Using the GRanges object for a circos plot using shiny.gosling
Setup Tracks
Track 1
We will have 3 tracks in this circos plot. The outermost layer displays the width of a sequence as a bar plot in maroon.
In the track below, we first give it an id. In the
following line, we assign a title to it. This title will be displayed at
the top of the rings. Next, we prepare and pass the data with
track_data_gr, which takes the GRanges object, the
chromosome field names and the genomic field names. We set the mark to
bar as we need a simple bar plot here. We define x and y
via visual_channel_x and visual_channel_y.
Finally, we set the color of the bar plot and the width &
height.
track1 <- add_single_track(
id = "track1",
title = "Quality",
data = track_data_gr(
gr_circos, chromosomeField = "seqnames",
genomicFields = c("start", "end")
),
mark = "bar",
x = visual_channel_x(field = "start", type = "genomic", axis = "bottom"),
y = visual_channel_y(field = "QUAL", type = "quantitative", axis = "right"),
color = visual_channel_color(
value = "#BB3114"
),
width = 700,
height = 100
)Track 2
The middle layer plots the start of the sequence to its end.
In this layer, we begin similarly by setting the id and the title of
the layer. Next we pass the same data object as our data will not
change. But other data can be used here. We just need to pass the
appropriate chromosomeField and genomicFields.
We set this as mark = "rect" as we want to fill the ring
with a color if data is a certain value. These are called ideograms. We
set the strokeWidth to 0.5. We set the stroke itself to
have different colors based on the value in the field
argument. Since “REF” is passed as field, and it has 4 values: A, C, G
and T, we set the domain and range accordingly. A corresponds to color
#73A97D and so on. Finally, we set the x and
xe. Since this is a rect ( rectangle ) plot,
it only has x and xe and no
y.
track2 <- add_single_track(
id = "track2",
title = "REF",
data = track_data_gr(
gr_circos, chromosomeField = "seqnames",
genomicFields = c("start", "end")
),
mark = "rect",
strokeWidth = visual_channel_stroke_width(
value = 0.5
),
stroke = visual_channel_stroke(
field = "REF",
type = "nominal",
domain = list("A", "C", "G", "T"),
range = list("#73A97D", "#C1BB78", "#1F5E89", "#CF784B")
),
x = visual_channel_x(field = "start", type = "genomic"),
xe = visual_channel_x(field = "end", type = "genomic"),
width = 700,
height = 100
)Track 3
Finally, the innermost layer shows the changes in sequences.
For the last layer, we start off again by setting the id and the
title. We set the data with track_data_gr but we now set
the chromosomeField and genomicFields to
to.gr columns. We also set longToWideId. This
will spread the data based on event column. So the new data
will have double the number of columns and will have a new column for
every existing column with the suffix _2. So
to.gr.start will have a corresponding column
to.gr.start_2 added to the DB. This is essentially widening
the dataset. We set alignment to overlay, as we want the tracks to be
plotted on top of each other. We then add 2 sub-tracks to this track.
The sub-tracks inherits all the data from the parent track. These tracks
are set with mark = "withinLink" so that the 2 tracks are
linked with each other. And we set x, xe,
x1 and x1e to refer to the correct columns in
the data. The first sub-track traces all variations that are not a part
of ch10 and the second sub-track traces the ones that are a
part of ch10. The first sub-track will trace on gray color
and the second sub-track will trace in various colors based on its
sequence name. In the end we also specify the width, height and the
stroke width.
track3 <- add_single_track(
id = "track3",
title = "Highlight similarities of Chr 10 with others",
data = track_data_gr(
gr_circos, chromosomeField = "to.gr.seqnames",
genomicFields = c("to.gr.start", "to.gr.end"),
longToWideId = "event"
),
alignment = "overlay",
opacity = visual_channel_opacity(
value = 0.4
),
tracks = add_multi_tracks(
add_single_track(
dataTransform = track_data_transform(
type = "filter", field = "to.gr.seqnames", oneOf = list("10"), not = TRUE
),
mark = "withinLink",
x = visual_channel_x(
field = "to.gr.start", type = "genomic"
),
xe = visual_channel_x(
field = "to.gr.start_2", type = "genomic"
),
x1 = visual_channel_x(
field = "to.gr.end", type = "genomic"
),
x1e = visual_channel_x(
field = "to.gr.end_2", type = "genomic"
),
stroke = visual_channel_stroke(
value = "lightgray"
),
strokeWidth = visual_channel_stroke_width(
value = 1
)
),
add_single_track(
dataTransform = track_data_transform(
type = "filter", field = "to.gr.seqnames", oneOf = list("10")
),
mark = "withinLink",
x = visual_channel_x(
field = "to.gr.start", type = "genomic"
),
xe = visual_channel_x(
field = "to.gr.start_2", type = "genomic"
),
x1 = visual_channel_x(
field = "to.gr.end", type = "genomic"
),
x1e = visual_channel_x(
field = "to.gr.end_2", type = "genomic"
),
stroke = visual_channel_stroke(
field = "to.gr.seqnames_2",
type = "nominal",
range = c(
"#E79F00", "#029F73", "#0072B2", "#CB7AA7", "#D45E00",
"#57B4E9", "#EFE441"
)
),
strokeWidth = visual_channel_stroke_width(
value = 1.5
)
)
),
width = 700,
height = 200
)Final View
Finally we put all the 3 main tracks together along with some parameters to control the layout style, the alignment and so on.
Run App
We put all of this in a shiny app.
ui <- fluidPage(
use_gosling(),
fluidRow(
column(6, goslingOutput("gosling_plot")),
column(
1, br(), actionButton(
"download_pdf",
"PDF",
icon = icon("cloud-arrow-down")
)
)
)
)
server <- function(input, output, session) {
output$gosling_plot <- renderGosling({
gosling(
component_id = "component_1",
single_composed_track
)
})
observeEvent(input$download_pdf, {
export_pdf(component_id = "component_1")
})
}
shinyApp(ui, server)