Interactive IGV visualisation (Shiny + igvShiny) of differential regions

Launches a Shiny app with an embedded IGV browser and an interactive table listing differentially-bound regions (from `differential_binding()` or `differential_acccessibility()` results). Clicking on a region in the table will pan IGV to that locus. Sample coverage and region tracks are loaded as quantitative/annotation tracks.

Usage

browse_igv_regions(
  diff_results,
  samples = NULL,
  colour_cond1 = "#ff6600",
  colour_cond2 = "#2288dd",
  use_genome = NULL,
  padding_width = 20000,
  host = "localhost",
  port = NULL
)

Arguments

diff_results: A `DamIDResults` object, as returned by `differential_binding()` or `differential_accessibility()`.
samples: Optional character vector of sample names to display (default: all in dataset).
colour_cond1, colour_cond2: Colours for differentially enriched region tracks.
use_genome: IGV genome name (inferred from peak annotations if not given).
padding_width: Width to pad browser viewbox on either side of the peak
host: Hostname for the server location (defaults to localhost)
port: Port for connection (if NULL (default) the port is assigned by Shiny)

Value

Invisibly returns the Shiny app object created by `shinyApp()`.

Examples

# \donttest{
# This example launches an interactive Shiny app and is not run by
# automated checks. It requires an internet connection for IGV.

.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

# Launch the interactive browser (requires network access; uncomment to run)
# browse_igv_regions(diff_results)
# }