This module provides an interactive principal components plot and an interactive heatmap with correlation of principal components with sample variables.
Usage
tm_g_pca(
label,
mae_name,
exclude_assays = character(),
pre_output = NULL,
post_output = NULL
)
ui_g_pca(id, datasets, mae_name, pre_output, post_output)
srv_g_pca(id, datasets, reporter, mae_name, exclude_assays)
sample_tm_g_pca()
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).- id
(
string
) the shiny module id.- datasets
(
Datasets
)
teal specific argument which is automatically passed to the UI and server functions, holding all the data sets provided in the app initialization.- reporter
(
Reporter
) object
Functions
ui_g_pca()
: sets up the user interface.srv_g_pca()
: sets up the server with reactive graph.sample_tm_g_pca()
: sample module function.
Examples
mae <- hermes::multi_assay_experiment
mae_data <- dataset("MAE", mae)
data <- teal_data(mae_data)
app <- init(
data = data,
modules = modules(
tm_g_pca(
label = "PCA plot",
mae_name = "MAE"
)
)
)
#> [INFO] 2022-10-17 14:23:54.2407 pid:2146 token:[] teal.modules.hermes Initializing tm_g_pca
if (interactive()) {
shinyApp(app$ui, app$server)
}
# Alternatively you can run the sample module with this function call:
if (interactive()) {
sample_tm_g_pca()
}