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waterfallPlot_module.R
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waterfallPlot_module.R
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# UI function for the waterfall plot module
wfPlotUI <- function(id, label = "Gene expression plot parameters"){
ns <- NS(id) # Setting a unique namespace for this module
# Creating a list of dropdown choices for the plot type selection
choices <- as.list(filter(colMapping, Module_Code != "Mutation" & !is.na(Final_Column_Label))$Final_Column_Name)
names(choices) <- filter(colMapping, Module_Code != "Mutation" & !is.na(Final_Column_Label))$Final_Column_Label
# Using tagList() instead of fluidPage() to allow for the ADC/CAR T-cell therapy button to change the tab
tagList(
useShinyjs(),
fluidPage(
theme = shinythemes::shinytheme(theme = "paper"),
###############################################################
#----------------------- SIDEBAR -----------------------------#
###############################################################
sidebarLayout(
position = "left", # Placing the sidebar on the left side of the screen
sidebarPanel(
width = 3,
height = "70vh",
# Dropdown menu to select variable to use for arranging/grouping patients in waterfall plot
selectInput(ns("grouping_var"),
label = "Select a grouping variable", # The name of each list item is what is shown in the box;
choices = choices), # the value corresponds to a column of the CDEs
radioButtons(ns("plot_type"),
label = "Select a type of plot to generate",
choices = list("Waterfall plot" = "wf",
"Box plot" = "bx",
"Strip plot" = "str",
"Scatter plot" = "sctr")),
conditionalPanel(
condition = paste0("input['", ns("plot_type"), "'] == 'sctr'"),
textInput(ns("gene2"),
label = "2nd gene for comparison")),
conditionalPanel(
condition = paste0("input['", ns("plot_type"), "'] == 'bx' || input['", ns("plot_type"), "'] == 'str'"),
checkboxInput(ns("labels"),
label = "Add x-axis labels",
value = FALSE)),
conditionalPanel(
condition = paste0("input['", ns("plot_type"), "'] == 'bx' || input['", ns("plot_type"), "'] == 'str' || input['", ns("plot_type"), "'] == 'sctr'"),
checkboxInput(ns("log"),
label = "Log2 transform the data",
value = FALSE)),
conditionalPanel(
condition = paste0("input['", ns("plot_type"), "'] == 'bx' || input['", ns("plot_type"), "'] == 'str'"),
checkboxInput(ns("test"),
label = "Perform significance tests",
value = FALSE)),
conditionalPanel(
condition = paste0("input['", ns("test"), "'] == 1"),
checkboxGroupInput(ns("comparisons"),
label = "Select 2 groups to compare",
choices = c("A", "B", "C", "D"))), # These are just placeholders and will be replaced
# on the server side with the appropriate categories
helpText("The grouping variable will be used to arrange patients along the x axis (for waterfall plots)
or to group patients together (for box and violin plots) based on a common clinical characteristic
to help highlight expression patterns within the groups."),
helpText("NOTE: If cell lines is selected, please reference the 'Summary Stats' tab for expression data,
as only one sample is available for most cell lines."),
br(),
# http://timelyportfolio.github.io/buildingwidgets/week25/sweetalert_examples.html <- Alert to notify
# user that they're switching tabs
shinyjs::hidden(
actionButton(ns("adc_flag"),
label = "See targeted therapies",
class = "btn-primary")
# width = "50%")
),
br(),
br(),
br(),
downloadButton(ns("plot_download"),
label = "plot",
class = "plotdwnld"),
shinyBS::bsTooltip(ns("plot_download"),
title = "Click here to download a copy of the plot",
placement = "right",
trigger = "hover"),
br(),
br(),
br(),
# Adds a help button to identify acronyms for disease types in cohorts
actionButton(ns("key_button"),
label = "Sample Key",
icon = icon("info-circle"),
class = "btn-primary",
disabled = FALSE),
shinyBS::bsTooltip(ns("key_button"),
title = "Click for a Sample Type Key",
placement = "right",
trigger = "hover"),
br(),
br(),
),
###############################################################
#----------------------- MAIN PLOT PANEL ---------------------#
###############################################################
mainPanel(
position = "right",
tabsetPanel(
#-------------------- Waterfall plot -----------------------#
tabPanel("Plot", # This is the title of the tab panel, NOT the name of the plot object!
br(),
br(), # Linebreaks to help center the plot on the page
fluidRow(
column(11, offset = 0, align = "left", # This will be a reactive object that is linked to an item in the
plotlyOutput(ns("plot"), height = "60vh") # output list, created in the "server" script
)
)
),
#-------------------- Summary table -----------------------#
tabPanel("Summary stats",
br(),
br(),
fluidRow(
column(12, offset = 0, align = "left",
DT::dataTableOutput(ns("table")))
)
)
)
)
)
)
)
}
# Server function for the waterfall plot module
wfPlot <- function(input, output, session, clinData, expData, adc_cart_targetData, gene, dataset, parent){
library(ggpubr)
# bs <- 17 # Base font size for figures
# Making the gene2 input non-case sensitive
observeEvent(input$gene2, {
newValue <- toupper(input$gene2)
updateTextInput(session, "gene2", value = newValue)
})
#################################################################
#-------------------- DATA PREPARATION -------------------------#
#################################################################
# Setting up a list of grouping variables that are available for each dataset.
dropdown_choices <- filter(colMapping, Module_Code != "Mutation" & !is.na(Final_Column_Label))$Final_Column_Name
names(dropdown_choices) <- filter(colMapping, Module_Code != "Mutation" & !is.na(Final_Column_Label))$Final_Column_Label
# Some dropdown choices are not available for all datasets - this function will filter the options
# depending on which dataset the user has selected.
disabled_choices <- reactive({
x <- filter(colMapping, Module_Code != "Mutation" & is.na(!!sym(dataset())))$Final_Column_Name
names(x) <- filter(colMapping, Module_Code != "Mutation" & is.na(!!sym(dataset())))$Final_Column_Label
return(x)
})
# Updating the options for significance testing to reflect the
# grouping variable chosen by the user.
observe({
x <- unique(plotData()[[input$grouping_var]])
x <- x[!is.na(x)]
updateCheckboxGroupInput(session,
inputId = "comparisons",
label = "Select 2 of the following to compare",
choices = x)
})
# Updating plot dropdown options based on the dataset selected by the user
observeEvent(dataset(), {
updateSelectInput(
session = session,
inputId = "grouping_var",
choices = dropdown_choices[!dropdown_choices %in% disabled_choices()])
}, ignoreInit = T)
observeEvent(input$key_button, {
showModal(
modalDialog(
title = "Sample Type Key",
HTML(
paste(
"AML: Acute Myeloid Leukemia",
"CB: Cord Blood",
"CD34+ PB: CD34+ Peripheral Blood",
"DS: Down Syndrome AML",
"MPN: Myeloproliferative Neoplasm",
"NBM: Normal Bone Marrow",
"NBM Lymph Neg: Myeloid Sorted Normal Bone Marrow",
"NBM Lymph Pos: Lymphoid Sorted Normal Bone Marrow",
"TMD: Transient Myeloproliferative Disorder",
sep = "<br>")
),
easyClose = TRUE)
)
})
#################################################################
#------------------------- FUNCTIONS ---------------------------#
#################################################################
reLevel_cols <- function(col) {
new_col <- col
if (any(grepl("Other AML", col))) {
new_col <- forcats::fct_relevel(new_col, "Other AML", after = Inf)
}
if (any(grepl("No Relevant CNV", col))) {
new_col <- forcats::fct_relevel(col, "No Relevant CNV", after = Inf)
}
return(new_col)
}
# Filtering the ADC & CAR T-cell therapy data to select therapies targeting the gene of interest.
# This will be used for the conditional button that links to the therapeutic database tab.
therapyData <- reactive({
filter(adc_cart_targetData, `Gene target` == gene())
})
# Filtering the counts data to only retain the gene of interest.
# An error will be thrown if non-existent or unrecognized gene is provided.
geneData <- reactive({
validate(
need(gene(), "Please enter a gene symbol or miRNA in the text box to the left.") %then%
need(gene() %in% rownames(expData()), paste0(gene(), " does not exist in the counts data!\nDouble-check the symbol or ID, or try an alias/synonym."))
)
# Requests entry of another gene symbol ONLY when the input plot type is a scatter plot
if (input$plot_type == "sctr" && input$gene2 == "") {
validate("Please enter a 2nd gene symbol or miRNA in the new text box.")
}
if (input$gene2 == gene()) {
validate("Please enter a different 2nd gene symbol.")
}
# The default value for an empty text entry box = ""
genes2keep <- if (input$plot_type == "sctr" & input$gene2 != "") {
c(gene(), input$gene2)
} else if (input$plot_type != "sctr") {
gene()
}
df <- expData() %>%
rownames_to_column("Gene") %>%
filter(Gene %in% genes2keep) %>%
dplyr::select(Gene, any_of(intersect(clinData()$PatientID, colnames(expData()))))
return(df)
})
# Transforming the counts into a long-format dataframe (to use with ggplot).
plotData <- reactive({
# Should prevent the user from selecting "Malignancy" or "Tissue" as the grouping variable for the initialized TARGET dataset
# Needed to add in some additional checks as it is the case that when the user has a selected filter and then switches datasets
# it might not be available for the new dataset
validate(
need(!((dataset() %in% c("TARGET", "BeatAML", "SWOG", "TCGA", "StJude", "GMKF", "CCLE")) && (input$grouping_var %in% disabled_choices())), "That grouping option is not available for this dataset.\nPlease select another option."))
plotDF <- geneData() %>%
pivot_longer(names_to = "PatientID", values_to = "Expression", -Gene) %>%
drop_na(Expression) %>% # Removing samples without expression data from the dataset
mutate(across(Expression, ~as.numeric(.))) %>%
left_join(., clinData(), by = "PatientID") %>%
mutate(Log2 = log2(Expression + 1))
# Modifying the chosen grouping variable to keep the NBMs and PBs from being categorized as NA.
# This keeps them on the plot - if they're recategorized as NA, they will be removed from the final plot.
plotDF <- plotDF %>%
mutate_at(vars(any_of(!!input$grouping_var), -one_of("Age.Category")), ~case_when(Disease.Group == "NBM" ~ "NBM",
Disease.Group == "NBM, Lymph Neg" ~ "NBM, Lymph Neg",
Disease.Group == "NBM, Lymph Pos" ~ "NBM, Lymph Pos",
Disease.Group == "CB" ~ "CB",
Disease.Group == "CD34+ PB" ~ "CD34+ PB",
TRUE ~ .)) %>%
mutate(across(any_of(c("MLL.Fusion", "Rare.Fusion", "Primary.Fusion", "SNVs", "Primary.CNV")), ~reLevel_cols(.)))
# Define the order of the grouping variable levels for the TARGET cohort
if (dataset() == "TARGET") {
plotDF <- plotDF %>%
mutate_at(vars(Cell.Line), ~forcats::fct_relevel(., "AML", after = Inf)) %>%
mutate_at(vars(any_of(!!input$grouping_var), -one_of("Age.Category")), ~forcats::fct_relevel(., "MPN", after = Inf)) %>%
mutate_at(vars(any_of(!!input$grouping_var), -one_of("Age.Category")), ~forcats::fct_relevel(., "DS", after = Inf)) %>%
mutate_at(vars(any_of(!!input$grouping_var), -one_of("Age.Category")), ~forcats::fct_relevel(., "TMD", after = Inf)) %>%
mutate_at(vars(any_of(!!input$grouping_var), -one_of("Age.Category")), ~forcats::fct_relevel(., "Cell line", after = Inf)) %>%
mutate_at(vars(any_of(!!input$grouping_var), -one_of("Age.Category")), ~forcats::fct_relevel(., "CB", after = Inf)) %>%
mutate_at(vars(any_of(!!input$grouping_var), -one_of("Age.Category")), ~forcats::fct_relevel(., "CD34+ PB", after = Inf)) %>%
mutate_at(vars(any_of(!!input$grouping_var), -one_of("Age.Category")), ~forcats::fct_relevel(., "NBM", after = Inf)) %>%
mutate_at(vars(any_of(!!input$grouping_var), -one_of("Age.Category")), ~forcats::fct_relevel(., "NBM, Lymph Neg", after = Inf)) %>%
mutate_at(vars(any_of(!!input$grouping_var), -one_of("Age.Category")), ~forcats::fct_relevel(., "NBM, Lymph Pos", after = Inf))
}
plotDF <- plotDF %>%
# group_by(!!input$grouping_var) %>% # Don't think this is needed....
arrange_(input$grouping_var, "Expression") # Reordering patients so that the specified groups are
# grouped together and ordered by increasing expression.
if (input$plot_type == "wf") {
# Setting the patient order using factor levels, so they won't be rearranged
# alphabetically by ggplot (this step is required for waterfall plots made w/ ggplot)
plotDF$PatientID <- factor(plotDF$PatientID, levels = plotDF$PatientID)
}
return(plotDF)
})
#----------------- Plot generation function -------------------#
plotFun <- reactive({
if (any(grepl(gene(), c(miRmapping$Alias, miRmapping$hsa.ID.miRbase21)))) {
units <- "RPM"
} else {
units <- "TPM"
}
# Selecting units to display on the y-axis
if (input$log == TRUE) {
expCol <- "Log2"
yaxLab <- paste0("\n", gene(), " Expression (log2 ", units, " + 1)\n") # The extra newline is to keep x-axis labels
} else { # from running off the side of the plot
expCol <- "Expression"
yaxLab <- paste0("\n", gene(), " Expression (", units, ")\n")
}
# Customizing the x-axis labels based on user input
xaxLabs <- if (input$labels == TRUE) {
element_text(hjust = 1, vjust = 1, angle = 90)
} else {
element_blank()
}
# Removes the plot legend if you turn on x-axis labels
plotLegend <- ifelse(input$labels == TRUE, FALSE, TRUE)
if (input$plot_type == "bx") { # Generating box plots
p <- plotData() %>%
drop_na(input$grouping_var) %>%
ggplot(aes_string(x = input$grouping_var, y = expCol, fill = input$grouping_var)) +
theme_classic(base_size = bs) +
labs(x = NULL, y = yaxLab, fill = gsub("\\.", " ", input$grouping_var)) +
theme(axis.title.y = element_text(size = bs),
axis.text.x = xaxLabs,
axis.text.y = element_text(size = bs),
plot.title = element_text(size = bs + 2, hjust = 0.5),
legend.position = "bottom",
legend.text = element_text(size = bs - 5),
legend.title = element_blank()) +
geom_boxplot(width = 0.2, outlier.shape = NA, aes_string(fill = input$grouping_var), color = "black") +
guides(color = "none")
# Try to convert to a plotly plot with interactive tooltips
p <- ggplotly(p, tooltip = c("y", "color"), dynamicTicks = TRUE)
# Adjust legend position in plotly
p <- layout(p,
showlegend = plotLegend,
legend = list(orientation = "h",
y = -0.1,
x = 0.5,
xanchor = "center"
),
xaxis = list(tickfont = list(size = bs)))
p
} else if (input$plot_type == "str") { # Generating strip plots w/ jittered raw data points
p <- plotData() %>%
drop_na(input$grouping_var) %>%
ggplot(aes_string(x = input$grouping_var, y = expCol, fill = input$grouping_var, color = input$grouping_var)) +
theme_classic(base_size = bs) +
geom_jitter(width = 0.2, height = 0) +
labs(x = NULL, y = yaxLab, fill = gsub("\\.", " ", input$grouping_var)) +
theme(axis.title.y = element_text(size = bs),
axis.text.x = xaxLabs,
axis.text.y = element_text(size = bs),
plot.title = element_text(size = bs + 2, hjust = 0.5),
legend.position = "bottom",
legend.text = element_text(size = bs - 6),
legend.title = element_blank()) +
guides(color = "none")
# Try to convert to a plotly plot with interactive tooltips
p <- ggplotly(p, tooltip = c("y", "color"), dynamicTicks = TRUE)
# Adjust legend position in plotly
p <- layout(p,
showlegend = plotLegend,
legend = list(orientation = "h",
y = -0.1,
x = 0.5,
xanchor = "center"
))
p
} else if (input$plot_type == "wf") { # Generating a waterfall plot
p <- plotData() %>%
drop_na(input$grouping_var) %>%
ggplot(aes_string(x = "PatientID", y = "Expression", fill = input$grouping_var)) +
theme_classic(base_size = bs) +
labs(x = NULL, y = yaxLab, fill = gsub("\\.", " ", input$grouping_var)) +
theme(axis.text.x = element_blank(),
axis.text.y = element_text(size = bs),
plot.title = element_text(size = bs + 2, hjust = 0.5),
axis.ticks.x = element_blank(),
legend.position = "bottom",
legend.text = element_text(size = bs - 6),
legend.title = element_blank()) +
geom_bar(stat = "identity", width = 1, position = position_dodge(width = 0.4))
# Try to convert to a plotly plot with interactive tooltips
p <- ggplotly(p, dynamicTicks = TRUE)
# Adjust legend position in plotly
p <- layout(p,
legend = list(orientation = "h",
y = -0.1,
x = 0.5,
xanchor = "center"
))
p
} else if (input$plot_type == "sctr") { # Generating a scatter plot
p <- plotData() %>%
drop_na(input$grouping_var) %>%
filter(Disease.Group == c("AML")) %>%
ungroup() %>%
dplyr::select(PatientID, Gene, Expression, !!sym(input$grouping_var)) %>%
pivot_wider(names_from = "Gene", values_from = "Expression")
if (input$log == TRUE) {
p <- p %>%
mutate(expCol_1 = log2(!!sym(gene()) + 1),
expCol_2 = log2(!!sym(input$gene2) + 1))
xaxLab <- paste0("\n", gene(), " (log2 ", units, " + 1)")
yaxLab <- paste0(input$gene2, " (log2 ", units, " + 1)\n")
} else {
xaxLab <- paste0("\n", gene(), " (", units, ")")
yaxLab <- paste0(input$gene2, " (", units, ")\n")
p <- p %>%
rename(expCol_1 = !!sym(gene()),
expCol_2 = !!sym(input$gene2))
}
# Calculate correlation and p-value
correlation <- cor.test(p$expCol_1, p$expCol_2, method = "pearson")
p_value <- round(correlation$p.value, 6)
p <- p %>%
ggpubr::ggscatter(., x = "expCol_1", y = "expCol_2",
cor.coef = FALSE,
cor.coef.size = 7,
cor.method = "spearman",
xlab = xaxLab,
ylab = yaxLab,
size = 2,
add = "reg.line",
conf.int = TRUE,
color = input$grouping_var,
add.params = list(color = "black", fill = "grey90", size = 1)) +
guides(color = guide_legend(override.aes = list(size = 5, shape = 16))) +
theme(axis.title = element_text(size = bs),
axis.text = element_text(size = bs),
legend.text = element_text(size = bs - 5),
legend.title = element_blank(),
legend.position = "bottom")
p <- ggplotly(p, dynamicTicks = TRUE)
# Add p-value annotation
p <- p %>%
layout(annotations = list(
x = 1,
y = 1,
text = paste("p-value =", p_value),
xref = "paper",
yref = "paper",
showarrow = FALSE,
font = list(size = 22)
))
# Adjust legend position in plotly
p <- layout(p,
legend = list(orientation = "h",
y = -0.2,
x = 0.5,
xanchor = "center"
))
p
}
### Perform Statistical Comparison ###
if (length(input$comparisons) > 1) {
validate(
need(length(input$comparisons > 1), "Please select 2 groups to compare."))
# c <- ggpubr::stat_compare_means(method = "wilcox.test", comparisons = list(input$comparisons)) ### This no longer works when using ggplotly (fails to recognize added geom)
### Manually generate the comparison and add to plot ###
# Sort plot data for the comparitors of interest
df <- plotData() %>%
drop_na(input$grouping_var) %>%
filter(!!as.name(input$grouping_var) %in% input$comparisons) %>%
ungroup()
# Create the formula
grouping_var <- input$grouping_var
formula <- as.formula(paste0("Expression ~ ", grouping_var))
test <- compare_means(formula = formula, data = df, method = "wilcox.test")
p_value <- test$p
# Add the result from the statistical test as an annotation to the plot
y = ifelse(input$log == TRUE, max(plotData()$Log2) + 1, max(plotData()$Expression) + 1)
p <- p %>% add_annotations(
text = paste("p-value:", format(p_value, digits = 3)),
x = 1.5, y = y,
showarrow = FALSE
)
} else {
p # Return the plot as-is with no additional geom layers
}
})
#----------------- Summary table function -------------------#
# Function to generate an expression summary table from the plot data
### Needs to be treated a little differently for cell line data, we don't want to group by Disease.Group
### Instead want to plot all cell lines individually for sorting. E.g., don't use Disease.Group as a grouping variable.
# Function to generate an expression summary table from the plot data
tableFun <- reactive({
plotData() %>%
drop_na(input$grouping_var) %>%
{
if (dataset() == "CCLE" && input$grouping_var == "Disease.Group") {
group_by(., Name)
} else {
group_by(., !!as.name(input$grouping_var))
}
} %>%
dplyr::summarize(N = n(),
Gene = gene(),
`Mean (TPM)` = round(mean(Expression, na.rm = T), 2),
`Median (TPM)` = round(median(Expression, na.rm = T), 2),
`Range (TPM)` = paste0(round(min(Expression), 2), " - ", round(max(Expression), 2)),
`N >= 5 (TPM)` = sum(Expression >= 5, na.rm = T),
`% >= 5 (TPM)` = round(sum(Expression >= 5, na.rm = T) / n() * 100, 2),
.groups = "keep")
})
#################################################################
#-------------------- FINAL MODULE OUTPUTS ---------------------#
#################################################################
#-------------------- Plot tab -----------------------#
# Saving the plot to the output list object so it can be run & saved reactively
output$plot <- renderPlotly({
# Create the plot
plotFun()
})
# Adding a download button widget for the plot
output$plot_download <- downloadHandler(
filename = function() {
paste0(dataset(), "_AML_", gene(), "_", input$grouping_var, "_", input$plot_type, "_generated_", format(Sys.time(), "%m.%d.%Y"), ".png")
},
content = function(file) {
ggsave(filename = file, plot = plotFun(), width = 6, height = 4.5, device = "png", dpi = 250)
}
)
output$ggplot_download <- downloadHandler(
filename = function() {
paste0(dataset(), "_AML_", gene(), "_", input$grouping_var, "_ggplotObject_generated_", format(Sys.time(), "%m.%d.%Y"), ".RDS")
},
content = function(file) {
withProgress(message = "Preparing RDS file", detail = "This may take a while...", value = 0, {
for (i in 1:50) {
incProgress(1/70)
Sys.sleep(0.25)
}
})
saveRDS(object = plotFun(), file = file, compress = F)
}
)
#-------------------- Data tab -----------------------#
# https://glin.github.io/reactable/articles/examples.html#conditional-styling
output$table <- DT::renderDataTable({
DT::datatable(tableFun(),
class = "compact nowrap hover row-border order-column", # Defines the CSS formatting of the final table, can string multiple options together
callback = DT::JS("$('table.dataTable.no-footer').css('border-bottom', 'none');"),
extensions = 'Buttons', # See https://rstudio.github.io/DT/extensions.html for more extensions & features
options = list(scrollY = "70vh",
dom = 'Bfrtip',
buttons = list(
list(extend = 'excel', filename = paste0(dataset(), "_AML_", gene(), "_Summary_Table_generated_", format(Sys.time(), "%m.%d.%Y")))),
scrollX = TRUE,
searchHighlight = TRUE,
pageLength = 5000
),
escape = F) %>%
DT::formatStyle(columns = c(1,2,4), fontSize = "100%")
})
# This will hide the ADC/CAR T action buttons if the gene isn't the target of any clinical trials
observeEvent(gene(), {
if (gene() %in% therapyData()$`Gene target`) {
shinyjs::show("adc_flag")
}
})
# The server side of the ADC/CAR T action button that will switch the user
# when clicked
observeEvent(input$adc_flag, {
newval <- "extData"
updateTabItems(session = parent, "sdbr", selected = newval)
})
}