(
      sample(
        x = c(TRUE, FALSE),
        size = 1,
        replace = TRUE,
        prob = c(0.2, 0.8)
      ),
      n = n()
    ))

  # Plot KEGG enrichment
  calculate_kegg_enrichment(
    data,
    protein_id = uniprot_id,
    is_significant = significant,
    pathway_id = pathway_id,
    pathway_name = pathway_name,
    plot = TRUE,
    plot_cutoff = "pval 0.05"
  )

  # Calculate KEGG enrichment
  kegg <- calculate_kegg_enrichment(
    data,
    protein_id = uniprot_id,
    is_significant = significant,
    pathway_id = pathway_id,
    pathway_name = pathway_name,
    plot = FALSE
  )

  head(kegg, n = 10)
}
# }