rGenomeTracks
rGenomeTracks package leverages the power of pyGenomeTracks software with the interactivity of R. pyGenomeTracks is a python software that offers robust method for visualizing epigenetic data files like narrowPeak, Hic matrix, TADs and arcs, however though, here is no way currently to use it within R interactive session. rGenomeTracks wrapped the whole functionality of pyGenomeTracks with additional utilites to make to more pleasant for R users.
# loading the rGenomeTracks
library(rGenomeTracks)
# loading example data
#library(rGenomeTracksData)Installing PyGenomeTracks
You should have pyGenomeTracks installed on R’s loading environment.
To avoid dependency clash, we highly recommend using
install_pyGenomeTracks(). That way, you ensure using the
tested pyGenomeTracks version with the current release. rGenomeTracks is
supposed to automatically prompt you to install this dependency after
running plot_gtracks(). If this step failed, you can
manually install pyGenomeTracks with
install_pyGenomeTracks()
Principle
rGenomeTracks deals creates tracks in a class
genome_track. Currently, there are 14 tracks available: 1.
track_bed() 2. track_bedgraph() 3. track_bedgraph_matrix() 4.
track_gtf() 5. track_hlines() 6. track_vlines() 7. track_spacer() 8.
track_bigwig() 9. track_epilogos() 10. track_narrowPeak() 11.
track_domains() 12. track_hic_matrix() 13. track_links() 14.
track_scalebar() 15. track_x_axis()
Please refer to the help page for each one of them for details and examples.
We will download .h5 matrix and store the location in temporary directory for demonstration.
# Download h5 example
ah <- AnnotationHub()
query(ah, "rGenomeTracksData")
h5_dir <- ah[["AH95901"]]
# Create HiC track from HiC matrix
h5 <- track_hic_matrix(
file = h5_dir, depth = 250000, min_value = 5, max_value = 200,
transform = "log1p", show_masked_bins = FALSE
)Other demonstration for TADS, arcs and bigwig data will be loaded from the built-in package example data.
# Load other examples
tads_dir <- system.file("extdata", "tad_classification.bed", package = "rGenomeTracks")
arcs_dir <- system.file("extdata", "links2.links", package = "rGenomeTracks")
bw_dir <- system.file("extdata", "bigwig2_X_2.5e6_3.5e6.bw", package = "rGenomeTracks")
# Create TADS track
tads <- track_domains(
file = tads_dir, border_color = "black",
color = "none", height = 5,
line_width = 5,
show_data_range = FALSE,
overlay_previous = "share-y"
)
# Create arcs track
arcs <- track_links(
file = arcs_dir, links_type = "triangles",
line_style = "dashed",
overlay_previous = "share-y",
color = "darkred",
line_width = 3,
show_data_range = FALSE
)
# Create bigwig track
bw <- track_bigwig(
file = bw_dir, color = "red",
max_value = 50,
min_value = 0,
height = 4,
overlay_previous = "no",
show_data_range = FALSE
)genome_track objects can be added together using
+ function.
The track(s) to be plotted is to be passed to
plot_gtracks() for the generation of the plot.
Additionally, plot_gtracks() requires the genomic region to
be plotted. Optionally, you can set plot title, dpi, width, height,
fontsize, track-to-label fraction, label alignment position, and
directory to save the plot.
# Plot the tracks
## Note to verify installing miniconda if not installed.
layout(matrix(c(1,1,2,3,4,4), nrow = 3, ncol = 2, byrow = TRUE))
plot_gtracks(tracks, chr = "X", start = 25 * 10^5, end = 31 * 10^5)
# Plot HiC, TADS and bigwig tracks
plot_gtracks(h5 + tads + bw, chr = "X", start = 25 * 10^5, end = 31 * 10^5)
Tips
Quickly create multiple tracks
If you have tracks with the same format, you can import them quickly by using lapply() and reduce() functions.
dirs <- list.files(system.file("extdata", package = "rGenomeTracks"), full.names = TRUE)
# filter only bed files (without bedgraphs or narrowpeaks)
bed_dirs <- grep(
dirs, pattern = ".bed(?!graph)", perl = TRUE, value = TRUE)
bed_list <- lapply(bed_dirs, track_bed)
bed_tracks <- Reduce("+", bed_list)You can repeat this process for tracks of same category then pass the tracks to plot_gtracks()
Create complex layout figures
You may choose create a complex figure using layout() and split.screen() function then save the device.
Note that you cannot make use of dir argument in plot_gtracks() if you used this method as it is passed to pyGenomeTracks. So, you have to capture R’s graphic device and save it manually.
Session Information
sessionInfo()
#> R version 4.4.2 (2024-10-31)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] rGenomeTracks_1.13.0 rmarkdown_2.29
#>
#> loaded via a namespace (and not attached):
#> [1] Matrix_1.7-1 rGenomeTracksData_0.99.0 jsonlite_1.8.9
#> [4] compiler_4.4.2 Rcpp_1.0.13-1 stringr_1.5.1
#> [7] bmp_0.3 jquerylib_0.1.4 png_0.1-8
#> [10] yaml_2.3.10 fastmap_1.2.0 reticulate_1.40.0
#> [13] lattice_0.22-6 R6_2.5.1 igraph_2.1.1
#> [16] knitr_1.49 imager_1.0.2 maketools_1.3.1
#> [19] readbitmap_0.1.5 bslib_0.8.0 tiff_0.1-12
#> [22] rlang_1.1.4 cachem_1.1.0 stringi_1.8.4
#> [25] xfun_0.49 sass_0.4.9 sys_3.4.3
#> [28] cli_3.6.3 magrittr_2.0.3 digest_0.6.37
#> [31] grid_4.4.2 lifecycle_1.0.4 vctrs_0.6.5
#> [34] evaluate_1.0.1 glue_1.8.0 buildtools_1.0.0
#> [37] purrr_1.0.2 jpeg_0.1-10 tools_4.4.2
#> [40] pkgconfig_2.0.3 htmltools_0.5.8.1