/tidyCoverage

Primary LanguageROtherNOASSERTION

tidyCoverage

Load libraries and example datasets

library(tidyCoverage)
library(tidySummarizedExperiment)
library(rtracklayer)
library(plyranges)
library(purrr)
library(ggplot2)

# ~~~~~~~~~~~~~~~ Import genomic features into a named list ~~~~~~~~~~~~~~~ #
features <- list(
    TSSs = system.file("extdata", "TSSs.bed", package = "tidyCoverage"),
    conv_sites = system.file("extdata", "conv_transcription_loci.bed", package = "tidyCoverage")
) |> map(~ import(.x))

# ~~~~~~~~~~~~ Import coverage tracks into a `BigWigFileList` ~~~~~~~~~~~~~ #
tracks <- list(
    Scc1 = system.file("extdata", "Scc1.bw", package = "tidyCoverage"), 
    RNA_fwd = system.file("extdata", "RNA.fwd.bw", package = "tidyCoverage"),
    RNA_rev = system.file("extdata", "RNA.rev.bw", package = "tidyCoverage"),
    PolII = system.file("extdata", "PolII.bw", package = "tidyCoverage"), 
    MNase = system.file("extdata", "MNase.bw", package = "tidyCoverage")
) |> BigWigFileList()

Plot tracks coverage aggregated over genomic features

CE <- CoverageExperiment(tracks, features, width = 1000, ignore.strand = FALSE) 
CE |> 
    filter(track %in% c('MNase', 'PolII')) |> 
    filter(features == 'TSSs') |> 
    aggregate() |> 
    ggplot(aes(x = coord, y = mean)) + 
    geom_ribbon(aes(ymin = ci_low, ymax = ci_high, fill = track), alpha = 0.2) + 
    geom_line(aes(col = track)) + 
    facet_grid(track ~ ., scales = "free") + 
    labs(x = 'Distance from TSS', y = 'Signal coverage') + 
    theme_bw() + 
    theme(legend.position = 'top')

Plot coverage over a single locus

CoverageExperiment(tracks, GRanges("II:450001-455000"), width = 5000) |> 
    expand() |> 
    ggplot(aes(x = coord, y = coverage)) + 
        geom_col(aes(fill = track, col = track)) + 
        facet_grid(track~., scales = 'free') + 
        scale_x_continuous(expand = c(0, 0)) + 
        theme_bw() + 
        theme(legend.position = "none", aspect.ratio = 0.1)

Related projects

A number of CRAN, Bioconductor or GitHub packages already exist to enable genomic track data visualization, for instance:

Compared to these existing solutions, tidyCoverage directly extends SummarizedExperiment infrastructure and follows tidy "omics" principles. It does not directly provide plotting functionalities, but instead focuses on data recovery, structure and coercion, using a familiar grammar and standard representation of the data. This ensures seamless integration of genomic track investigation in exisiting Bioconductor and data analysis workflows.