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R/pwrEWAS.shiny_v1.7.R
In pwrEWAS: A user-friendly tool for comprehensive power estimation for epigenome wide association studies (EWAS)
Defines functions
pwrEWAS_shiny
Documented in
pwrEWAS_shiny
#' @title Shiny pwrEWAS
#'
#' @description pwrEWAS_shiny provides a user-friendly point-and-click interface for pwrEWAS
#'
#' @keywords DNAm microarray power Shiny
#'
#' @return pwrEWAS_shiny initializes pwrEWAS's user-interface
#'
#' @export
#'
#' @examples
#'
#' if(interactive()) {
#' pwrEWAS_shiny()
#' }
pwrEWAS_shiny <- function(){
# library(shiny)
# library(shinyBS)
# library(ggplot2)
# library(parallel)
# user input / default values
input2 <- NULL
input2$Nmin <- 10
input2$Nmax <- 50
input2$NCntPer <- 0.5
input2$Nsteps <- 10
input2$J <- 100000 # simulated CPGs
input2$targetDmCpGs <- 100
input2$targetDeltaString <- "0.2, 0.5"
input2$tauString <- "0.01, 0.03"
input2$targetDelta <- as.numeric(unlist(strsplit(input2$targetDeltaString,",")))
input2$method <- "limma"
input2$detectionLimit <- 0.01
input2$FDRcritVal <- 0.05
input2$cores <- round(parallel::detectCores(all.tests = FALSE, logical = TRUE)/2)
input2$sim <- 50
input2$tissueType <- "Saliva"
# input <- input2
#############################################################
server <- function(input,output){
shiny::observeEvent(input$goButton, {
# reset plots
output$powerPlot <- NULL
output$meanPower <- NULL
output$probTP <- NULL
output$deltaDensity <- NULL
output$log <- NULL
shiny::withProgress(message = 'Program running. Please wait.', detail = "This can take several minutes. Progress will be displayed in R console.", value = NULL, {
runTimeStart <- Sys.time()
if(input$switchTargetDmSd == 1){
out <- pwrEWAS(minTotSampleSize = input$Nmin,
maxTotSampleSize = input$Nmax,
SampleSizeSteps = input$Nsteps,
NcntPer = input$NCntPer,
targetDelta = as.numeric(unlist(strsplit(input$targetDeltaString,","))),
J = input$J,
targetDmCpGs = input$targetDmCpGs,
tissueType = input$tissueType,
detectionLimit = input$detectionLimit,
DMmethod = input$method,
FDRcritVal = input$FDRcritVal,
core = input$cores,
sims = input$sim)
} else if(input$switchTargetDmSd == 2){
out <- pwrEWAS(minTotSampleSize = input$Nmin,
maxTotSampleSize = input$Nmax,
SampleSizeSteps = input$Nsteps,
NcntPer = input$NCntPer,
deltaSD = as.numeric(unlist(strsplit(input$tauString,","))),
J = input$J,
targetDmCpGs = input$targetDmCpGs,
tissueType = input$tissueType,
detectionLimit = input$detectionLimit,
DMmethod = input$method,
FDRcritVal = input$FDRcritVal,
core = input$cores,
sims = input$sim)
}
output$powerPlot <- shiny::renderPlot({isolate(pwrEWAS_powerPlot(out$powerArray, sd = ifelse(input$switchTargetDmSd == 1, FALSE, TRUE)))})
# mean power table
meanPowerTable <- cbind(rownames(out$meanPower), round(out$meanPower, 2))
if(input$switchTargetDmSd == 1){
colnames(meanPowerTable)[1] <- shiny::HTML("N</sub> \\ Δ<sub>β")
} else if(input$switchTargetDmSd == 2){
colnames(meanPowerTable)[1] <- shiny::HTML("N</sub> \\ SD(Δ<sub>β)")
}
positionToAddTitle <- ceiling(dim(meanPowerTable)[2]/2)
colnames(meanPowerTable)[positionToAddTitle] <- paste0(shiny::HTML("Power<br/>"), colnames(meanPowerTable)[positionToAddTitle])
output$meanPower <- shiny::renderTable({meanPowerTable}, sanitize.text.function = function(x) x)
# delta density plot
output$deltaDensity <- shiny::renderPlot({isolate(pwrEWAS_deltaDensity(out$deltaArray, input$detectionLimit, sd = ifelse(input$switchTargetDmSd == 1, FALSE, TRUE)))})
# probability of detecting at least one TP
probTPTable <- cbind(rownames(out$metric$probTP), round(out$metric$probTP, 2))
if(input$switchTargetDmSd == 1){
colnames(probTPTable)[1] <- shiny::HTML("N</sub> \\ Δ<sub>β")
} else if(input$switchTargetDmSd == 2){
colnames(probTPTable)[1] <- shiny::HTML("N</sub> \\ SD(Δ<sub>β)")
}
colnames(probTPTable)[positionToAddTitle] <- paste0(shiny::HTML("P(#TP≥1) <br/>"), colnames(probTPTable)[positionToAddTitle])
output$probTP <- shiny::renderTable({probTPTable}, sanitize.text.function = function(x) x)
# run time
runTimeStop <- difftime(Sys.time(), runTimeStart, units = "auto")
# log
logString <- paste0(
"Tissue type = ", input$tissueType, "\n",
"Minimum total sample size = ", input$Nmin, "\n",
"Maximum total sample size = ", input$Nmax, "\n",
"Sample size increments = ", input$Nsteps, "\n",
"Percentage samples in group 1 = ", input$NCntPer, "\n",
"Number of CpGs to be tested = ", input$J, "\n",
"Target number of DM CpGs = ", input$targetDmCpGs, "\n",
if(input$switchTargetDmSd == 1){
paste0("'Target max Delta' was selected \n",
"Target maximal difference in DNAm (comma delimited) = ", input$targetDeltaString)
} else if(input$switchTargetDmSd == 2){
paste0("'SD(Δ)Delta)' was selected \n",
"Std. dev. of difference in DNAm (comma delimited) = ", input$tauString)}, "\n",
"Target FDR = ", input$FDRcritVal, "\n",
"Detection Limit = ", input$detectionLimit, "\n",
"Method for DM analysis = ", input$method, "\n",
"Number of simulated data sets = ", input$sim, "\n",
"Threads = ", input$cores, "\n",
"Run time = ", round(runTimeStop,1), " ", attr(runTimeStop, "units"))
output$log <- renderText({HTML(logString)})
}) # processbar done
})
}
ui <- shiny::fluidPage(
shiny::tags$head(shiny::tags$style(shiny::HTML(".shiny-notification {
height: 150px;
width: 400px;
position:fixed;
font-size: 200%;
top: calc(50% - 35px);;
left: calc(50% - 100px);;}"))),
shiny::tags$style(type='text/css', '#log {text-align: left;}'),
shiny::titlePanel("pwrEWAS"),
shiny::HTML("pwrEWAS is a computationally efficient tool to estimate power in EWAS as a function of sample and effect size
for two-group comparisons of DNAm (e.g., case vs control, exposed vs non-exposed, etc.). Detailed description
of in-/outputs, instructions and an example, as well as interpretations of the example results are provided in
the following vignette: "),
shiny::tags$a(href="https://bioconductor.org/packages/devel/bioc/vignettes/pwrEWAS/inst/doc/pwrEWAS.pdf", "pwrEWAS vignette"),
shiny::HTML("</br></br>Authors: Stefan Graw, Devin Koestler </br>"),
shiny::HTML("Department of Biostatistics, University of Kansas School of Medicine"),
shiny::sidebarLayout(
shiny::sidebarPanel(
### Inputs
shinyBS::popify(shiny::selectInput(inputId = "tissueType", label = "Tissue Type", choices = c("Adult (PBMC)",
"Saliva",
"Sperm",
"Lymphoma",
"Placenta",
"Liver",
"Colon",
"Blood adult",
"Blood 5 year olds",
"Blood newborns",
"Cord-blood (whole blood)",
"Cord-blood (PBMC)")),
'Heterogeneity of different tissue types can have effects on the results. Please select your tissue type of interest or one you believe is the closest.', placement = "top"),
shinyBS::popify(shiny::numericInput(inputId = "Nmin", label = "Minimum total sample size", value = input2$Nmin, min = 4, step = 1),
'Lowest total sample sizes to be considered.'),
shinyBS::popify(shiny::numericInput(inputId = "Nmax", label = "Maximum total sample size", value = input2$Nmax, min = 4, step = 1),
'Highest total sample sizes to be considered.'),
shinyBS::popify(shiny::numericInput(inputId = "Nsteps", label = "Sample size increments", value = input2$Nsteps, min = 1, step = 1),
'Steps with which total sample size increases from "Minimum total sample size" to "Maximum total sample size".'),
shinyBS::popify(shiny::numericInput(inputId = "NCntPer", label = "Samples rate for group 1", value = input2$NCntPer, min = 0, max = 1, step = 0.1),
'Rate by which the total sample size is split into groups (0.5 corresponds to a balanced study; rate for group 2 is equal to 1 rate of group 1)'),
shinyBS::popify(shiny::numericInput(inputId = "J", label = "Number of CpGs tested", value = input2$J, min = 1, step = 10000),
'Number of CpG site that will simulated and tested (increasing Number of CpGs tested will require increasing RAM (memory)).'),
shinyBS::popify(shiny::numericInput(inputId = "targetDmCpGs", label = "Target number of DM CpGs", value = input2$targetDmCpGs, min = 1, step = 10),
'Target number of CpGs simulated with meaningful differences (differences greater than detection limit)'),
shinyBS::popify(shinyWidgets::radioGroupButtons(inputId = "switchTargetDmSd",choiceValues = c(1,2), justified = TRUE, choiceNames = c(shiny::HTML("Target max Δ"), shiny::HTML("SD(Δ)"))),
shiny::HTML('The expected simulated differences in methylation can be control by "Target max Δ" or "SD(Δ)". For "Target max Δ" standard deviations of the simulated differences is automatically determined such that the 99%til of the simulated differences are within a range around the provided values. If "SD(Δ)" is chosen, differences in methylation will be simulated using provided standard deviation.')),
shiny::conditionalPanel(
condition = "input.switchTargetDmSd == 1",
shinyBS::popify(shiny::textInput(inputId = "targetDeltaString", label = "Target maximal difference in DNAm (comma delimited)", value = input2$targetDeltaString),
'Standard deviations of the simulated differences is automatically determined such that the 99%til of the simulated differences are within a range around the provided values.')
),
shiny::conditionalPanel(
condition = "input.switchTargetDmSd == 2",
shinyBS::popify(shiny::textInput(inputId = "tauString", label = "Std. dev. of difference in DNAm (comma delimited)", value = input2$tauString),
'Differnces in methylation will be simulated using provided standard deviation.')
),
shinyBS::popify(shiny::numericInput(inputId = "FDRcritVal", label = "Target FDR", value = input2$FDRcritVal, min = 0, max = 1, step = 0.01),
'Critical value to control the False Discovery Rate (FDR) using the Benjamini and Hochberg method.'),
shiny::checkboxInput(inputId = "advancedSettings", label = "Advanced settings"),
shiny::conditionalPanel(
condition = "input.advancedSettings == 1",
shinyBS::popify(shiny::numericInput(inputId = "detectionLimit", label = "Detection Limit", value = input2$detectionLimit, min = 0, max = 1, step = 0.01),
'Limit to detect changes in methylation. Simulated differences below the detection limit will not be consider as meaningful differentially methylated CpGs.'),
shinyBS::popify(shiny::selectInput(inputId = "method", label = "Method for DM analysis", choices = c("limma", "t-test (unequal var)", "t-test (equal var)", "Wilcox rank sum", "CPGassoc")),
'Method used to perform differential methylation analysis.', placement = "top"),
shinyBS::popify(shiny::numericInput(inputId = "sim", label = "Number of simulated data sets", value = input2$sim, min = 1, step = 10),
'Number of repeated simulation/simulated data sets under the same conditions for consistent results.'),
shinyBS::popify(shiny::numericInput(inputId = "cores", label = "Threads", value = input2$cores, min = 1, max = parallel::detectCores(all.tests = FALSE, logical = TRUE)-1, step = 1),
'Number of cores used to run multiple threads. Ideally, the number of different total samples sizes multiplied by the number of effect sizes should be a multiple (m) of the number of cores (#sampleSizes * #effectSizes = m * #threads). An increasing number of threads will require an increasing amount of RAM (memory).', placement = "top")
),
# submitButton(text = "Simulate"),
shiny::actionButton(inputId = "goButton", label = "Go!", width = '100%', style='font-size:150%')
),
### Outputs
shiny::mainPanel(
shiny::fluidRow(
shiny::column(12, align="center",
shiny::plotOutput("powerPlot"),
shiny::br(),shiny::br(),shiny::br(),
shiny::splitLayout(cellWidths = c("50%", "50%"),
shiny::tableOutput(outputId = "meanPower"),
shiny::tableOutput(outputId = "probTP")),
shiny::plotOutput("deltaDensity"),
shiny::verbatimTextOutput(outputId = "log")
)
)
)
)
)
shiny::shinyApp(ui = ui, server = server)
}
# pwrEWAS_shiny()
Try the pwrEWAS package in your browser
Any scripts or data that you put into this service are public.
pwrEWAS documentation built on Nov. 8, 2020, 11:13 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions pwrEWAS_shiny
Documented in pwrEWAS_shiny
#' @title Shiny pwrEWAS
#'
#' @description pwrEWAS_shiny provides a user-friendly point-and-click interface for pwrEWAS
#'
#' @keywords DNAm microarray power Shiny
#'
#' @return pwrEWAS_shiny initializes pwrEWAS's user-interface
#'
#' @export
#'
#' @examples
#'
#' if(interactive()) {
#' pwrEWAS_shiny()
#' }
pwrEWAS_shiny <- function(){
# library(shiny)
# library(shinyBS)
# library(ggplot2)
# library(parallel)
# user input / default values
input2 <- NULL
input2$Nmin <- 10
input2$Nmax <- 50
input2$NCntPer <- 0.5
input2$Nsteps <- 10
input2$J <- 100000 # simulated CPGs
input2$targetDmCpGs <- 100
input2$targetDeltaString <- "0.2, 0.5"
input2$tauString <- "0.01, 0.03"
input2$targetDelta <- as.numeric(unlist(strsplit(input2$targetDeltaString,",")))
input2$method <- "limma"
input2$detectionLimit <- 0.01
input2$FDRcritVal <- 0.05
input2$cores <- round(parallel::detectCores(all.tests = FALSE, logical = TRUE)/2)
input2$sim <- 50
input2$tissueType <- "Saliva"
# input <- input2
#############################################################
server <- function(input,output){
shiny::observeEvent(input$goButton, {
# reset plots
output$powerPlot <- NULL
output$meanPower <- NULL
output$probTP <- NULL
output$deltaDensity <- NULL
output$log <- NULL
shiny::withProgress(message = 'Program running. Please wait.', detail = "This can take several minutes. Progress will be displayed in R console.", value = NULL, {
runTimeStart <- Sys.time()
if(input$switchTargetDmSd == 1){
out <- pwrEWAS(minTotSampleSize = input$Nmin,
maxTotSampleSize = input$Nmax,
SampleSizeSteps = input$Nsteps,
NcntPer = input$NCntPer,
targetDelta = as.numeric(unlist(strsplit(input$targetDeltaString,","))),
J = input$J,
targetDmCpGs = input$targetDmCpGs,
tissueType = input$tissueType,
detectionLimit = input$detectionLimit,
DMmethod = input$method,
FDRcritVal = input$FDRcritVal,
core = input$cores,
sims = input$sim)
} else if(input$switchTargetDmSd == 2){
out <- pwrEWAS(minTotSampleSize = input$Nmin,
maxTotSampleSize = input$Nmax,
SampleSizeSteps = input$Nsteps,
NcntPer = input$NCntPer,
deltaSD = as.numeric(unlist(strsplit(input$tauString,","))),
J = input$J,
targetDmCpGs = input$targetDmCpGs,
tissueType = input$tissueType,
detectionLimit = input$detectionLimit,
DMmethod = input$method,
FDRcritVal = input$FDRcritVal,
core = input$cores,
sims = input$sim)
}
output$powerPlot <- shiny::renderPlot({isolate(pwrEWAS_powerPlot(out$powerArray, sd = ifelse(input$switchTargetDmSd == 1, FALSE, TRUE)))})
# mean power table
meanPowerTable <- cbind(rownames(out$meanPower), round(out$meanPower, 2))
if(input$switchTargetDmSd == 1){
colnames(meanPowerTable)[1] <- shiny::HTML("N</sub> \\ Δ<sub>β")
} else if(input$switchTargetDmSd == 2){
colnames(meanPowerTable)[1] <- shiny::HTML("N</sub> \\ SD(Δ<sub>β)")
}
positionToAddTitle <- ceiling(dim(meanPowerTable)[2]/2)
colnames(meanPowerTable)[positionToAddTitle] <- paste0(shiny::HTML("Power<br/>"), colnames(meanPowerTable)[positionToAddTitle])
output$meanPower <- shiny::renderTable({meanPowerTable}, sanitize.text.function = function(x) x)
# delta density plot
output$deltaDensity <- shiny::renderPlot({isolate(pwrEWAS_deltaDensity(out$deltaArray, input$detectionLimit, sd = ifelse(input$switchTargetDmSd == 1, FALSE, TRUE)))})
# probability of detecting at least one TP
probTPTable <- cbind(rownames(out$metric$probTP), round(out$metric$probTP, 2))
if(input$switchTargetDmSd == 1){
colnames(probTPTable)[1] <- shiny::HTML("N</sub> \\ Δ<sub>β")
} else if(input$switchTargetDmSd == 2){
colnames(probTPTable)[1] <- shiny::HTML("N</sub> \\ SD(Δ<sub>β)")
}
colnames(probTPTable)[positionToAddTitle] <- paste0(shiny::HTML("P(#TP≥1) <br/>"), colnames(probTPTable)[positionToAddTitle])
output$probTP <- shiny::renderTable({probTPTable}, sanitize.text.function = function(x) x)
# run time
runTimeStop <- difftime(Sys.time(), runTimeStart, units = "auto")
# log
logString <- paste0(
"Tissue type = ", input$tissueType, "\n",
"Minimum total sample size = ", input$Nmin, "\n",
"Maximum total sample size = ", input$Nmax, "\n",
"Sample size increments = ", input$Nsteps, "\n",
"Percentage samples in group 1 = ", input$NCntPer, "\n",
"Number of CpGs to be tested = ", input$J, "\n",
"Target number of DM CpGs = ", input$targetDmCpGs, "\n",
if(input$switchTargetDmSd == 1){
paste0("'Target max Delta' was selected \n",
"Target maximal difference in DNAm (comma delimited) = ", input$targetDeltaString)
} else if(input$switchTargetDmSd == 2){
paste0("'SD(Δ)Delta)' was selected \n",
"Std. dev. of difference in DNAm (comma delimited) = ", input$tauString)}, "\n",
"Target FDR = ", input$FDRcritVal, "\n",
"Detection Limit = ", input$detectionLimit, "\n",
"Method for DM analysis = ", input$method, "\n",
"Number of simulated data sets = ", input$sim, "\n",
"Threads = ", input$cores, "\n",
"Run time = ", round(runTimeStop,1), " ", attr(runTimeStop, "units"))
output$log <- renderText({HTML(logString)})
}) # processbar done
})
}
ui <- shiny::fluidPage(
shiny::tags$head(shiny::tags$style(shiny::HTML(".shiny-notification {
height: 150px;
width: 400px;
position:fixed;
font-size: 200%;
top: calc(50% - 35px);;
left: calc(50% - 100px);;}"))),
shiny::tags$style(type='text/css', '#log {text-align: left;}'),
shiny::titlePanel("pwrEWAS"),
shiny::HTML("pwrEWAS is a computationally efficient tool to estimate power in EWAS as a function of sample and effect size
for two-group comparisons of DNAm (e.g., case vs control, exposed vs non-exposed, etc.). Detailed description
of in-/outputs, instructions and an example, as well as interpretations of the example results are provided in
the following vignette: "),
shiny::tags$a(href="https://bioconductor.org/packages/devel/bioc/vignettes/pwrEWAS/inst/doc/pwrEWAS.pdf", "pwrEWAS vignette"),
shiny::HTML("</br></br>Authors: Stefan Graw, Devin Koestler </br>"),
shiny::HTML("Department of Biostatistics, University of Kansas School of Medicine"),
shiny::sidebarLayout(
shiny::sidebarPanel(
### Inputs
shinyBS::popify(shiny::selectInput(inputId = "tissueType", label = "Tissue Type", choices = c("Adult (PBMC)",
"Saliva",
"Sperm",
"Lymphoma",
"Placenta",
"Liver",
"Colon",
"Blood adult",
"Blood 5 year olds",
"Blood newborns",
"Cord-blood (whole blood)",
"Cord-blood (PBMC)")),
'Heterogeneity of different tissue types can have effects on the results. Please select your tissue type of interest or one you believe is the closest.', placement = "top"),
shinyBS::popify(shiny::numericInput(inputId = "Nmin", label = "Minimum total sample size", value = input2$Nmin, min = 4, step = 1),
'Lowest total sample sizes to be considered.'),
shinyBS::popify(shiny::numericInput(inputId = "Nmax", label = "Maximum total sample size", value = input2$Nmax, min = 4, step = 1),
'Highest total sample sizes to be considered.'),
shinyBS::popify(shiny::numericInput(inputId = "Nsteps", label = "Sample size increments", value = input2$Nsteps, min = 1, step = 1),
'Steps with which total sample size increases from "Minimum total sample size" to "Maximum total sample size".'),
shinyBS::popify(shiny::numericInput(inputId = "NCntPer", label = "Samples rate for group 1", value = input2$NCntPer, min = 0, max = 1, step = 0.1),
'Rate by which the total sample size is split into groups (0.5 corresponds to a balanced study; rate for group 2 is equal to 1 rate of group 1)'),
shinyBS::popify(shiny::numericInput(inputId = "J", label = "Number of CpGs tested", value = input2$J, min = 1, step = 10000),
'Number of CpG site that will simulated and tested (increasing Number of CpGs tested will require increasing RAM (memory)).'),
shinyBS::popify(shiny::numericInput(inputId = "targetDmCpGs", label = "Target number of DM CpGs", value = input2$targetDmCpGs, min = 1, step = 10),
'Target number of CpGs simulated with meaningful differences (differences greater than detection limit)'),
shinyBS::popify(shinyWidgets::radioGroupButtons(inputId = "switchTargetDmSd",choiceValues = c(1,2), justified = TRUE, choiceNames = c(shiny::HTML("Target max Δ"), shiny::HTML("SD(Δ)"))),
shiny::HTML('The expected simulated differences in methylation can be control by "Target max Δ" or "SD(Δ)". For "Target max Δ" standard deviations of the simulated differences is automatically determined such that the 99%til of the simulated differences are within a range around the provided values. If "SD(Δ)" is chosen, differences in methylation will be simulated using provided standard deviation.')),
shiny::conditionalPanel(
condition = "input.switchTargetDmSd == 1",
shinyBS::popify(shiny::textInput(inputId = "targetDeltaString", label = "Target maximal difference in DNAm (comma delimited)", value = input2$targetDeltaString),
'Standard deviations of the simulated differences is automatically determined such that the 99%til of the simulated differences are within a range around the provided values.')
),
shiny::conditionalPanel(
condition = "input.switchTargetDmSd == 2",
shinyBS::popify(shiny::textInput(inputId = "tauString", label = "Std. dev. of difference in DNAm (comma delimited)", value = input2$tauString),
'Differnces in methylation will be simulated using provided standard deviation.')
),
shinyBS::popify(shiny::numericInput(inputId = "FDRcritVal", label = "Target FDR", value = input2$FDRcritVal, min = 0, max = 1, step = 0.01),
'Critical value to control the False Discovery Rate (FDR) using the Benjamini and Hochberg method.'),
shiny::checkboxInput(inputId = "advancedSettings", label = "Advanced settings"),
shiny::conditionalPanel(
condition = "input.advancedSettings == 1",
shinyBS::popify(shiny::numericInput(inputId = "detectionLimit", label = "Detection Limit", value = input2$detectionLimit, min = 0, max = 1, step = 0.01),
'Limit to detect changes in methylation. Simulated differences below the detection limit will not be consider as meaningful differentially methylated CpGs.'),
shinyBS::popify(shiny::selectInput(inputId = "method", label = "Method for DM analysis", choices = c("limma", "t-test (unequal var)", "t-test (equal var)", "Wilcox rank sum", "CPGassoc")),
'Method used to perform differential methylation analysis.', placement = "top"),
shinyBS::popify(shiny::numericInput(inputId = "sim", label = "Number of simulated data sets", value = input2$sim, min = 1, step = 10),
'Number of repeated simulation/simulated data sets under the same conditions for consistent results.'),
shinyBS::popify(shiny::numericInput(inputId = "cores", label = "Threads", value = input2$cores, min = 1, max = parallel::detectCores(all.tests = FALSE, logical = TRUE)-1, step = 1),
'Number of cores used to run multiple threads. Ideally, the number of different total samples sizes multiplied by the number of effect sizes should be a multiple (m) of the number of cores (#sampleSizes * #effectSizes = m * #threads). An increasing number of threads will require an increasing amount of RAM (memory).', placement = "top")
),
# submitButton(text = "Simulate"),
shiny::actionButton(inputId = "goButton", label = "Go!", width = '100%', style='font-size:150%')
),
### Outputs
shiny::mainPanel(
shiny::fluidRow(
shiny::column(12, align="center",
shiny::plotOutput("powerPlot"),
shiny::br(),shiny::br(),shiny::br(),
shiny::splitLayout(cellWidths = c("50%", "50%"),
shiny::tableOutput(outputId = "meanPower"),
shiny::tableOutput(outputId = "probTP")),
shiny::plotOutput("deltaDensity"),
shiny::verbatimTextOutput(outputId = "log")
)
)
)
)
)
shiny::shinyApp(ui = ui, server = server)
}
# pwrEWAS_shiny()
Try the pwrEWAS package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.