Usage
tm_g_volcanoplot(
label,
mae_name,
exclude_assays = character(),
pre_output = NULL,
post_output = NULL,
.test = FALSE
)
ui_g_volcanoplot(id, mae_name, pre_output, post_output, .test = FALSE)
srv_g_volcanoplot(
id,
data,
filter_panel_api,
reporter,
mae_name,
exclude_assays,
.test = FALSE
)
sample_tm_g_volcanoplot(.test = FALSE)
Arguments
- label
(
string
)
menu item label of the module in the teal app.- mae_name
(
string
)
name of the MAE data used in the teal module.- exclude_assays
(
character
)
names of the assays which should not be included in choices in the teal module.- pre_output
(
shiny.tag
orNULL
)
placed before the output to put the output into context (for example a title).- post_output
(
shiny.tag
orNULL
)
placed after the output to put the output into context (for example theshiny::helpText()
elements can be useful).- .test
(
flag
)
whether to display the internal structure of the plot for testing purposes.- id
(
string
) the shiny module id.- data
(
reactive
)reactive(<teal_data>)
holding all the data sets provided during app initialization after going through the filters.- filter_panel_api
(
FilterPanelAPI
)
object describing the actual filter panel API.- reporter
(
Reporter
) object
Functions
ui_g_volcanoplot()
: sets up the user interface.srv_g_volcanoplot()
: sets up the server with reactive graph.sample_tm_g_volcanoplot()
: sample module function.
Examples
data <- teal_data(MAE = hermes::multi_assay_experiment)
app <- init(
data = data,
modules = modules(
tm_g_volcanoplot(
label = "volcanoplot",
mae_name = "MAE"
)
)
)
#> Initializing tm_g_volcanoplot
if (interactive()) {
shinyApp(app$ui, app$server)
}
# Alternatively you can run the sample module with this function call:
if (interactive()) {
sample_tm_g_volcanoplot()
}