This function maps R objects to plotly.js,
an (MIT licensed) web-based interactive charting library. It provides
abstractions for doing common things (e.g. mapping data values to
fill colors (via color) or creating animations (via frame)) and sets
some different defaults to make the interface feel more 'R-like'
(i.e., closer to plot() and ggplot2::qplot()).
# S3 method for SpatialExperiment
plot_ly(
data = data.frame(),
...,
type = NULL,
name = NULL,
color = NULL,
colors = NULL,
alpha = NULL,
stroke = NULL,
strokes = NULL,
alpha_stroke = 1,
size = NULL,
sizes = c(10, 100),
span = NULL,
spans = c(1, 20),
symbol = NULL,
symbols = NULL,
linetype = NULL,
linetypes = NULL,
split = NULL,
frame = NULL,
width = NULL,
height = NULL,
source = "A"
)A data frame (optional) or crosstalk::SharedData object.
Arguments (i.e., attributes) passed along to the trace type.
See schema() for a list of acceptable attributes for a given trace type
(by going to traces -> type -> attributes). Note that attributes
provided at this level may override other arguments
(e.g. plot_ly(x = 1:10, y = 1:10, color = I("red"), marker = list(color = "blue"))).
A character string specifying the trace type (e.g. "scatter", "bar", "box", etc).
If specified, it always creates a trace, otherwise
Values mapped to the trace's name attribute. Since a trace can
only have one name, this argument acts very much like split in that it
creates one trace for every unique value.
Values mapped to relevant 'fill-color' attribute(s)
(e.g. fillcolor,
marker.color,
textfont.color, etc.).
The mapping from data values to color codes may be controlled using
colors and alpha, or avoided altogether via I() (e.g., color = I("red")).
Any color understood by grDevices::col2rgb() may be used in this way.
Either a colorbrewer2.org palette name (e.g. "YlOrRd" or "Blues"),
or a vector of colors to interpolate in hexadecimal "#RRGGBB" format,
or a color interpolation function like colorRamp().
A number between 0 and 1 specifying the alpha channel applied to color.
Defaults to 0.5 when mapping to fillcolor and 1 otherwise.
Similar to color, but values are mapped to relevant 'stroke-color' attribute(s)
(e.g., marker.line.color
and line.color
for filled polygons). If not specified, stroke inherits from color.
Similar to colors, but controls the stroke mapping.
Similar to alpha, but applied to stroke.
(Numeric) values mapped to relevant 'fill-size' attribute(s)
(e.g., marker.size,
textfont.size,
and error_x.width).
The mapping from data values to symbols may be controlled using
sizes, or avoided altogether via I() (e.g., size = I(30)).
A numeric vector of length 2 used to scale size to pixels.
(Numeric) values mapped to relevant 'stroke-size' attribute(s)
(e.g.,
marker.line.width,
line.width for filled polygons,
and error_x.thickness)
The mapping from data values to symbols may be controlled using
spans, or avoided altogether via I() (e.g., span = I(30)).
A numeric vector of length 2 used to scale span to pixels.
(Discrete) values mapped to marker.symbol.
The mapping from data values to symbols may be controlled using
symbols, or avoided altogether via I() (e.g., symbol = I("pentagon")).
Any pch value or symbol name may be used in this way.
A character vector of pch values or symbol names.
(Discrete) values mapped to line.dash.
The mapping from data values to symbols may be controlled using
linetypes, or avoided altogether via I() (e.g., linetype = I("dash")).
Any lty (see par) value or dash name may be used in this way.
A character vector of lty values or dash names
(Discrete) values used to create multiple traces (one trace per value).
(Discrete) values used to create animation frames.
Width in pixels (optional, defaults to automatic sizing).
Height in pixels (optional, defaults to automatic sizing).
a character string of length 1. Match the value of this string
with the source argument in event_data() to retrieve the
event data corresponding to a specific plot (shiny apps can have multiple plots).
plotly
Unless type is specified, this function just initiates a plotly
object with 'global' attributes that are passed onto downstream uses of
add_trace() (or similar). A formula must always be used when
referencing column name(s) in data (e.g. plot_ly(mtcars, x = ~wt)).
Formulas are optional when supplying values directly, but they do
help inform default axis/scale titles
(e.g., plot_ly(x = mtcars$wt) vs plot_ly(x = ~mtcars$wt))
For initializing a plotly-geo object: plot_geo()
For initializing a plotly-mapbox object: plot_mapbox()
For translating a ggplot2 object to a plotly object: ggplotly()
For modifying any plotly object: layout(), add_trace(), style()
For linked brushing: highlight()
For arranging multiple plots: subplot(), crosstalk::bscols()
For inspecting plotly objects: plotly_json()
For quick, accurate, and searchable plotly.js reference: schema()
example(read10xVisium)
#>
#> rd10xV> dir <- system.file(
#> rd10xV+ file.path("extdata", "10xVisium"),
#> rd10xV+ package = "SpatialExperiment")
#>
#> rd10xV> sample_ids <- c("section1", "section2")
#>
#> rd10xV> samples <- file.path(dir, sample_ids, "outs")
#>
#> rd10xV> list.files(samples[1])
#> [1] "raw_feature_bc_matrix" "spatial"
#>
#> rd10xV> list.files(file.path(samples[1], "spatial"))
#> [1] "scalefactors_json.json" "tissue_lowres_image.png"
#> [3] "tissue_positions_list.csv"
#>
#> rd10xV> file.path(samples[1], "raw_feature_bc_matrix")
#> [1] "/home/runner/work/_temp/Library/SpatialExperiment/extdata/10xVisium/section1/outs/raw_feature_bc_matrix"
#>
#> rd10xV> (spe <- read10xVisium(samples, sample_ids,
#> rd10xV+ type = "sparse", data = "raw",
#> rd10xV+ images = "lowres", load = FALSE))
#> # A SpatialExperiment-tibble abstraction: 99 × 7
#> # Features = 50 | Cells = 99 | Assays = counts
#> .cell in_tissue array_row array_col sample_id pxl_col_in_fullres
#> <chr> <lgl> <int> <int> <chr> <int>
#> 1 AAACAACGAATAGTTC-1 FALSE 0 16 section1 2312
#> 2 AAACAAGTATCTCCCA-1 TRUE 50 102 section1 8230
#> 3 AAACAATCTACTAGCA-1 TRUE 3 43 section1 4170
#> 4 AAACACCAATAACTGC-1 TRUE 59 19 section1 2519
#> 5 AAACAGAGCGACTCCT-1 TRUE 14 94 section1 7679
#> 6 AAACAGCTTTCAGAAG-1 FALSE 43 9 section1 1831
#> 7 AAACAGGGTCTATATT-1 FALSE 47 13 section1 2106
#> 8 AAACAGTGTTCCTGGG-1 FALSE 73 43 section1 4170
#> 9 AAACATGGTGAGAGGA-1 FALSE 62 0 section1 1212
#> 10 AAACATTTCCCGGATT-1 FALSE 61 97 section1 7886
#> # ℹ 89 more rows
#> # ℹ 1 more variable: pxl_row_in_fullres <int>
#>
#> rd10xV> # base directory 'outs/' from Space Ranger can also be omitted
#> rd10xV> samples2 <- file.path(dir, sample_ids)
#>
#> rd10xV> (spe2 <- read10xVisium(samples2, sample_ids,
#> rd10xV+ type = "sparse", data = "raw",
#> rd10xV+ images = "lowres", load = FALSE))
#> # A SpatialExperiment-tibble abstraction: 99 × 7
#> # Features = 50 | Cells = 99 | Assays = counts
#> .cell in_tissue array_row array_col sample_id pxl_col_in_fullres
#> <chr> <lgl> <int> <int> <chr> <int>
#> 1 AAACAACGAATAGTTC-1 FALSE 0 16 section1 2312
#> 2 AAACAAGTATCTCCCA-1 TRUE 50 102 section1 8230
#> 3 AAACAATCTACTAGCA-1 TRUE 3 43 section1 4170
#> 4 AAACACCAATAACTGC-1 TRUE 59 19 section1 2519
#> 5 AAACAGAGCGACTCCT-1 TRUE 14 94 section1 7679
#> 6 AAACAGCTTTCAGAAG-1 FALSE 43 9 section1 1831
#> 7 AAACAGGGTCTATATT-1 FALSE 47 13 section1 2106
#> 8 AAACAGTGTTCCTGGG-1 FALSE 73 43 section1 4170
#> 9 AAACATGGTGAGAGGA-1 FALSE 62 0 section1 1212
#> 10 AAACATTTCCCGGATT-1 FALSE 61 97 section1 7886
#> # ℹ 89 more rows
#> # ℹ 1 more variable: pxl_row_in_fullres <int>
#>
#> rd10xV> # tabulate number of spots mapped to tissue
#> rd10xV> cd <- colData(spe)
#>
#> rd10xV> table(
#> rd10xV+ in_tissue = cd$in_tissue,
#> rd10xV+ sample_id = cd$sample_id)
#> sample_id
#> in_tissue section1 section2
#> FALSE 28 27
#> TRUE 22 22
#>
#> rd10xV> # view available images
#> rd10xV> imgData(spe)
#> DataFrame with 2 rows and 4 columns
#> sample_id image_id data scaleFactor
#> <character> <character> <list> <numeric>
#> 1 section1 lowres #### 0.0510334
#> 2 section2 lowres #### 0.0510334
spe |>
plot_ly(x = ~ array_col, y = ~ array_row)