[](https://doi.org/10.1093/bioinformatics/btae487)
[](https://www.tidyverse.org/lifecycle/#stable)
# tidyCoverage
The `tidyCoverage` R package provides a framework for rapid investigation of collections of genomic tracks over genomic features, relying on the principle of tidy data manipulation. It relies on `CoverageExperiment` and `AggregatedCoverage` classes, directly extending the `SummarizedExperiment` fundamental class,
If you are using `tidyCoverage`, please consider citing:
Serizay J, Koszul R (2024). “Epigenomics coverage data extraction and aggregation in R with tidyCoverage.” _Bioinformatics_ *40*, doi:10.1093/bioinformatics/btae487 <https://doi.org/10.1093/bioinformatics/btae487>.
## Installation
In `R >= 4.4` and `Bioconductor >= 3.19`:
```r
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("tidyCoverage")
```
## Load libraries and example datasets
```r
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()
```
## Extract coverage for each track over each set of features
```r
CE <- CoverageExperiment(tracks, features, width = 5000, ignore.strand = FALSE)
```
## Plot tracks coverage aggregated over genomic features
```r
CE |>
filter(track %in% c('MNase', 'PolII')) |>
filter(features == 'TSSs') |>
aggregate() |>
ggplot() +
geom_aggrcoverage(aes(col = track)) +
facet_grid(track ~ ., scales = "free") +
labs(x = 'Distance from TSS', y = 'Signal coverage')
```
## Plot coverage over a single locus
```r
CoverageExperiment(tracks, GRanges("II:450001-455000")) |>
expand() |>
ggplot() +
geom_coverage(aes(fill = track)) +
facet_grid(track~., scales = 'free')
```
## Related projects
A number of `CRAN`, `Bioconductor` or `GitHub` packages already exist to enable genomic track
data visualization, for instance:
- `Gviz` [\[Bioconductor\]](https://www.bioconductor.org/packages/release/bioc/html/Gviz.html)
- `soGGi` [\[Bioconductor\]](https://www.bioconductor.org/packages/release/bioc/html/soGGi.html)
- `GenomicPlot` [\[Bioconductor\]](https://www.bioconductor.org/packages/release/bioc/html/GenomicPlot.html)
- `plotgardener` [\[Bioconductor\]](https://www.bioconductor.org/packages/release/bioc/html/plotgardener.html)
- `genomation` [\[Bioconductor\]](https://www.bioconductor.org/packages/release/bioc/html/genomation.html)
- `ggcoverage` [\[GitHub\]](https://github.com/showteeth/ggcoverage)
- `GenomicScores` [\[Bioconductor\]](https://www.bioconductor.org/packages/release/bioc/html/GenomicScores.html)
Compared to these existing solutions, `tidyCoverage` directly extends `SummarizedExperiment` infrastructure and
follows [tidy "omics" principles](https://www.biorxiv.org/content/10.1101/2023.09.10.557072v2). 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.
