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Plot a Venn Diagram from Genomic or Set Overlap Results

Source: R/plotVenn.R
plotVenn.Rd

This function creates a Venn diagram using the eulerr package to visualize intersections across multiple sets. Supports both GenomicOverlapResult and SetOverlapResult objects. Diagrams are recommended for two to four sets: beyond that, an area-proportional layout is rarely attainable, and plotUpSet is the better choice.

Usage

plotVenn(
  overlap_object,
  fills = TRUE,
  edges = TRUE,
  labels = FALSE,
  quantities = list(type = "counts"),
  legend = "right",
  main = NULL,
  verbose = TRUE,
  ...
)

Arguments

overlap_object

A GenomicOverlapResult or SetOverlapResult object returned by computeOverlaps.

fills

Controls the fill appearance of the diagram. Can be:

  • logical: TRUE (default) shows fills, FALSE hides them

  • character vector: Colors for the fills. Default colors are: c("#2B70AB", "#FFB027", "#3EA742", "#CD3301", "#9370DB", "#008B8B", "#D87093", "#C7C4FC", "#FCBAA3", "#187EFA", "#CFCE64", "#527346", "#F8B6D8", "#AB80C5", "#B99074"). One color is used per region of the diagram, so a two-set diagram uses the first three, a three-set diagram the first seven, and a four-set diagram all fifteen.

  • list: Fine control with graphical parameters including fill (colors), alpha (transparency 0-1)

edges

Controls the edge/border appearance. Can be:

  • logical: TRUE (default) shows edges, FALSE hides them

  • character vector: Colors for the edges

  • list: Fine control with col (colors), alpha (transparency 0-1), lty (line type), lwd (line width), lex (line expansion)

labels

Controls set labels. Can be:

  • logical: TRUE shows default labels, FALSE hides them

  • character vector: Custom text for labels

  • list: Fine control with col (text color), fontsize, font (1=plain, 2=bold, 3=italic, 4=bold italic), fontfamily, cex (character expansion), alpha (transparency 0-1)

quantities

Controls intersection quantities display. Can be:

  • logical: TRUE shows counts, FALSE hides them

  • character vector: Custom text labels

  • list: Fine control with type (c("counts", "percent")), col (text color), fontsize, font, fontfamily, cex, alpha

legend

Controls the legend. Can be:

  • logical: FALSE to disable

  • character: Position ("right", "top", "bottom", "left")

  • list: Fine control with side (position), labels (custom labels), col, fontsize, cex, fontfamily

main

Title for the plot. Can be character, expression, or list with label (text), col, fontsize, font, fontfamily

verbose

Logical. If TRUE (default), prints diagError and whether it exceeds the 1e-6 threshold, and names any populated region the diagram gives no area to. The diagnostics are attached to the returned object regardless (see Value).

...

Additional arguments passed to plot.euler.

Value

A Venn diagram plot generated by eulerr, with two attributes:

  • "fit_diagnostics": a list with stress, diagError, regionError (see euler) and undrawnRegions, the populated regions given no area. diagError is the largest difference, over all regions, between the share of the diagram's area a region receives and the share its count requires; Micallef and Rodgers (2014) consider a diagram accurate when it is at most 1e-6.

  • "euler_fit": the eulerr fit itself.

See also

plotVennError to see which regions the diagram misrepresents and in which direction, plotUpSet.

Examples

# Example with gene sets
data(gene_list)
res_sets <- computeOverlaps(gene_list)

# Basic plot
plotVenn(res_sets)
#> ✔ Venn diagError = 8.693e-13  (<= 1e-06)
#>   Access fit diagnostics with attr(<plotVenn output>, "fit_diagnostics")


# Customize fills with transparency and custom colors
plotVenn(res_sets,
         fills = list(fill = c("#FF6B6B", "#4ECDC4", "#45B7D1"),
                      alpha = 0.6))
#> ✔ Venn diagError = 8.693e-13  (<= 1e-06)
#>   Access fit diagnostics with attr(<plotVenn output>, "fit_diagnostics")


# Customize edges
plotVenn(res_sets,
         edges = list(col = "darkgray", lwd = 2, lty = 2))
#> ✔ Venn diagError = 2.510e-14  (<= 1e-06)
#>   Access fit diagnostics with attr(<plotVenn output>, "fit_diagnostics")


# Customize labels
plotVenn(res_sets,
         labels = list(col = "white", font = 2, fontsize = 14))
#> ✔ Venn diagError = 8.693e-13  (<= 1e-06)
#>   Access fit diagnostics with attr(<plotVenn output>, "fit_diagnostics")


# Show both counts and percentages
plotVenn(res_sets,
         quantities = list(type = c("counts", "percent"),
                          col = "black", fontsize = 10))
#> ✔ Venn diagError = 8.693e-13  (<= 1e-06)
#>   Access fit diagnostics with attr(<plotVenn output>, "fit_diagnostics")


# Add a title
plotVenn(res_sets,
         main = list(label = "Gene Set Overlaps",
                     col = "navy", fontsize = 16, font = 2))
#> ✔ Venn diagError = 8.693e-13  (<= 1e-06)
#>   Access fit diagnostics with attr(<plotVenn output>, "fit_diagnostics")


# Transparent fills with colored borders only
plotVenn(res_sets,
         fills = "transparent",
         edges = list(col = c("red", "blue", "green"), lwd = 3))
#> ✔ Venn diagError = 8.693e-13  (<= 1e-06)
#>   Access fit diagnostics with attr(<plotVenn output>, "fit_diagnostics")


# Custom legend
plotVenn(res_sets,
         legend = list(side = "bottom",
                      labels = c("Treatment A", "Treatment B", "Control"),
                      fontsize = 12))
#> ✔ Venn diagError = 8.693e-13  (<= 1e-06)
#>   Access fit diagnostics with attr(<plotVenn output>, "fit_diagnostics")


# Inspect eulerr's fit diagnostics (stress, diagError, regionError)
venn <- plotVenn(res_sets)
#> ✔ Venn diagError = 2.821e-13  (<= 1e-06)
#>   Access fit diagnostics with attr(<plotVenn output>, "fit_diagnostics")
attr(venn, "fit_diagnostics")
#> $stress
#> [1] 5.208696e-25
#> 
#> $diagError
#> [1] 2.820591e-13
#> 
#> $regionError
#>                               random_genes_A 
#>                                 1.323386e-13 
#>                               random_genes_B 
#>                                 1.004752e-13 
#>                               random_genes_C 
#>                                 1.043610e-13 
#>                random_genes_A&random_genes_B 
#>                                 3.377854e-14 
#>                random_genes_A&random_genes_C 
#>                                 2.820591e-13 
#>                random_genes_B&random_genes_C 
#>                                 9.008072e-14 
#> random_genes_A&random_genes_B&random_genes_C 
#>                                 6.875056e-14 
#> 
#> $undrawnRegions
#> character(0)
#>