Draw Proportional Venn Diagram for differential binding analysis

Generates a two-set Venn/proportional diagram summarising the results of the differential binding analysis. The set union represents significant binding peaks that fail to show significant differences in occupancy; the exclusive regions of each set represent regions with enriched differential binding in that condition. Note that regions can be bound in both conditions, and still show differential occupancy.

Usage

plot_venn(
  diff_results,
  title = "Enriched binding at loci",
  subtitle = "",
  set_labels = NULL,
  filename = NULL,
  font = "sans",
  format = c("pdf", "svg"),
  region_colours = c("#FFA500", "#2288DD", "#CCCCCC")
)

Arguments

diff_results: A `DamIDResults` object, as returned by `differential_binding()` or `differential_accessibility()`.
title: Plot title to use.
subtitle: Subtitle to use (default is empty).
set_labels: Character vector of length 2. Names for the two sets/circles (defaults to the analysis condition names).
filename: Character. Path at which to save the diagram, if not NULL.
font: Font name to use (default is "sans")
format: Character. Output plot format, "pdf" or "svg" (default "pdf").
region_colours: Character vector of length 2 or 3. Fill colours for each set region (default: c("#FFA500", "#2288DD", "#CCCCCC")).

Value

The function is called to generating a plot. It invisibly returns `NULL`.

Examples

# Helper function to create a sample DamIDResults object
.generate_example_results <- function() {
    mock_genes_gr <- GenomicRanges::GRanges(
        seqnames = S4Vectors::Rle("2L", 7),
        ranges = IRanges::IRanges(
            start = c(1000, 2000, 3000, 5000, 6000, 7000, 8000),
            end = c(1500, 2500, 3500, 5500, 6500, 7500, 20000000)
        ),
        gene_id = c("FBgn001", "FBgn002", "FBgn003", "FBgn004", "FBgn005", "FBgn006", "FBgn007"),
        gene_name = c("geneA", "geneB", "geneC", "geneD", "geneE", "geneF", "LargeTestGene")
    )
    data_dir <- system.file("extdata", package = "damidBind")
    loaded_data <- load_data_peaks(
        binding_profiles_path = data_dir,
        peaks_path = data_dir,
        ensdb_genes = mock_genes_gr,
        quantile_norm = TRUE
    )
    diff_results <- differential_binding(
        loaded_data,
        cond = c("L4", "L5"),
        cond_names = c("L4 Neurons", "L5 Neurons")
    )
    return(diff_results)
}
diff_results <- .generate_example_results()
#> Locating binding profile files
#> Building binding profile dataframe from input files ...
#>  - Loaded: Bsh_Dam_L4_r1-ext300-vs-Dam.kde-norm
#>  - Loaded: Bsh_Dam_L4_r2-ext300-vs-Dam.kde-norm
#>  - Loaded: Bsh_Dam_L5_r1-ext300-vs-Dam.kde-norm
#>  - Loaded: Bsh_Dam_L5_r2-ext300-vs-Dam.kde-norm
#> Applying quantile normalisation
#> Locating peak files
#> Calculating occupancy over peaks
#> Calculating average occupancy per region ...
#> Differential analysis setup:
#> Condition 1: 'L4' (display as 'L4 Neurons')
#>   Found 2 replicates:
#>     Bsh_Dam_L4_r1-ext300-vs-Dam.kde-norm_qnorm
#>     Bsh_Dam_L4_r2-ext300-vs-Dam.kde-norm_qnorm
#> Condition 2: 'L5' (display as 'L5 Neurons')
#>   Found 2 replicates:
#>     Bsh_Dam_L5_r1-ext300-vs-Dam.kde-norm_qnorm
#>     Bsh_Dam_L5_r2-ext300-vs-Dam.kde-norm_qnorm
#> limma contrasts: L4-L5
#> 
#> 277 loci enriched in L4 Neurons
#> Highest-ranked genes:
#> LargeTestGene, LargeTestGene, LargeTestGene, LargeTestGene, LargeTestGene, LargeTestGene, LargeTestGene, LargeTestGene, LargeTestGene, LargeTestGene
#> 
#> 173 loci enriched in L5 Neurons
#> Highest-ranked genes:
#> LargeTestGene, LargeTestGene, LargeTestGene, LargeTestGene, LargeTestGene, LargeTestGene, LargeTestGene, LargeTestGene, LargeTestGene, LargeTestGene

# Generate the Venn diagram
plot_venn(diff_results)