# 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()`

    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.

     # Create one object from HiC, arcs and bigwid
     tracks <- tads + arcs + bw

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 4.4.1 (2024-06-14)
    #> Platform: x86_64-pc-linux-gnu
    #> Running under: Ubuntu 24.04.1 LTS
    #>
    #> Matrix products: default
    #> BLAS:   /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so
    #> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
    #>
    #> locale:
    #>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    #>  [3] LC_TIME=en_GB              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: America/New_York
    #> tzcode source: system (glibc)
    #>
    #> attached base packages:
    #> [1] stats     graphics  grDevices utils     datasets  methods   base
    #>
    #> other attached packages:
    #> [1] rGenomeTracks_1.12.0
    #>
    #> loaded via a namespace (and not attached):
    #>  [1] vctrs_0.6.5              cli_3.6.3                knitr_1.48
    #>  [4] rlang_1.1.4              xfun_0.48                highr_0.11
    #>  [7] stringi_1.8.4            tiff_0.1-12              purrr_1.0.2
    #> [10] png_0.1-8                readbitmap_0.1.5         jsonlite_1.8.9
    #> [13] glue_1.8.0               htmltools_0.5.8.1        sass_0.4.9
    #> [16] rmarkdown_2.28           grid_4.4.1               evaluate_1.0.1
    #> [19] jquerylib_0.1.4          fastmap_1.2.0            yaml_2.3.10
    #> [22] lifecycle_1.0.4          stringr_1.5.1            compiler_4.4.1
    #> [25] bmp_0.3                  igraph_2.1.1             Rcpp_1.0.13
    #> [28] pkgconfig_2.0.3          imager_1.0.2             lattice_0.22-6
    #> [31] digest_0.6.37            R6_2.5.1                 reticulate_1.39.0
    #> [34] magrittr_2.0.3           rGenomeTracksData_0.99.0 Matrix_1.7-1
    #> [37] bslib_0.8.0              tools_4.4.1              jpeg_0.1-10
    #> [40] cachem_1.1.0