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Visualize Genomic Annotation Landscape

Source: R/13_viz_genome.R
av_plot_annotation.Rd

Creates publication-ready plots summarizing the genomic context of variants in an annotated AlphaVarSet. Three plot types are available:

  • "region": Bar chart of region_type distribution (hierarchical: cds, 5utr, 3utr, intron, promoter, intergenic).

  • "tss": Density plot of dist_to_tss on a symmetric log10 scale, showing how far variants are from the nearest TSS.

  • "custom": Bar chart counting variants overlapping with user-defined custom regions (boolean is_*/in_* columns created by av_annotate_regions).

Usage

av_plot_annotation(
  obj,
  type = c("region", "tss", "custom"),
  proximal_window = 2000
)

Arguments

obj

An object of class AlphaVarSet. Must contain annotation columns produced by av_annotate_genes() and/or av_annotate_regions().

type

Character. One of "region", "tss", or "custom".

proximal_window

Integer (bp). For type = "tss" only: half-width of the window around the TSS used to compute the "proximal" fraction shown in the subtitle. Default 2000 matches the promoter definition used by av_annotate_genes().

Value

A ggplot object.

Interpretation

The distribution reflects the genomic context of scored variants. A high proportion of intronic/intergenic variants is typical for regulatory scores. Enrichment in CDS or UTR regions compared to genomic background may indicate protein-coding or post-transcriptional regulatory effects. Compare to the expected genome-wide distribution (~1-2% CDS, ~25% intronic, ~45% intergenic) to identify deviations.

Examples

example_file <- system.file("extdata", "bcl11a_rs1427407_example.csv",
                            package = "AlphaVaR")
obj <- av_import_csv(example_file)
#> Reading bcl11a_rs1427407_example.csv ...

# type = "custom" needs no TxDb: av_annotate_regions() accepts any GRanges.
windows <- GenomicRanges::GRanges(
  seqnames = "chr2",
  ranges   = IRanges::IRanges(
    start = c(60489908, 60590000),
    end   = c(60491908, 60592000)
  )
)
obj <- av_annotate_regions(obj, windows[1], label = "is_variant_window")
obj <- av_annotate_regions(obj, windows[2], label = "is_control_window")
obj$meta$custom_regions
#> [1] "is_variant_window" "is_control_window"

av_plot_annotation(obj, type = "custom")


# "region" and "tss" read the columns av_annotate_genes() adds, so they need
# the hg38 TxDb, a ~1 GB Suggests package.
if (requireNamespace("TxDb.Hsapiens.UCSC.hg38.knownGene", quietly = TRUE)) {
  obj <- av_annotate_genes(obj)
  print(table(obj$data$region_type))
  print(av_plot_annotation(obj, type = "region"))
  print(av_plot_annotation(obj, type = "tss", proximal_window = 2000))
}
#> Extracting gene information from annotation database...
#> Calculating strand-aware distances to nearest TSS...
#> Resolving gene symbols...
#> 'select()' returned 1:1 mapping between keys and columns
#> Classifying genomic regions (hierarchical)...
#> Annotation complete.
#> 
#> intron 
#>   2676