Package 'teal.modules.hermes'

Description

teal.modules.hermes provides RNA-seq analysis modules to add to a teal application.

Author(s)

Maintainer: Dony Unardi [email protected]

Authors:

• Daniel Sabanés Bové

• Nikolas Burkoff

• Dinakar Kulkarni

• Konrad Pagacz

• Namrata Bhatia

• Dawid Kaledkowski

• Pawel Rucki

• Jeff Luong

• Stefanie Bienert

• Haocheng Li

• Lyndsee Midori Zhang

Other contributors:

• Sorin Voicu [contributor]

• Benoit Falquet [contributor]

• Mahmoud Hallal [contributor]

• Tim Treis [contributor]

• Vedha Viyash [contributor]

• F. Hoffmann-La Roche AG [copyright holder, funder]

See Also

Useful links:

• https://insightsengineering.github.io/teal.modules.hermes/

• https://github.com/insightsengineering/teal.modules.hermes/

• Report bugs at https://github.com/insightsengineering/teal.modules.hermes/issues

Description

This defines the input for the ADTTE specification.

Usage

adtteSpecInput(inputId, label_paramcd = "Select Endpoint")

Arguments

Value

The UI part.

See Also

adtteSpecServer() for the module server and a complete example.

Description

This defines the server part for the ADTTE specification. The resulting data set binned_adtte_subset contains the subset of ADTTE selected by the time-to-event endpoint, joined together with the gene information extracted from specified assay and experiment, as numeric and factor columns. The factor column is created by binning the numeric column according to the quantile cutoffs specified in probs.

Usage

adtteSpecServer(
  id,
  data,
  mae_name,
  adtte_name,
  adtte_vars,
  experiment_data,
  experiment_name,
  assay,
  genes,
  probs
)

Arguments

Value

List with the following elements:

• binned_adtte_subset: reactive containing the joined ADTTE and gene data.

• gene_col: reactive containing the string with the column name of the original numeric gene variable.

• gene_factor: string with the variable name for the binned gene data.

• time_unit: reactive string with the time unit for the current subset.

See Also

adtteSpecInput() for the module UI.

Examples

library(dplyr)

ui <- function(id) {
  ns <- NS(id)

  teal.widgets::standard_layout(
    encoding = uiOutput(ns("encoding_ui")),
    output = verbatimTextOutput(ns("summary"))
  )
}

server <- function(id, data, filter_panel_api) {
  checkmate::assert_class(data, "reactive")
  checkmate::assert_class(shiny::isolate(data()), "teal_data")
  moduleServer(id, function(input, output, session) {
    output$encoding_ui <- renderUI({
      tags$div(
        experimentSpecInput(session$ns("experiment"), data, mae_name = "MAE"),
        assaySpecInput(session$ns("assay")),
        geneSpecInput(session$ns("genes"), funs = list(Mean = colMeans)),
        adtteSpecInput(session$ns("adtte"))
      )
    })
    experiment <- experimentSpecServer(
      "experiment",
      data = data,
      filter_panel_api = filter_panel_api,
      mae_name = "MAE"
    )
    assay <- assaySpecServer(
      "assay",
      assays = experiment$assays
    )
    genes <- geneSpecServer(
      "genes",
      funs = list(Mean = colMeans),
      gene_choices = experiment$genes
    )
    adtte <- adtteSpecServer(
      "adtte",
      data = data,
      adtte_name = "ADTTE",
      mae_name = "MAE",
      adtte_vars = list(
        aval = "AVAL",
        avalu = "AVALU",
        is_event = "is_event",
        paramcd = "PARAMCD",
        usubjid = "USUBJID"
      ),
      experiment_data = experiment$data,
      experiment_name = experiment$name,
      assay = assay,
      genes = genes,
      probs = reactive({
        0.5
      })
    )
    output$summary <- renderPrint({
      binned_adtte_subset <- adtte$binned_adtte_subset()
      summary(binned_adtte_subset)
    })
  })
}

my_app <- function() {
  data <- teal_data()
  data <- within(data, {
    ADSL <- teal.data::rADSL
    ADTTE <- teal.data::rADTTE %>%
      dplyr::mutate(is_event = .data$CNSR == 0)
    MAE <- hermes::multi_assay_experiment
  })
  join_keys(data) <- default_cdisc_join_keys[c("ADSL", "ADTTE", "MAE")]

  app <- init(
    data = data,
    modules = modules(
      module(
        label = "adtteSpec example",
        server = server,
        ui = ui,
        datanames = "all"
      )
    )
  )
  shinyApp(app$ui, app$server)
}

if (interactive()) {
  my_app()
}

Description

This defines the input for the assay specification.

Usage

assaySpecInput(inputId, label_assays = "Select Assay")

Arguments

Value

The UI part.

See Also

assaySpecServer() for the module server and a complete example.

Description

This defines the server part for the assay specification.

Usage

assaySpecServer(id, assays, exclude_assays = character())

Arguments

Value

The chosen assay as a reactive string.

See Also

assaySpecInput() for the module UI.

Examples

ui <- function(id) {
  ns <- NS(id)
  teal.widgets::standard_layout(
    encoding = uiOutput(ns("encoding_ui")),
    output = textOutput(ns("result"))
  )
}

server <- function(id, data, filter_panel_api) {
  moduleServer(id, module = function(input, output, session) {
    output$encoding_ui <- renderUI({
      tags$div(
        experimentSpecInput(session$ns("experiment"), data, "MAE"),
        assaySpecInput(
          session$ns("assay"),
          label_assays = "Please choose assay"
        )
      )
    })
    experiment <- experimentSpecServer(
      id = "experiment",
      data = data,
      filter_panel_api = filter_panel_api,
      mae_name = "MAE"
    )
    assay <- assaySpecServer(
      "assay",
      experiment$assays,
      exclude_assays = c("counts", "cpm", "tpm", "bla")
    )
    output$result <- renderPrint({
      assay()
    })
  })
}

my_app <- function() {
  data <- teal_data(MAE = hermes::multi_assay_experiment)
  app <- init(
    data = data,
    modules = modules(
      module(
        label = "assaySpec example",
        server = server,
        ui = ui,
        datanames = "all"
      )
    )
  )
  shinyApp(app$ui, app$server)
}
if (interactive()) {
  my_app()
}

Description

Check whether x is a list of ADTTE variables.

Usage

assert_adtte_vars(x)

Arguments

See Also

assertions for more details.

Examples

assert_adtte_vars(list(aval = "AV", is_event = "EV", paramcd = "PC", usubjid = "ID", avalu = "u"))

Description

Check whether x is a list of summary functions.

Usage

assert_summary_funs(x, null.ok = FALSE)

Arguments

See Also

assertions for more details.

Examples

assert_summary_funs(list(mean = colMeans, raw = NULL), null.ok = TRUE)

Description

Check whether x is a reactive input.

Usage

check_reactive(x)

assert_reactive(x, .var.name = checkmate::vname(x), add = NULL)

test_reactive(x)

Arguments

See Also

assertions for more details.

Examples

check_reactive("bla")
check_reactive(reactive("bla"))

Description

Check whether x is a shiny tag.

Usage

check_tag(x, null.ok = FALSE)

assert_tag(x, null.ok = FALSE, .var.name = checkmate::vname(x), add = NULL)

test_tag(x, null.ok = FALSE)

expect_tag(x, null.ok = FALSE, info = NULL, label = vname(x))

Arguments

See Also

assertions for more details.

Examples

check_tag("bla")
check_tag(NULL, null.ok = TRUE)

Description

This defines the input for the experiment specification.

Usage

experimentSpecInput(
  inputId,
  data,
  mae_name,
  label_experiments = "Select Experiment"
)

Arguments

Value

The UI part.

See Also

experimentSpecServer() for the module server and a complete example.

Description

This defines the server part for the experiment specification.

Usage

experimentSpecServer(
  id,
  data,
  filter_panel_api,
  mae_name,
  name_annotation = "symbol",
  sample_vars_as_factors = TRUE,
  with_mae_col_data = TRUE
)

Arguments

Value

List with the following reactive objects:

• data: the hermes::AnyHermesData experiment.

• name: the name of the experiment as selected by the user.

• genes: a data.frame with the genes in data, with columns id and name.

• assays: the names of the assays in data.

See Also

experimentSpecInput() for the module UI.

Examples

ui <- function(id,
               mae_name) {
  ns <- NS(id)
  teal.widgets::standard_layout(
    encoding = uiOutput(ns("encoding_ui")),
    output = tags$div(
      verbatimTextOutput(ns("summary")),
      verbatimTextOutput(ns("head"))
    )
  )
}

server <- function(id,
                   data,
                   filter_panel_api,
                   mae_name) {
  moduleServer(id, function(input, output, session) {
    output$encoding_ui <- renderUI({
      tags$div(
        experimentSpecInput(
          session$ns("my_experiment"),
          data,
          mae_name,
          label_experiments = "Please choose experiment"
        ),
        selectInput(
          session$ns("property"),
          "Please choose property",
          c("data", "name", "genes", "assays")
        )
      )
    })
    experiment <- experimentSpecServer(
      "my_experiment",
      data,
      filter_panel_api,
      mae_name
    )
    result <- reactive({
      req(input$property)
      switch(input$property,
        data = experiment$data(),
        name = experiment$name(),
        genes = experiment$genes(),
        assays = experiment$assays()
      )
    })
    output$summary <- renderPrint({
      result <- result()
      hermes::summary(result)
    })
    output$head <- renderPrint({
      result <- result()
      utils::head(result)
    })
  })
}

my_app <- function() {
  data <- teal_data(MAE = hermes::multi_assay_experiment)
  app <- init(
    data = data,
    modules = modules(
      module(
        label = "experimentSpec example",
        server = server,
        server_args = list(mae_name = "MAE"),
        ui = ui,
        ui_args = list(mae_name = "MAE"),
        datanames = "all"
      )
    )
  )
  shinyApp(app$ui, app$server)
}
if (interactive()) {
  my_app()
}

Description

This defines the input for the gene signature specification.

Usage

geneSpecInput(
  inputId,
  funs,
  label_genes = "Select Gene(s)",
  label_funs = "Select Gene Summary",
  label_text_button = "Enter list of genes",
  label_lock_button =
    "Lock gene selection (so that it does not get updated when filtering)",
  label_select_all_button = paste0("Select All Genes (first ", max_options, ")"),
  label_select_none_button = "Select None",
  max_options = 200L,
  max_selected = max_options
)

Arguments

Value

The UI part.

See Also

geneSpecServer() for the module server and a complete example.

Examples

geneSpecInput("my_genes", list(mean = colMeans), label_funs = "Please select function")

Description

This defines the server part for the gene signature specification.

Usage

geneSpecServer(
  id,
  funs,
  gene_choices,
  label_modal_title = "Enter list of genes",
  label_modal_footer = c("Please enter a comma-separated list of gene IDs and/or names.",
    "(Note that genes not included in current choices will be removed)")
)

Arguments

Value

Reactive hermes::GeneSpec which can be used as input for the relevant hermes functions.

See Also

geneSpecInput() for the module UI.

Examples

ui <- function(id, funs) {
  ns <- NS(id)
  teal.widgets::standard_layout(
    encoding = tags$div(
      geneSpecInput(
        ns("my_genes"),
        funs = funs,
        label_funs = "Please select function"
      )
    ),
    output = textOutput(ns("result"))
  )
}
server <- function(id,
                   data,
                   funs) {
  checkmate::assert_class(data, "reactive")
  checkmate::assert_class(shiny::isolate(data()), "teal_data")
  moduleServer(id, function(input, output, session) {
    gene_choices <- reactive({
      mae <- data()[["MAE"]]
      object <- mae[[1]]
      gene_ids <- rownames(object)
      gene_names <- SummarizedExperiment::rowData(object)$symbol
      gene_data <- data.frame(
        id = gene_ids,
        name = gene_names
      )
      gene_data[order(gene_data$name), ]
    })
    gene_spec <- geneSpecServer(
      "my_genes",
      funs = funs,
      gene_choices = gene_choices
    )
    output$result <- renderText({
      validate_gene_spec(
        gene_spec(),
        gene_choices()$id
      )
      gene_spec <- gene_spec()
      gene_spec$get_label()
    })
  })
}
funs <- list(mean = colMeans)
my_app <- function() {
  data <- teal_data(MAE = hermes::multi_assay_experiment)
  app <- init(
    data = data,
    modules = modules(
      module(
        label = "GeneSpec example",
        server = server,
        server_args = list(funs = funs),
        ui = ui,
        ui_args = list(funs = funs),
        datanames = "all"
      )
    )
  )
  shinyApp(app$ui, app$server)
}
if (interactive()) {
  my_app()
}

Description

This helper function takes an assignment list and converts it to a group list.

Usage

h_assign_to_group_list(x)

Arguments

Value

A combination list.

Examples

assign_list <- list(
  "ASIAN" = "1",
  "BLACK OR AFRICAN AMERICAN" = "1",
  "MULTIPLE" = "2",
  "UNKNOWN" = "2",
  "WHITE" = "4"
)
objective_list <- list(
  "ASIAN/BLACK OR AFRICAN AMERICAN" = c("ASIAN", "BLACK OR AFRICAN AMERICAN"),
  "MULTIPLE/UNKNOWN" = c("MULTIPLE", "UNKNOWN"),
  "WHITE" = "WHITE"
)
result_list <- h_assign_to_group_list(assign_list)
stopifnot(identical(result_list, objective_list))

Description

Given a group list and a factor, this helper function collapses the levels in the factor accordingly and also ensures that the resulting levels are in the order given in the group list.

Usage

h_collapse_levels(x, group_list)

Arguments

Value

The transformed factor x with new levels.

Examples

set.seed(123)
x <- factor(sample(
  c("ASIAN", "BLACK OR AFRICAN AMERICAN", "MULTIPLE", "UNKNOWN", "WHITE"),
  size = 30L,
  replace = TRUE
))
group_list <- list(
  "ASIAN/BLACK OR AFRICAN AMERICAN" = c("ASIAN", "BLACK OR AFRICAN AMERICAN"),
  "MULTIPLE/UNKNOWN" = c("MULTIPLE", "UNKNOWN"),
  "WHITE" = "WHITE"
)
x_collapsed <- h_collapse_levels(x, group_list)
stopifnot(identical(levels(x_collapsed), names(group_list)))

Description

This helper function extracts words from a string. Here words are defined as containing lower or upper case letters, colons and dots. All other characters are considered separators.

Usage

h_extract_words(x)

Arguments

Value

Character vector with the extracted words.

Examples

h_extract_words("a, b, , c, 234; 34562 - GeneID:bla")
h_extract_words("GeneID:1820, sdf.393; 32596")

Description

Given a hermes::AnyHermesData data object, as well as the annotation column name to use as gene name, this function formats the contained genes as a data.frame ready for consumption in h_order_genes() e.g.

Usage

h_gene_data(object, name_annotation)

Arguments

Details

Note that missing names or names that only contain whitespace are replaced by empty strings for consistency and better labeling in the UI downstream

Value

A data.frame with id and name columns containing all genes from object.

Examples

object <- hermes::hermes_data[1:10, ]
h_gene_data(object, "symbol")

Description

A function to help with merging of MAE to ADTTE.

Usage

h_km_mae_to_adtte(
  adtte,
  mae,
  genes,
  experiment_name = "hd1",
  assay_name = "counts",
  usubjid_var = "USUBJID"
)

Arguments

Value

A data frame containing all columns/rows from adtte that match by subject ID with the row names of the MAE and have the gene samples available in the given experiment. The attribute gene_cols contains the column names for the gene columns.

Note

The final gene column names can start with a different string than the original gene IDs (or labels), in particular white space and colons are removed.

Examples

library(dplyr)
mae <- hermes::multi_assay_experiment
adtte <- teal.data::rADTTE %>%
  dplyr::mutate(CNSR = as.logical(CNSR))

new_adtte <- h_km_mae_to_adtte(
  adtte,
  mae,
  genes = hermes::gene_spec("GeneID:1820"),
  experiment_name = "hd2"
)
new_adtte2 <- h_km_mae_to_adtte(
  adtte,
  mae,
  genes = hermes::gene_spec(c("GeneID:1820", "GeneID:94115"), fun = colMeans),
  experiment_name = "hd2"
)
new_adtte3 <- h_km_mae_to_adtte(
  adtte,
  mae,
  genes = hermes::gene_spec(c(A = "GeneID:1820", B = "GeneID:94115")),
  experiment_name = "hd2"
)

Description

The possible gene choices are ordered as follows. First come all genes which have a non-empty name, ordered by their name alphabetically. Last come all genes with an empty name, ordered by their ID alphabetically.

Usage

h_order_genes(genes)

Arguments

Value

The ordered data.frame.

Examples

genes <- data.frame(
  id = c("7", "1", "2", "345346", "0"),
  name = c("e", "", "c", "", "a")
)
h_order_genes(genes)

Description

This helper function takes a vector of words and tries to match them with the id and name columns of possible gene choices.

Usage

h_parse_genes(words, choices)

Arguments

Value

The subset of choices which matches words in ID or name.

Examples

h_parse_genes(
  c("a", "2535"),
  data.frame(id = as.character(2533:2537), name = letters[1:5])
)

Description

This helper function takes the intersection of selected and choices for genes and updates the inputId accordingly. It then shows a notification if not all selected genes were available.

Usage

h_update_gene_selection(session, inputId, selected, choices)

Arguments

Description

This function plots the correlation heatmap.

Usage

heatmap_plot(object, assay_name)

Arguments

Value

Plot to be displayed in the teal app.

Examples

library(hermes)
object <- HermesData(summarized_experiment)
result <- heatmap_plot(object, assay_name = "counts")

Description

This predicate function is helpful for functions where arguments could not yet be initialized from the teal module.

Usage

is_blank(x)

Arguments

Value

Flag whether x is identical to an empty string, i.e. "".

Examples

is_blank("")
is_blank(" ")

Description

When multiple sample variables are used in a given module, then this wrapper makes it much easier to specify in the server function.

Usage

multiSampleVarSpecServer(inputIds, original_data, ...)

Arguments

Value

List with the final transformed experiment_data reactive and a list vars which contains the selected sample variables as reactives under their input ID.

Examples

## Not run: 
# In the server use:
sample_var_specs <- multiSampleVarSpecServer(
  inputIds = c("facet_var", "color_var"),
  experiment_name = reactive({
    input$experiment_name
  }),
  original_data = ori_data # nolint Please update the <ori_data>
)
# Then can extract the transformed data and selected variables later:
experiment_data <- sample_var_specs$experiment_data()
facet_var <- sample_var_specs$vars$facet_var()
color_var <- sample_var_specs$vars$color_var()

## End(Not run)

Description

This defines the input for the sample variable specification.

Usage

sampleVarSpecInput(
  inputId,
  label_vars = "Select sample variable",
  label_levels_button = "Combine factor levels"
)

Arguments

Value

The UI part.

See Also

sampleVarSpecServer() for the module server and a complete example.

Examples

sampleVarSpecInput("my_vars", label_vars = "Select faceting variable")

Description

This defines the server part for the sample variable specification.

Usage

sampleVarSpecServer(
  id,
  experiment_name,
  original_data,
  transformed_data = original_data,
  assign_lists = reactiveValues(),
  num_levels = NULL,
  categorical_only = !is.null(num_levels),
  explicit_na = FALSE,
  label_modal_title = "Please click to group the original factor levels"
)

Arguments

Value

Reactive SummarizedExperiment::SummarizedExperiment which can be used as input for the relevant hermes functions.

Note

Only atomic columns (e.g. not DataFrame columns) of the colData which are not completely missing (NA) will be shown for selection. If num_levels is specified then only factor columns will be available.

See Also

sampleVarSpecInput() for the module UI.

Examples

ui <- function(id) {
  checkmate::assert_class(data, "teal_data")
  ns <- NS(id)

  teal.widgets::standard_layout(
    encoding = uiOutput(ns("encoding_ui")),
    output = plotOutput(ns("plot"))
  )
}
server <- function(id,
                   data) {
  checkmate::assert_class(data, "reactive")
  checkmate::assert_class(shiny::isolate(data()), "teal_data")
  moduleServer(id, function(input, output, session) {
    output$encoding_ui <- renderUI({
      mae <- data()[["MAE"]]
      experiment_name_choices <- names(mae)
      tags$div(
        selectInput(session$ns("experiment_name"), "Select experiment", experiment_name_choices),
        sampleVarSpecInput(session$ns("facet_var"), "Select faceting variable")
      )
    })
    experiment_data <- reactive({
      req(input$experiment_name)
      mae <- data()[["MAE"]]
      object <- mae[[input$experiment_name]]
      SummarizedExperiment::colData(object) <-
        hermes::df_cols_to_factor(SummarizedExperiment::colData(object))
      object
    })
    facet_var_spec <- sampleVarSpecServer(
      "facet_var",
      experiment_name = reactive({
        input$experiment_name
      }),
      original_data = experiment_data
    )
    output$plot <- renderPlot({
      experiment_data_final <- facet_var_spec$experiment_data()
      facet_var <- facet_var_spec$sample_var()
      hermes::draw_boxplot(
        experiment_data_final,
        assay_name = "counts",
        genes = hermes::gene_spec(hermes::genes(experiment_data_final)[1]),
        facet_var = facet_var
      )
    })
  })
}
my_app <- function() {
  data <- teal_data(MAE = hermes::multi_assay_experiment)
  app <- init(
    data = data,
    modules = modules(
      module(
        label = "sampleVarSpec example",
        server = server,
        ui = ui,
        datanames = "all"
      )
    )
  )
  shinyApp(app$ui, app$server)
}
if (interactive()) {
  my_app()
}

Description

This module provides an interactive barplot for RNA-seq gene expression analysis.

Usage

tm_g_barplot(
  label,
  mae_name,
  exclude_assays = character(),
  summary_funs = list(Mean = colMeans, Median = matrixStats::colMedians, Max =
    matrixStats::colMaxs),
  pre_output = NULL,
  post_output = NULL,
  .test = FALSE,
  transformators = list()
)

ui_g_barplot(
  id,
  mae_name,
  summary_funs,
  pre_output,
  post_output,
  .test = FALSE
)

srv_g_barplot(
  id,
  data,
  filter_panel_api,
  reporter,
  mae_name,
  exclude_assays,
  summary_funs,
  .test = FALSE
)

sample_tm_g_barplot(.test = FALSE)

Arguments

Value

Shiny module to be used in the teal app.

Functions

• ui_g_barplot(): sets up the user interface.

• srv_g_barplot(): sets up the server with reactive graph.

• sample_tm_g_barplot(): sample module function.

Examples

data <- teal_data(MAE = hermes::multi_assay_experiment)
app <- init(
  data = data,
  modules = modules(
    tm_g_barplot(
      label = "barplot",
      mae_name = "MAE"
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

# Alternatively you can run the sample module with this function call:
if (interactive()) {
  sample_tm_g_barplot()
}

Description

This module provides an interactive boxplot for RNA-seq gene expression analysis.

Usage

tm_g_boxplot(
  label,
  mae_name,
  exclude_assays = character(),
  summary_funs = list(None = NULL, Mean = colMeans, Median = matrixStats::colMedians, Max
    = matrixStats::colMaxs),
  pre_output = NULL,
  post_output = NULL,
  .test = FALSE,
  transformators = list()
)

ui_g_boxplot(
  id,
  mae_name,
  summary_funs,
  pre_output,
  post_output,
  .test = FALSE
)

srv_g_boxplot(
  id,
  data,
  filter_panel_api,
  reporter,
  mae_name,
  exclude_assays,
  summary_funs,
  .test = FALSE
)

sample_tm_g_boxplot(.test = FALSE)

Arguments

Value

Shiny module to be used in the teal app.

Functions

• ui_g_boxplot(): sets up the user interface.

• srv_g_boxplot(): sets up the server with reactive graph.

• sample_tm_g_boxplot(): sample module function.

Examples

data <- teal_data(MAE = hermes::multi_assay_experiment)
app <- init(
  data = data,
  modules = modules(
    tm_g_boxplot(
      label = "boxplot",
      mae_name = "MAE"
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

# Alternatively you can run the sample module with this function call:
if (interactive()) {
  sample_tm_g_boxplot()
}

Description

This module provides an interactive survival forest plot.

Usage

tm_g_forest_tte(
  label,
  adtte_name,
  mae_name,
  adtte_vars = list(aval = "AVAL", is_event = "is_event", paramcd = "PARAMCD", usubjid =
    "USUBJID", avalu = "AVALU"),
  exclude_assays = "counts",
  summary_funs = list(Mean = colMeans, Median = matrixStats::colMedians, Max =
    matrixStats::colMaxs),
  pre_output = NULL,
  post_output = NULL,
  plot_height = c(600L, 200L, 2000L),
  plot_width = c(1360L, 500L, 2000L),
  .test = FALSE,
  transformators = list()
)

ui_g_forest_tte(
  id,
  adtte_name,
  mae_name,
  summary_funs,
  pre_output,
  post_output,
  .test = FALSE
)

srv_g_forest_tte(
  id,
  data,
  filter_panel_api,
  reporter,
  adtte_name,
  mae_name,
  adtte_vars,
  exclude_assays,
  summary_funs,
  plot_height,
  plot_width,
  .test = FALSE
)

sample_tm_g_forest_tte(.test = FALSE)

Arguments

Value

Shiny module to be used in the teal app.

Functions

• ui_g_forest_tte(): sets up the user interface.

• srv_g_forest_tte(): sets up the server with reactive graph.

• sample_tm_g_forest_tte(): sample module function.

Examples

library(dplyr)

data <- teal_data()
data <- within(data, {
  ADTTE <- teal.data::rADTTE %>%
    dplyr::mutate(is_event = .data$CNSR == 0)
  MAE <- hermes::multi_assay_experiment
})
join_keys(data)["ADTTE", "ADTTE"] <- c("STUDYID", "USUBJID", "PARAMCD")

app <- init(
  data = data,
  modules = modules(
    tm_g_forest_tte(
      label = "forestplot",
      adtte_name = "ADTTE",
      mae_name = "MAE"
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

# Alternatively you can run the sample module with this function call:
if (interactive()) {
  sample_tm_g_forest_tte()
}

Description

This teal module produces a grid style Kaplan-Meier plot for data with ADaM structure.

Usage

tm_g_km(
  label,
  adtte_name,
  mae_name,
  adtte_vars = list(aval = "AVAL", is_event = "is_event", paramcd = "PARAMCD", usubjid =
    "USUBJID", avalu = "AVALU"),
  exclude_assays = "counts",
  summary_funs = list(Mean = colMeans, Median = matrixStats::colMedians, Max =
    matrixStats::colMaxs),
  pre_output = NULL,
  post_output = NULL,
  .test = FALSE,
  transformators = list()
)

ui_g_km(
  id,
  adtte_name,
  mae_name,
  summary_funs,
  pre_output,
  post_output,
  .test = FALSE
)

srv_g_km(
  id,
  data,
  filter_panel_api,
  reporter,
  adtte_name,
  mae_name,
  adtte_vars,
  summary_funs,
  exclude_assays,
  .test = FALSE
)

sample_tm_g_km(.test = FALSE)

Arguments

Value

Shiny module to be used in the teal app.

Functions

• ui_g_km(): sets up the user interface.

• srv_g_km(): sets up the user interface.

• sample_tm_g_km(): sample module function.

Examples

library(dplyr)

data <- teal_data()
data <- within(data, {
  ADTTE <- teal.data::rADTTE %>%
    dplyr::mutate(is_event = .data$CNSR == 0)
  MAE <- hermes::multi_assay_experiment
})
join_keys(data)["ADTTE", "ADTTE"] <- c("STUDYID", "USUBJID", "PARAMCD")


modules <- modules(
  tm_g_km(
    label = "kaplan-meier",
    adtte_name = "ADTTE",
    mae_name = "MAE"
  )
)

app <- init(
  data = data,
  modules = modules
)

if (interactive()) {
  shinyApp(ui = app$ui, server = app$server)
}

# Alternatively you can run the sample module with this function call:
if (interactive()) {
  sample_tm_g_km()
}

Description

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,
  .test = FALSE,
  transformators = list()
)

ui_g_pca(id, mae_name, pre_output, post_output, .test = FALSE)

srv_g_pca(
  id,
  data,
  filter_panel_api,
  reporter,
  mae_name,
  exclude_assays,
  .test = FALSE
)

sample_tm_g_pca(.test = FALSE)

Arguments

Value

Shiny module to be used in the teal app.

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

data <- teal_data(MAE = hermes::multi_assay_experiment)
app <- init(
  data = data,
  modules = modules(
    tm_g_pca(
      label = "PCA plot",
      mae_name = "MAE"
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

# Alternatively you can run the sample module with this function call:
if (interactive()) {
  sample_tm_g_pca()
}

Description

This module adds quality flags, filters by genes and/or samples, normalizes AnyHermesData objects and provides interactive plots for RNA-seq gene expression quality control.

Usage

tm_g_quality(
  label,
  mae_name,
  exclude_assays = character(),
  pre_output = NULL,
  post_output = NULL,
  .test = FALSE,
  transformators = list()
)

ui_g_quality(id, mae_name, pre_output, post_output, .test = FALSE)

srv_g_quality(
  id,
  data,
  filter_panel_api,
  reporter,
  mae_name,
  exclude_assays,
  .test = FALSE
)

sample_tm_g_quality(.test = FALSE)

Arguments

Value

Shiny module to be used in the teal app.

Functions

• ui_g_quality(): sets up the user interface.

• srv_g_quality(): sets up the server with reactive graphs.

• sample_tm_g_quality(): sample module function.

Examples

data <- teal_data(MAE = hermes::multi_assay_experiment)
app <- init(
  data = data,
  modules = modules(
    tm_g_quality(
      label = "Quality Control",
      mae_name = "MAE"
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

# Alternatively you can run the sample module with this function call:
if (interactive()) {
  sample_tm_g_quality()
}

Description

This module provides an interactive scatterplot for RNA-seq gene expression analysis.

Usage

tm_g_scatterplot(
  label,
  mae_name,
  exclude_assays = "counts",
  summary_funs = list(Mean = colMeans, Median = matrixStats::colMedians, Max =
    matrixStats::colMaxs),
  pre_output = NULL,
  post_output = NULL,
  .test = FALSE,
  transformators = list()
)

ui_g_scatterplot(
  id,
  mae_name,
  summary_funs,
  pre_output,
  post_output,
  .test = FALSE
)

srv_g_scatterplot(
  id,
  data,
  filter_panel_api,
  reporter,
  mae_name,
  exclude_assays,
  summary_funs,
  .test = FALSE
)

sample_tm_g_scatterplot(.test = FALSE)

Arguments

Value

Shiny module to be used in the teal app.

Functions

• ui_g_scatterplot(): sets up the user interface.

• srv_g_scatterplot(): sets up the server with reactive graph.

• sample_tm_g_scatterplot(): sample module function.

Examples

data <- teal_data(MAE = hermes::multi_assay_experiment)
app <- init(
  data = data,
  modules = modules(
    tm_g_scatterplot(
      label = "scatterplot",
      mae_name = "MAE"
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

# Alternatively you can run the sample module with this function call:
if (interactive()) {
  sample_tm_g_scatterplot()
}

Description

This module provides an interactive volcano plot for RNA-seq gene expression analysis.

Usage

tm_g_volcanoplot(
  label,
  mae_name,
  exclude_assays = character(),
  pre_output = NULL,
  post_output = NULL,
  .test = FALSE,
  transformators = list()
)

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

Value

Shiny module to be used in the teal app.

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"
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

# Alternatively you can run the sample module with this function call:
if (interactive()) {
  sample_tm_g_volcanoplot()
}

Description

This function plots the most expressed genes.

Usage

top_gene_plot(object, assay_name)

Arguments

Value

Plot to be displayed in the teal app.

Examples

library(hermes)
object <- HermesData(summarized_experiment)
result <- top_gene_plot(object, assay_name = "counts")

Description

This validation function checks that a given hermes::GeneSpec has at least one gene selected and that all genes are included in possible choices.

Usage

validate_gene_spec(gene_spec, gene_choices)

Arguments

Description

This validation function checks that a given vector x is a factor with the specified number of levels.

Usage

validate_n_levels(x, name, n_levels)

Arguments