R/ShinyGPATree.R
In asthakhatiwada/GPATree: A package to implement the GPA-Tree method
Defines functions
ShinyGPATree
Documented in
ShinyGPATree
#' Run ShinyGPATree App
#'
#' This function will initialize the ShinyGPATree App for dynamic and interactive visualization of GPA-Tree model results.
#'
#' @author Aastha Khatiwada
#'
#' @param object An object of class GPATree.
#' @return Output of GPA-Tree model.
#' @examples
#' \dontrun{
#' library(GPATree)
#'
#' # load GPATree example data
#' data(GPATreeExampleData)
#'
#' #fitting the GPATree model
#' fit <- GPATree(GPATreeExampleData$gwasPval, GPATreeExampleData$annMat)
#'
#' # initialize the ShinyGPATree app
#' ShinyGPATree(fit)
#' }
#' @export
ShinyGPATree <- function(object){
ui <- shiny::fluidPage(
shiny::tags$head(shiny::tags$style(".checkbox-inline {margin: 0 !important;}")),
# App title ----
shiny::titlePanel("ShinyGPATree"),
shiny::tags$head(shiny::tags$style(shiny::HTML(".shiny-notification {position:fixed; width: 200px; top: 0%; right: 0%;}"))),
# Sidebar layout with input and output definitions ----
shiny::sidebarLayout(
# Sidebar panel for inputs ----
shiny::sidebarPanel(
# Input: Slider for the value of log cp ----
shiny::sliderInput(inputId = "log10cp",
label = "Complexity parameter (cp), log10 scale",
value = -5,
min = -5,
max = 0,
step = 0.002,
width= "95%"),
shiny::helpText("Change cp to further prune the GPA-Tree model fit. To plot the original GPA-Tree model set pointer at -5."),
shiny::hr(),
shiny::h4("Plot options"),
shiny::sliderInput(inputId = "plotHeight",
label="Plot height",
value = 350,
min=100,
max=2100,
step = 50,
width= "95%"),
shiny::sliderInput(inputId = "plotWidth",
label="Plot width",
value = 600,
min=400,
max=4000,
step = 50,
width= "95%"),
shiny::helpText("Change plot dimension using the options above.")
),
# Main panel for displaying outputs ----
shiny::mainPanel( #main panel ####
shiny::tabsetPanel( # tabsetpanel ####
shiny::tabPanel("Plot",
shiny::hr(),
shiny::downloadButton("downloadPlot", label = "Download Plot"),
shiny::htmlOutput('cpconversion'),
shiny::plotOutput(outputId = "distPlot", height = "auto", width = "100%"),
shiny::hr(),
shiny::htmlOutput("anntext"),
shiny::div(DT::dataTableOutput(outputId = "leafInfo", width = "100%"), style = "font-size:80%")
),
shiny::tabPanel("Info",
shiny::wellPanel(
shiny::fluidRow(
shiny::column(5,
shiny::numericInput(inputId = "FDR",
label="False Discovery Rate (FDR) level",
value = 0.01,
min=0,
max=1,
step = 0.01),
shiny::helpText("FDR must be between 0 and 1.")),
shiny::column(4, offset = 2,
shiny::radioButtons(inputId = 'fdrControl',
label = 'Type of FDR',
choices = list('global' = 'global',
'local' = 'local'),
selected = 'global'))
),
shiny::hr(),
shiny::checkboxGroupInput(inputId="associd",
label="Choose association status of SNPs",
choices = list("Non-null" = 1, "Null" = 0),
inline=TRUE,
selected = 1,
width = "80%"),
shiny::checkboxGroupInput(inputId="leafindex",
label="Choose Leaf",
choices = list("LEAF 1" = "LEAF 1",
"LEAF 2" = "LEAF 2",
"LEAF 3" = "LEAF 3"),
inline=TRUE,
width = "80%",
selected = c("LEAF 1")),
shiny::downloadButton("downloadTable", label = "Download SNP Table")
),
shiny::h5(shiny::tags$b("SNP Table")),
shiny::div(shiny::tableOutput(outputId = 'tabletext'), style = "font-size:75%"),
shiny::div(DT::dataTableOutput(outputId = "table", width = "100%"), style = "font-size:80%")
)
)
)
)
)
# column description for SNP Table ####
nt <- function() {
if (ncol(object@fit$Zmarg) == 1){
col1 <- c("SNPID", "local FDR", 'p-value', 'Leaf', "others")
col2 <- c("SNP ID.",
"1 - posterior probability of being non-null for the phenotype based on GPA-Tree model.",
"GWAS association p-value provided to the GPATree() function parameter 'gwasPval'.",
"Leaves in which SNPs fall.",
"Functional annotation information provided to the GPATree() function parameter 'annMat'.")
df_out <- as.data.frame(cbind(col1, col2))
colnames(df_out) <- c(" ","Column description")
}
return(df_out)
}
# defined function assocAll ####
assocAll <- function(FDR, fdrControl, log10cp) {
if (log10cp == -5){
assoc_out <- assoc(object, FDR = FDR, fdrControl = fdrControl)
} else {
GPATreemodel_pruned <- prune(object, cp = 10^log10cp)
assoc_out <- assoc(GPATreemodel_pruned, FDR = FDR, fdrControl = fdrControl)
}
assoc_out <- cbind(assoc_out, 1 - object@fit$Zmarg)
if (ncol(object@fit$Zmarg) == 1){
colnames(assoc_out)[3] <- 'local FDR'
}
if (is.null(rownames(assoc_out))) {
assoc_out <- cbind('SNPID' = paste('SNP_', 1:nrow(assoc_out), sep = ''), assoc_out)
} else {
assoc_out <- cbind('SNPID' = rownames(assoc_out), assoc_out)
}
assoc_out <- cbind(assoc_out, object@gwasPval)
ncol_start_ann <- ncol(assoc_out)
if (ncol(object@fit$Zmarg) == 1){
colnames(assoc_out)[5] <- 'p-value'
}
assoc_out <- cbind(assoc_out, object@annMat)
colnames(assoc_out)[(ncol_start_ann+1):ncol(assoc_out)] <- colnames(object@annMat)
return(assoc_out)
}
# defined function plotOutput ####
plotOutput <- function(log10cp){
if (log10cp == -5){
plot(object)
} else {
GPATreemodel_pruned <- prune(object, cp = 10^log10cp)
plot(GPATreemodel_pruned)
}
}
#defined function leafinfo_output ####
leafinfo_output <- function(object, log10cp){
if (log10cp == -5){
out <- leaf(object)
} else {
GPATreemodel_pruned <- prune(object, cp = 10^log10cp)
out <- leaf(GPATreemodel_pruned)
}
out <- as.data.frame(cbind('Leaf' = rownames(out), out))
# if (nrow(out) == 1){ out$Note <- 'No annotations selected' }
return(out)
}
# server ####
server <- function(input, output, session) {
output$cpconversion <- shiny::renderText({
if (input$log10cp == -5){
paste(" ", "<b> GPA-Tree model<b>", sep="<br/>")
} else {
paste(" ", paste("<b>GPA-Tree model pruned using cp =", round(10^input$log10cp, 5),"<b>"), sep = '<br/>')
}
})
pHeight <- shiny::reactive({
new_height <- input$plotHeight
return(new_height)
})
pWidth <- shiny::reactive({
new_width <- input$plotWidth
return(new_width)
})
n0 <- shiny::reactive({
leafinfo_out <- leafinfo_output(object,
log10cp = input$log10cp)
for (i in 1:ncol(object@fit$Zmarg)) {
leafinfo_out[, i+1] <- formatC(as.numeric(leafinfo_out[, i+1]), format = "E", digits = 2)
}
return(leafinfo_out)
})
output$distPlot <- shiny::renderPlot(height = function()pHeight(), width = function()pWidth(), {
# update of progress bar dependent on number of leaves in the tree
n_index <- n0()
n <- nrow(n_index)
# Create a Progress object
progress <- shiny::Progress$new(session, min = 1, max = 1.1*n)
# Make sure it closes when we exit this reactive, even if there's an error
on.exit(progress$close())
progress$set(message = "Updating plot . . .", value = 0)
for (i in 1:n) {
# Update the progress bar
progress$set(value = i)
# Pause for 0.1 seconds to simulate a long computation.
Sys.sleep(0.1)
}
cex_new <- (pWidth()*pHeight())/(pWidth()*pHeight())
plotOutput(log10cp = input$log10cp)
})
output$anntext <- shiny::renderText({
paste("<b><h5>Leaf Description<h5><b>")
})
output$leafInfo <- DT::renderDataTable({
leafDat <- n0() %>%
DT::datatable(rownames = FALSE,
class = 'cell-border stripe',
options = list(scrollY = "140px",
searching = FALSE,
lengthMenu = c(100, 1000),
scipen = 4,
scrollX = TRUE,
paging = FALSE))
})
n1 <- shiny::reactive({
asso_ans <- assocAll(FDR = input$FDR, fdrControl = input$fdrControl, log10cp = input$log10cp)
return(asso_ans)
})
n2 <- shiny::reactive({
assocOutnew <- n1()
leaf_check <- c()
for (i in 1:length(input$leafindex)) {
leaf_out <- paste(stringr::str_split(input$leafindex[i], " ")[[1]][1],
stringr::str_split(input$leafindex[i], " ")[[1]][2])
leaf_check <- c(leaf_check, leaf_out)
}
select_rows <- c()
for (i in 1:ncol(object@fit$Zmarg)) {
select_rows <- c(select_rows, which(assocOutnew[, i+1] %in% input$associd))
}
assocOutnew <- assocOutnew[select_rows, ]
assocOutnew <- subset(assocOutnew, assocOutnew$leaf %in% leaf_check)
if (ncol(object@fit$Zmarg) == 1){
assocOutnew <- as.data.frame(assocOutnew[, c('SNPID', 'local FDR', 'p-value',
'leaf', colnames(object@annMat))])
assocOutnew <- assocOutnew[order(assocOutnew$`local FDR`), ]
}
for (i in 2:(2*ncol(object@fit$Zmarg)+1)) {
assocOutnew[, i] <- formatC(as.numeric(assocOutnew[, i]), format = "E", digits = 2)
}
colnames(assocOutnew)[2*ncol(object@fit$Zmarg)+2] <- 'Leaf'
return(assocOutnew)
})
output$tabletext <- shiny::renderTable({
tout <- nt()
return(tout)
})
output$table <- DT::renderDT({
# update of progress bar dependent on number of leaves in the tree
n <- 20
# Create a Progress object
progress <- shiny::Progress$new(session, min = 1, max = 1.1*n)
# Make sure it closes when we exit this reactive, even if there's an error
on.exit(progress$close())
progress$set(message = "Updating table . . .", value = 0)
for (i in 1:n) {
# Update the progress bar
progress$set(value = i)
# Pause for 0.1 seconds to simulate a long computation.
Sys.sleep(0.1)
}
dtab <- n2() %>%
DT::datatable(rownames = FALSE,
class = 'cell-border stripe',
options = list(scrollY = "200px",
searching = TRUE,
# dom = 'b',
lengthMenu = c(100, 1000),
scipen = 4,
scrollX = TRUE,
paging = FALSE))
return(dtab)
})
inputVar <- shiny::reactive({
n1_out <- n0()
if (ncol(object@fit$Zmarg) == 1){
n1out_leaf <- paste(n1_out$Leaf,
' (local FDR = ',
formatC(round(as.numeric(n1_out[, 2]), 3), 3, format="f"),
")", sep = '')
}
return(n1out_leaf)
})
shiny::observeEvent(inputVar(), {shiny::updateCheckboxGroupInput(session,
inputId="leafindex",
label = "Choose Leaf",
choices = inputVar(),
selected = inputVar(),
inline = TRUE)} )
output$downloadTable <- shiny::downloadHandler(
filename = function() {
paste('ShinyGPATree-SNPtable-', Sys.time(), '.csv', sep='')
},
content = function(file) {
write.csv(n2(), file, row.names = FALSE)
})
output$downloadPlot <- shiny::downloadHandler( filename = function() {
paste('ShinyGPATreePlot-cp', round(10^input$log10cp, 3), '.png', sep='')
},
content = function(file) {
grDevices::png(file,
height = pHeight(),
width = pWidth(),
# res = 300,
# bg = "transparent",
units = 'px')
print(plotOutput(log10cp = input$log10cp))
# print(p1())
grDevices::dev.off()
})
}
shiny::shinyApp(ui = ui, server = server)
}
asthakhatiwada/GPATree documentation built on March 8, 2021, 5:29 a.m.
R Package Documentation
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Defines functions ShinyGPATree
Documented in ShinyGPATree
#' Run ShinyGPATree App
#'
#' This function will initialize the ShinyGPATree App for dynamic and interactive visualization of GPA-Tree model results.
#'
#' @author Aastha Khatiwada
#'
#' @param object An object of class GPATree.
#' @return Output of GPA-Tree model.
#' @examples
#' \dontrun{
#' library(GPATree)
#'
#' # load GPATree example data
#' data(GPATreeExampleData)
#'
#' #fitting the GPATree model
#' fit <- GPATree(GPATreeExampleData$gwasPval, GPATreeExampleData$annMat)
#'
#' # initialize the ShinyGPATree app
#' ShinyGPATree(fit)
#' }
#' @export
ShinyGPATree <- function(object){
ui <- shiny::fluidPage(
shiny::tags$head(shiny::tags$style(".checkbox-inline {margin: 0 !important;}")),
# App title ----
shiny::titlePanel("ShinyGPATree"),
shiny::tags$head(shiny::tags$style(shiny::HTML(".shiny-notification {position:fixed; width: 200px; top: 0%; right: 0%;}"))),
# Sidebar layout with input and output definitions ----
shiny::sidebarLayout(
# Sidebar panel for inputs ----
shiny::sidebarPanel(
# Input: Slider for the value of log cp ----
shiny::sliderInput(inputId = "log10cp",
label = "Complexity parameter (cp), log10 scale",
value = -5,
min = -5,
max = 0,
step = 0.002,
width= "95%"),
shiny::helpText("Change cp to further prune the GPA-Tree model fit. To plot the original GPA-Tree model set pointer at -5."),
shiny::hr(),
shiny::h4("Plot options"),
shiny::sliderInput(inputId = "plotHeight",
label="Plot height",
value = 350,
min=100,
max=2100,
step = 50,
width= "95%"),
shiny::sliderInput(inputId = "plotWidth",
label="Plot width",
value = 600,
min=400,
max=4000,
step = 50,
width= "95%"),
shiny::helpText("Change plot dimension using the options above.")
),
# Main panel for displaying outputs ----
shiny::mainPanel( #main panel ####
shiny::tabsetPanel( # tabsetpanel ####
shiny::tabPanel("Plot",
shiny::hr(),
shiny::downloadButton("downloadPlot", label = "Download Plot"),
shiny::htmlOutput('cpconversion'),
shiny::plotOutput(outputId = "distPlot", height = "auto", width = "100%"),
shiny::hr(),
shiny::htmlOutput("anntext"),
shiny::div(DT::dataTableOutput(outputId = "leafInfo", width = "100%"), style = "font-size:80%")
),
shiny::tabPanel("Info",
shiny::wellPanel(
shiny::fluidRow(
shiny::column(5,
shiny::numericInput(inputId = "FDR",
label="False Discovery Rate (FDR) level",
value = 0.01,
min=0,
max=1,
step = 0.01),
shiny::helpText("FDR must be between 0 and 1.")),
shiny::column(4, offset = 2,
shiny::radioButtons(inputId = 'fdrControl',
label = 'Type of FDR',
choices = list('global' = 'global',
'local' = 'local'),
selected = 'global'))
),
shiny::hr(),
shiny::checkboxGroupInput(inputId="associd",
label="Choose association status of SNPs",
choices = list("Non-null" = 1, "Null" = 0),
inline=TRUE,
selected = 1,
width = "80%"),
shiny::checkboxGroupInput(inputId="leafindex",
label="Choose Leaf",
choices = list("LEAF 1" = "LEAF 1",
"LEAF 2" = "LEAF 2",
"LEAF 3" = "LEAF 3"),
inline=TRUE,
width = "80%",
selected = c("LEAF 1")),
shiny::downloadButton("downloadTable", label = "Download SNP Table")
),
shiny::h5(shiny::tags$b("SNP Table")),
shiny::div(shiny::tableOutput(outputId = 'tabletext'), style = "font-size:75%"),
shiny::div(DT::dataTableOutput(outputId = "table", width = "100%"), style = "font-size:80%")
)
)
)
)
)
# column description for SNP Table ####
nt <- function() {
if (ncol(object@fit$Zmarg) == 1){
col1 <- c("SNPID", "local FDR", 'p-value', 'Leaf', "others")
col2 <- c("SNP ID.",
"1 - posterior probability of being non-null for the phenotype based on GPA-Tree model.",
"GWAS association p-value provided to the GPATree() function parameter 'gwasPval'.",
"Leaves in which SNPs fall.",
"Functional annotation information provided to the GPATree() function parameter 'annMat'.")
df_out <- as.data.frame(cbind(col1, col2))
colnames(df_out) <- c(" ","Column description")
}
return(df_out)
}
# defined function assocAll ####
assocAll <- function(FDR, fdrControl, log10cp) {
if (log10cp == -5){
assoc_out <- assoc(object, FDR = FDR, fdrControl = fdrControl)
} else {
GPATreemodel_pruned <- prune(object, cp = 10^log10cp)
assoc_out <- assoc(GPATreemodel_pruned, FDR = FDR, fdrControl = fdrControl)
}
assoc_out <- cbind(assoc_out, 1 - object@fit$Zmarg)
if (ncol(object@fit$Zmarg) == 1){
colnames(assoc_out)[3] <- 'local FDR'
}
if (is.null(rownames(assoc_out))) {
assoc_out <- cbind('SNPID' = paste('SNP_', 1:nrow(assoc_out), sep = ''), assoc_out)
} else {
assoc_out <- cbind('SNPID' = rownames(assoc_out), assoc_out)
}
assoc_out <- cbind(assoc_out, object@gwasPval)
ncol_start_ann <- ncol(assoc_out)
if (ncol(object@fit$Zmarg) == 1){
colnames(assoc_out)[5] <- 'p-value'
}
assoc_out <- cbind(assoc_out, object@annMat)
colnames(assoc_out)[(ncol_start_ann+1):ncol(assoc_out)] <- colnames(object@annMat)
return(assoc_out)
}
# defined function plotOutput ####
plotOutput <- function(log10cp){
if (log10cp == -5){
plot(object)
} else {
GPATreemodel_pruned <- prune(object, cp = 10^log10cp)
plot(GPATreemodel_pruned)
}
}
#defined function leafinfo_output ####
leafinfo_output <- function(object, log10cp){
if (log10cp == -5){
out <- leaf(object)
} else {
GPATreemodel_pruned <- prune(object, cp = 10^log10cp)
out <- leaf(GPATreemodel_pruned)
}
out <- as.data.frame(cbind('Leaf' = rownames(out), out))
# if (nrow(out) == 1){ out$Note <- 'No annotations selected' }
return(out)
}
# server ####
server <- function(input, output, session) {
output$cpconversion <- shiny::renderText({
if (input$log10cp == -5){
paste(" ", "<b> GPA-Tree model<b>", sep="<br/>")
} else {
paste(" ", paste("<b>GPA-Tree model pruned using cp =", round(10^input$log10cp, 5),"<b>"), sep = '<br/>')
}
})
pHeight <- shiny::reactive({
new_height <- input$plotHeight
return(new_height)
})
pWidth <- shiny::reactive({
new_width <- input$plotWidth
return(new_width)
})
n0 <- shiny::reactive({
leafinfo_out <- leafinfo_output(object,
log10cp = input$log10cp)
for (i in 1:ncol(object@fit$Zmarg)) {
leafinfo_out[, i+1] <- formatC(as.numeric(leafinfo_out[, i+1]), format = "E", digits = 2)
}
return(leafinfo_out)
})
output$distPlot <- shiny::renderPlot(height = function()pHeight(), width = function()pWidth(), {
# update of progress bar dependent on number of leaves in the tree
n_index <- n0()
n <- nrow(n_index)
# Create a Progress object
progress <- shiny::Progress$new(session, min = 1, max = 1.1*n)
# Make sure it closes when we exit this reactive, even if there's an error
on.exit(progress$close())
progress$set(message = "Updating plot . . .", value = 0)
for (i in 1:n) {
# Update the progress bar
progress$set(value = i)
# Pause for 0.1 seconds to simulate a long computation.
Sys.sleep(0.1)
}
cex_new <- (pWidth()*pHeight())/(pWidth()*pHeight())
plotOutput(log10cp = input$log10cp)
})
output$anntext <- shiny::renderText({
paste("<b><h5>Leaf Description<h5><b>")
})
output$leafInfo <- DT::renderDataTable({
leafDat <- n0() %>%
DT::datatable(rownames = FALSE,
class = 'cell-border stripe',
options = list(scrollY = "140px",
searching = FALSE,
lengthMenu = c(100, 1000),
scipen = 4,
scrollX = TRUE,
paging = FALSE))
})
n1 <- shiny::reactive({
asso_ans <- assocAll(FDR = input$FDR, fdrControl = input$fdrControl, log10cp = input$log10cp)
return(asso_ans)
})
n2 <- shiny::reactive({
assocOutnew <- n1()
leaf_check <- c()
for (i in 1:length(input$leafindex)) {
leaf_out <- paste(stringr::str_split(input$leafindex[i], " ")[[1]][1],
stringr::str_split(input$leafindex[i], " ")[[1]][2])
leaf_check <- c(leaf_check, leaf_out)
}
select_rows <- c()
for (i in 1:ncol(object@fit$Zmarg)) {
select_rows <- c(select_rows, which(assocOutnew[, i+1] %in% input$associd))
}
assocOutnew <- assocOutnew[select_rows, ]
assocOutnew <- subset(assocOutnew, assocOutnew$leaf %in% leaf_check)
if (ncol(object@fit$Zmarg) == 1){
assocOutnew <- as.data.frame(assocOutnew[, c('SNPID', 'local FDR', 'p-value',
'leaf', colnames(object@annMat))])
assocOutnew <- assocOutnew[order(assocOutnew$`local FDR`), ]
}
for (i in 2:(2*ncol(object@fit$Zmarg)+1)) {
assocOutnew[, i] <- formatC(as.numeric(assocOutnew[, i]), format = "E", digits = 2)
}
colnames(assocOutnew)[2*ncol(object@fit$Zmarg)+2] <- 'Leaf'
return(assocOutnew)
})
output$tabletext <- shiny::renderTable({
tout <- nt()
return(tout)
})
output$table <- DT::renderDT({
# update of progress bar dependent on number of leaves in the tree
n <- 20
# Create a Progress object
progress <- shiny::Progress$new(session, min = 1, max = 1.1*n)
# Make sure it closes when we exit this reactive, even if there's an error
on.exit(progress$close())
progress$set(message = "Updating table . . .", value = 0)
for (i in 1:n) {
# Update the progress bar
progress$set(value = i)
# Pause for 0.1 seconds to simulate a long computation.
Sys.sleep(0.1)
}
dtab <- n2() %>%
DT::datatable(rownames = FALSE,
class = 'cell-border stripe',
options = list(scrollY = "200px",
searching = TRUE,
# dom = 'b',
lengthMenu = c(100, 1000),
scipen = 4,
scrollX = TRUE,
paging = FALSE))
return(dtab)
})
inputVar <- shiny::reactive({
n1_out <- n0()
if (ncol(object@fit$Zmarg) == 1){
n1out_leaf <- paste(n1_out$Leaf,
' (local FDR = ',
formatC(round(as.numeric(n1_out[, 2]), 3), 3, format="f"),
")", sep = '')
}
return(n1out_leaf)
})
shiny::observeEvent(inputVar(), {shiny::updateCheckboxGroupInput(session,
inputId="leafindex",
label = "Choose Leaf",
choices = inputVar(),
selected = inputVar(),
inline = TRUE)} )
output$downloadTable <- shiny::downloadHandler(
filename = function() {
paste('ShinyGPATree-SNPtable-', Sys.time(), '.csv', sep='')
},
content = function(file) {
write.csv(n2(), file, row.names = FALSE)
})
output$downloadPlot <- shiny::downloadHandler( filename = function() {
paste('ShinyGPATreePlot-cp', round(10^input$log10cp, 3), '.png', sep='')
},
content = function(file) {
grDevices::png(file,
height = pHeight(),
width = pWidth(),
# res = 300,
# bg = "transparent",
units = 'px')
print(plotOutput(log10cp = input$log10cp))
# print(p1())
grDevices::dev.off()
})
}
shiny::shinyApp(ui = ui, server = server)
}
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