4. Creating an Interactive Line Chart with
shiny.gosling
In this tutorial, we’ll explore how to create an interactive line
chart visualization of genomic data using the shiny and shiny.gosling
packages in R. This visualization will enable users to explore trends
and patterns within genomic datasets.
Introduction
The shiny and shiny.gosling packages offer powerful tools for
building interactive data visualizations within Shiny apps. In this
tutorial, we’ll demonstrate the process of constructing an interactive
line chart visualization using genomic data. This example leverages the
shiny package for user interface design and the shiny.gosling package
for generating the visualizations.
Understanding Multivec Data
Multivec
is a file format introduced by Higlass
suitable for visualizing multi-dimensional numerical data across genomic
coordinates. It’s widely utilized for representing various genomic
experiments such as ChIP-seq, ATAC-seq, and Hi-C, where measurements are
taken at different genomic positions.
Multivec data is essentially a matrix where rows correspond to
genomic positions or regions, and columns correspond to different
samples or experiments. Each matrix entry represents a value associated
with a specific genomic position and sample. The genomic positions are
typically represented as chromosomal coordinates (chromosome name and
base pair position), allowing accurate alignment with the genome. Tools
like the UCSC Genome Browser, IGV, and libraries like shiny.gosling can
visualize multivec data effectively.
To delve deeper into multivec data and its application in genomics,
explore these resources:
UCSC Genome
Browser: A versatile tool for visualizing genomic data, including
multivec data. Learn how to visualize multivec data in the UCSC Genome
Browser.
Integrative
Genomics Viewer (IGV): Another popular genome visualization tool
supporting multivec data. Discover how to load and visualize multivec
data in IGV.
BedGraph and
BigWig Formats: These common formats are used to represent multivec
data. Explore explanations of the BedGraph and BigWig formats.
Call library libraries.
library(shiny)
library(shiny.gosling)
Creating the Data Object
We’ll begin by fetching the multivec data using the track_data
function. This function retrieves data from a specified URL, where the
data is organized as a matrix with rows representing samples and columns
representing positions. We’ll define the row, column, and value
attributes along with the sample categories. The categories parameter
should contain a list of category names (e.g., “sample 1”).
# Create data object ----
view1_data <- track_data(
url = "https://resgen.io/api/v1/tileset_info/?d=UvVPeLHuRDiYA3qwFlm7xQ",
type = "multivec",
row = "sample",
column = "position",
value = "peak",
categories = list("sample 1")
)
Constructing the Line Chart Track
To visualize the data, we’ll define the visual channels for the line
chart. We’ll specify that the x-axis should represent genomic positions
and the y-axis should represent peak values. The add_single_track
function creates a line chart visualization using these visual channels.
Additionally, we’ll set the width and height of the plot and specify the
data source.
# Create visual channels ----
view1_x <- visual_channel_x(
field = "position", type = "genomic", axis = "bottom"
)
view1_y <- visual_channel_y(
field = "peak", type = "quantitative", axis = "right"
)
# Create single track ----
single_track <- add_single_track(
width = 800,
height = 180,
data = view1_data,
mark = "line",
x = view1_x,
y = view1_y,
size = visual_channel_size(
value = 2
)
)
Composing and Arranging the View
The compose_view function is used to create a composed view that
contains the single line chart track. The layout of the view is
specified as “linear”.
The arrange_views function arranges the composed view created
earlier. The title and subtitle for the arrangement are specified. The
arranged view is named single_composed_views.
# Compose the track ----
single_composed_view <- compose_view(
tracks = single_track,
layout = "linear"
)
# Arrange the view above ----
single_composed_views <- arrange_views(
title = "Basic Marks: line",
subtitle = "This is a simple line chart.",
views = single_composed_view
)
Shiny App ui
The navbarPage function creates a navigation bar at the top of the
user interface. Within the navigation bar, there is a single tab panel
named “Line Chart.” Inside the tab panel, the use_gosling function is
used to incorporate the shiny.gosling package. The fluidPage function is
used to create a fluid layout within the tab panel. Two columns are
defined: one for the goslingOutput and another for action buttons.
ui <- navbarPage(
title = "shiny.gosling",
tabPanel(
"Line Chart",
use_gosling(),
fluidPage(
width = 12,
fluidRow(
column(
width = 8,
goslingOutput("gosling_plot_test")
),
column(
width = 4,
fluidRow(
column(
2,
actionButton(
"download_png",
"PNG",
icon = icon("cloud-arrow-down")
)
),
column(
2,
actionButton(
"download_pdf",
"PDF",
icon = icon("cloud-arrow-down")
)
)
)
)
)
)
)
)
Shiny App server
The server logic is defined using the server function. The
observeEvent functions are used to respond to button clicks and user
interactions. Clicking the “PNG” or “PDF” buttons triggers the
respective download action for the visualization. The
output$gosling_plot_test function uses renderGosling to render the
composed view using the gosling function from the shiny.gosling
package.
server <- function(input, output, session) {
observeEvent(input$download_png, {
export_png(component_id = "sars_cov2")
})
observeEvent(input$download_pdf, {
export_pdf(component_id = "sars_cov2")
})
observeEvent(input$zoom_out, {
zoom_to_extent(
component_id = "sars_cov2",
view_id = "view2_track1"
)
})
output$gosling_plot_test <- renderGosling({
gosling(
component_id = "sars_cov2",
single_composed_views,
clean_braces = TRUE
)
})
}
shinyApp(ui, server)
Session Info
sessionInfo()
#> R Under development (unstable) (2024-10-21 r87258)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.21-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] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] sessioninfo_1.2.2 ggbio_1.55.0
#> [3] ggplot2_3.5.1 StructuralVariantAnnotation_1.23.0
#> [5] VariantAnnotation_1.53.0 Rsamtools_2.23.0
#> [7] Biostrings_2.75.0 XVector_0.47.0
#> [9] SummarizedExperiment_1.37.0 Biobase_2.67.0
#> [11] MatrixGenerics_1.19.0 matrixStats_1.4.1
#> [13] rtracklayer_1.67.0 GenomicRanges_1.59.0
#> [15] GenomeInfoDb_1.43.0 IRanges_2.41.0
#> [17] S4Vectors_0.45.0 BiocGenerics_0.53.0
#> [19] shiny_1.9.1 shiny.gosling_1.3.0
#>
#> loaded via a namespace (and not attached):
#> [1] RColorBrewer_1.1-3 rstudioapi_0.17.1 jsonlite_1.8.9
#> [4] magrittr_2.0.3 GenomicFeatures_1.59.0 rmarkdown_2.28
#> [7] fs_1.6.4 BiocIO_1.17.0 zlibbioc_1.53.0
#> [10] vctrs_0.6.5 memoise_2.0.1 RCurl_1.98-1.16
#> [13] base64enc_0.1-3 progress_1.2.3 htmltools_0.5.8.1
#> [16] S4Arrays_1.7.0 curl_5.2.3 SparseArray_1.7.0
#> [19] Formula_1.2-5 sass_0.4.9 bslib_0.8.0
#> [22] fontawesome_0.5.2 htmlwidgets_1.6.4 httr2_1.0.5
#> [25] plyr_1.8.9 cachem_1.1.0 GenomicAlignments_1.43.0
#> [28] shiny.react_0.4.0 mime_0.12 lifecycle_1.0.4
#> [31] pkgconfig_2.0.3 Matrix_1.7-1 R6_2.5.1
#> [34] fastmap_1.2.0 GenomeInfoDbData_1.2.13 digest_0.6.37
#> [37] colorspace_2.1-1 GGally_2.2.1 AnnotationDbi_1.69.0
#> [40] OrganismDbi_1.49.0 Hmisc_5.2-0 RSQLite_2.3.7
#> [43] filelock_1.0.3 fansi_1.0.6 httr_1.4.7
#> [46] abind_1.4-8 compiler_4.5.0 bit64_4.5.2
#> [49] withr_3.0.2 htmlTable_2.4.3 backports_1.5.0
#> [52] BiocParallel_1.41.0 DBI_1.2.3 ggstats_0.7.0
#> [55] biomaRt_2.63.0 rappdirs_0.3.3 DelayedArray_0.33.0
#> [58] rjson_0.2.23 tools_4.5.0 foreign_0.8-87
#> [61] httpuv_1.6.15 nnet_7.3-19 glue_1.8.0
#> [64] restfulr_0.0.15 promises_1.3.0 grid_4.5.0
#> [67] checkmate_2.3.2 cluster_2.1.6 reshape2_1.4.4
#> [70] generics_0.1.3 gtable_0.3.6 BSgenome_1.75.0
#> [73] tidyr_1.3.1 ensembldb_2.31.0 hms_1.1.3
#> [76] data.table_1.16.2 xml2_1.3.6 utf8_1.2.4
#> [79] pillar_1.9.0 stringr_1.5.1 later_1.3.2
#> [82] dplyr_1.1.4 BiocFileCache_2.15.0 lattice_0.22-6
#> [85] bit_4.5.0 biovizBase_1.55.0 RBGL_1.83.0
#> [88] tidyselect_1.2.1 knitr_1.48 gridExtra_2.3
#> [91] ProtGenerics_1.39.0 xfun_0.48 stringi_1.8.4
#> [94] UCSC.utils_1.3.0 lazyeval_0.2.2 yaml_2.3.10
#> [97] evaluate_1.0.1 codetools_0.2-20 tibble_3.2.1
#> [100] graph_1.85.0 BiocManager_1.30.25 cli_3.6.3
#> [103] rpart_4.1.23 xtable_1.8-4 munsell_0.5.1
#> [106] jquerylib_0.1.4 dichromat_2.0-0.1 Rcpp_1.0.13
#> [109] dbplyr_2.5.0 png_0.1-8 XML_3.99-0.17
#> [112] parallel_4.5.0 assertthat_0.2.1 blob_1.2.4
#> [115] prettyunits_1.2.0 AnnotationFilter_1.31.0 bitops_1.0-9
#> [118] txdbmaker_1.3.0 pwalign_1.3.0 scales_1.3.0
#> [121] purrr_1.0.2 crayon_1.5.3 rlang_1.1.4
#> [124] KEGGREST_1.47.0