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Visualize Top Network Enrichment Statistics

visualize_topn.Rd

This function generates a bar plot to visualize the top n network enrichment results for a specified category. The plot highlights the effect size (beta) and the false discovery rate (-log10(FDR)), enabling quick assessment of the most enriched terms or entities in the network.

Usage

visualize_topn(
  tkoi_list,
  category = "BiologicalProcess",
  top_n = 25,
  ranknorm = FALSE,
  lognorm = TRUE,
  high_color = "#FF5733",
  low_color = "#154360"
)

Arguments

tkoi_list

A tKOIList object containing network summary statistics.

category

A character string specifying the category to visualize. Default is "BiologicalProcess". Accepted values include:

  • "Anatomy"

  • "BiologicalProcess"

  • "CellType"

  • "CellularComponent"

  • "ClinicalLab"

  • "Complex"

  • "Compound"

  • "Disease"

  • "EC"

  • "Gene"

  • "MiRNA"

  • "MolecularFunction"

  • "Pathway"

  • "Protein"

  • "ProteinDomain"

  • "ProteinFamily"

  • "PwGroup"

  • "Reaction".

top_n

An integer specifying the number of top results to display. Default is 25.

ranknorm

A logical value indicating whether to apply inverse rank-based normalization to the beta values. Default is FALSE.

lognorm

A logical value indicating whether to apply log base-2 transformation to the beta values. Default is TRUE.

high_color

A character string specifying the high value color for the gradient scale representing -log10(FDR). Default is "#FF5733".

low_color

A character string specifying the low value color for the gradient scale representing -log10(FDR). Default is "#154360".

Value

A ggplot object representing the bar plot of top network enrichment statistics.

Details

The function performs the following steps:

  1. Extracts the top n entries from the specified category within the network_summary_statistics slot of the tKOIList object.

  2. Filters out entries with missing beta values.

  3. Optionally applies transformations to the beta values:

    • If ranknorm is set to TRUE, applies inverse rank-based normalization.

    • If lognorm is set to TRUE, applies log base-2 transformation.

    • If both transformations are enabled, ranknorm is overridden and set to FALSE.

  4. Adjusts very small fdr values to avoid extreme values in plotting.

  5. If the category is "Gene", the function joins additional metadata from the tkoi::genes table to obtain gene names. For other categories, the name column is used as the identifier.

  6. Filters out entries with missing identifiers and ensures consistent ordering of identifiers for plotting.

  7. Creates a horizontal bar plot where:

    • The x-axis represents the (possibly transformed) effect size (beta).

    • The y-axis represents the identifiers (e.g., terms or entities).

    • The color gradient of the bars represents -log10(FDR), with customizable low and high colors.

See also

ggplot, arrange, filter

Examples

if (FALSE) { # \dontrun{
# Visualize the top 10 Biological Processes with default transformations and colors
plt <- visualize_topn(tkoi_list, category = "BiologicalProcess", top_n = 10)
print(plt)

# Visualize the top 20 Genes with custom color gradient
plt <- visualize_topn(tkoi_list, category = "Gene", top_n = 20, high_color = "#E74C3C", low_color = "#3498DB")
print(plt)

# Visualize the top 15 Pathways without rank-based normalization or log transformation
plt <- visualize_topn(tkoi_list, category = "Pathway", top_n = 15, ranknorm = FALSE, lognorm = FALSE)
print(plt)

# Visualize the top 5 Diseases with rank-based normalization enabled
plt <- visualize_topn(tkoi_list, category = "Disease", top_n = 5, ranknorm = TRUE, lognorm = FALSE)
print(plt)
} # }