This is an example of how to create an interactive genomic
visualization using the shiny.gosling package within a Shiny app. It
leverages the shiny package for creating the user interface and
interactivity.
We create a Shiny app that visualizes genomic data using the
shiny.gosling package. It generates an interactive visualization with
tracks displaying DNA base counts and annotations, allowing users to
explore genomic data related to the SARS-CoV-2 virus.
Fetching Data
Below, we use the track_data() function to fetch data from the
specified URL. The data represents base counts for the SARS-CoV-2 virus
genome, organized into rows and columns. It includes attributes like
base, position, count, and categories (A, T, G, C).
“Multivec” is a term used in genomics to refer to a specific type of
data format used for representing and visualizing multi-dimensional
numerical data across genomic coordinates. It’s commonly used for
representing data like ChIP-seq, ATAC-seq, Hi-C, and other genomic
experiments where signals or measurements are collected at various
genomic positions.
Multivec data is essentially a matrix where rows correspond to
different genomic positions or regions, and columns correspond to
different samples or experiments. Each entry in the matrix represents a
value associated with a specific genomic position and sample. The
genomic positions along the rows of the matrix are usually represented
as chromosomal coordinates (chromosome name and base pair position).
This allows the data to be aligned with the genome, enabling accurate
visualization and analysis. There are different tools and file formats
that support multivec data, allowing researchers to work with and
visualize this type of data. The bigWig and bedGraph formats are
commonly used for representing multivec data. Visualization tools and
libraries like the UCSC Genome Browser, IGV (Integrative Genomics
Viewer), and libraries like “shiny.gosling” can render multivec data
visualizations.
Here are some resources and links where you can learn more about
multivec data and how it’s used in genomics research:
UCSC Genome Browser:
The UCSC Genome Browser is a widely used tool for visualizing genomic
data, including multivec data. Tutorial on
visualizing multivec data in the UCSC Genome Browser
IGV (Integrative Genomics Viewer):
IGV is another popular genome visualization tool that supports
multivec data. Tutorial
on loading and visualizing multivec data in IGV
BedGraph and BigWig Formats:
These are common file formats used for representing multivec data. Explanation
of the BedGraph format Explanation
of the BigWig format
track1_data <- track_data(
url = "https://server.gosling-lang.org/api/v1/tileset_info/?d=NC_045512_2-multivec",
type = "multivec",
row = "base",
column = "position",
value = "count",
categories = c("A", "T", "G", "C"),
start = "start",
end = "end"
)
Creating Tracks
Here, we define two tracks (track1 and track2) that will be displayed
in the visualization. track1 displays the count of DNA bases using a bar
mark, and track2 displays text annotations for certain conditions.
track1 <- add_single_track(
mark = "bar",
y = visual_channel_y(
field = "count", type = "quantitative", axis = "none"
)
)
track2 <- add_single_track(
dataTransform = track_data_transform(
type = "filter",
field = "count",
oneOf = list(0),
not = TRUE
),
mark = "text",
x = visual_channel_x(
field = "start", type = "genomic"
),
xe = visual_channel_x(
field = "end", type = "genomic"
),
size = 24,
color = "white",
visibility = list(list(
operation = "less-than",
measure = "width",
threshold = "|xe-x|",
transitionPadding = 30,
target = "mark"
),
list(
operation = "LT",
measure = "zoomLevel",
threshold = 40,
target = "track"
))
)
Defining Visual Channels
Now, lets define visual channels for track1. track1_x specifies the
genomic position on the x-axis, track1_color assigns colors based on DNA
bases, and track1_text specifies text annotations based on DNA
bases.
track1_x <- visual_channel_x(
field = "position", type = "genomic"
)
track1_color <- visual_channel_color(
field = "base",
type = "nominal",
domain = c("A", "T", "G", "C"),
legend = TRUE
)
track1_text <- visual_channel_text(
field = "base", type = "nominal"
)
track1_style <- default_track_styles(
inlineLegend = TRUE
)
Creating combined track
This code chunk combines the previously defined tracks (track1 and
track2) into a single track (track3) and specifies various properties
such as title, alignment, data, visual channels, and style.
track3 <- add_single_track(
title = "NC_045512.2 Sequence",
alignment = "overlay",
data = track1_data,
tracks = add_multi_tracks(
track1, track2
),
x = track1_x,
color = track1_color,
text = track1_text,
style = track1_style,
width = 800, height = 40
)
Creating the view
Lets create a view (view1) that contains the combined track (track3).
It specifies properties like multi-view mode, x-axis domain, alignment,
and linking.
view1 <- compose_view(
multi = TRUE,
centerRadius = 0,
xDomain = list(interval = c(1, 29903)),
linkingId = "detail",
alignment = "stack",
tracks = add_multi_tracks(
track3
)
)
Arranging the view
Next, we arrange views using the arrange_views function. It sets the
title, subtitle, assembly information, layout, spacing, and includes the
previously defined view1.
combined_view <- arrange_views(
title = "SARS-CoV-2",
subtitle = "Data Source: WashU Virus Genome Browser, NCBI, GISAID",
assembly = list(list("NC_045512.2", 29903)),
layout = "linear",
spacing = 50,
views = list(view1),
listify = FALSE
)
Shiny App
Finally, we define the Shiny user interface (UI) using the fluidPage
function. It includes the goslingOutput function to create a placeholder
for the visualization. We also define the Shiny server logic. It uses
the renderGosling function to render the interactive visualization using
the combined_view defined earlier.
ui <- fluidPage(
use_gosling(),
fluidRow(
column(6, goslingOutput("gosling_plot"))
)
)
server <- function(input, output, session) {
output$gosling_plot <- renderGosling({
gosling(
component_id = "sars_cov2",
combined_view
)
})
}
shinyApp(ui, server)
