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app.R
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app.R
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## quicKM web app
## Lewis Quayle
## 21/01/2022
### GLOBAL
## load libraries
library("bslib")
library("cowplot")
library("dplyr")
library("ggplot2")
library("readr")
library("shiny")
library("shinyWidgets")
library("stringr")
library("survival")
library("survminer")
library("summaryBox")
library("tidyr")
## load data
load(file.path("www", "tcga_brca.RData"))
## define utility functions
# filter counts data
filter_count_data <- function(clinical.data, expr.data, annotation, genes.input) {
# extract vector of case ids from filtered clinical data
case.ids <- pull(clinical.data, id)
# switch annotation type
annotation <- switch(annotation,
"Ensembl ID" = "ensembl",
"Entrez ID" = "entrez",
"HUGO Symbol" = "symbol")
# create a character vector used to filter count data rows
if (genes.input == "") {
genes <- NULL
} else {
genes <- genes.input %>%
strsplit(split = paste(",", ";", "\\s", sep = "|")) %>%
unlist() %>%
toupper()
}
# filter gene level expression data
expr.data %>%
dplyr::select(ensembl, entrez, symbol, all_of(case.ids)) %>%
dplyr::filter(get(annotation) %in% genes)
}
# calculate geometric mean
calc_geo_mean <- function(x) {
x %>%
log() %>%
mean() %>%
exp()
}
# generate survival data frame
generate_survival_data <- function(clinical.data, expr.data, split.cohort, censor.threshold, threshold) {
# only calculate with data frame resulting from valid genes input
req(nrow(expr.data) > 0)
# create an expression matrix
expr.data <- expr.data %>%
dplyr::select(-c(ensembl, entrez, symbol))
# extract vector of expression values for a single gene or compute the geometric mean for multiple genes
expr.value <- sapply(expr.data, FUN = calc_geo_mean)
# create the data frame used in survival analysis
surv.data <- data.frame("case.id" = names(expr.value),
"os_status" = clinical.data$os_status,
"os_time" = clinical.data$os_time,
"expr.value" = expr.value,
row.names = NULL)
# order by increasing gene / signature expression value
surv.data <- arrange(surv.data, expr.value)
# calculate quantiles in expression data
quant <- quantile(x = surv.data$expr.value, probs = seq(from = 0, to = 1, by = 0.25))
# define the quantile cutoff according to user input
cutoff <- switch(split.cohort, "lower quartile" = quant[2], "median" = quant[3], "upper quartile" = quant[4])
# binary stratum classifier variable
surv.data <- surv.data %>%
mutate(stratum = ifelse(surv.data$expr.value < cutoff, 0, 1))
# define follow-up period
if (censor.threshold) {
surv.data %>%
mutate(os_status = ifelse(os_time <= threshold, os_status, 0))
} else {
surv.data %>%
dplyr::filter(os_time <= threshold)
}
}
# clinical data plots
plot_clinical_svar <- function(data, x.var, x.lab) {
ggplot(data = data, aes_string(x = x.var)) +
geom_bar(colour = "#4D4D4D", fill = "#1E90FF") +
theme_bw() +
theme(aspect.ratio = 3/4,
plot.background = element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "cm"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.title.x = element_text(size = 14, face = "bold", margin = margin(10, 0, 10, 0)),
axis.title.y = element_text(size = 14, face = "bold", margin = margin(0, 15, 0, 15)),
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12)) +
labs(x = x.lab, y = "Number of Cases")
}
plot_clinical_mvar <- function(data, x.var, grp.var, x.lab, grp.lab) {
ggplot(data = data, aes_string(x = x.var, alpha = grp.var)) +
geom_bar(position = "dodge", colour = "#4D4D4D", fill = "#1E90FF") +
scale_alpha_manual(values = c(1, 0.6, 0.3)) +
theme_bw() +
theme(aspect.ratio = 3/4,
plot.background = element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "cm"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.title.x = element_text(size = 14, face = "bold", margin = margin(10, 0, 10, 0)),
axis.title.y = element_text(size = 14, face = "bold", margin = margin(0, 15, 0, 15)),
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
legend.title = element_text(face = "bold", size = 12),
legend.text = element_text(size = 10),
legend.background = element_rect(colour = "#4D4D4D")) +
labs(x = x.lab, y = "Number of Cases", alpha = grp.lab)
}
# survival analysis plot
plot_survival <- function(data, title, pval, conf.int, censor, surv.median.line, threshold) {
# custom plot theme
theme_survival <- function() {
theme_bw() +
theme(plot.background = element_blank(),
plot.title = element_text(size = 18, face = "bold"),
panel.grid.major = element_line(size = rel(0.5)),
panel.grid.minor = element_line(size = rel(0.25)),
axis.title.x = element_text(size = 14, face = "bold", margin = margin(10, 0, 10, 0)),
axis.title.y = element_text(size = 14, face = "bold", margin = margin(0, 15, 0, 15)),
axis.text = element_text(size = 12),
axis.ticks = element_line(size = rel(0.5)),
legend.title = element_text(size = 14, face = "bold"),
legend.text = element_text(size = 12),
legend.background = element_rect(colour = "#4D4D4D"))
}
# custom risk table theme
theme_rt <- function() {
theme_survival() +
theme(plot.title = element_text(size = 16, face = "bold"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
}
# compute survival curves using Kaplan-Meier model formula
fit <- survfit(data = data, formula = Surv(time = os_time, event = os_status) ~ stratum)
# generate plot
srvplot <- ggsurvplot(fit = fit,
data = data,
pval = pval,
pval.size = 4.5,
pval.coord = c(1, 0.1),
conf.int = conf.int,
censor = censor,
risk.table = FALSE,
surv.median.line = switch(surv.median.line, "TRUE" = "hv", "FALSE" = "none"),
ggtheme = theme_survival(),
palette = c("#053061", "#E41A1C"),
axes.offset = TRUE,
break.time.by = 1,
xlim = c(0, threshold),
ylim = c(0, 1),
break.y.by = 0.10,
title = title,
xlab = "Time (Years)",
ylab = "Overall Survival Probability",
legend = "right",
legend.title = "Group",
legend.labs = c("Low", "High"))
srvplot <- srvplot[[1]]
# generate risk table
risktab <- ggsurvplot(fit = fit,
data = data,
risk.table = TRUE,
ggtheme = theme_rt(),
fontsize = 4,
palette = c("#053061", "#E41A1C"),
break.time.by = 1,
xlim = c(0, threshold),
xlab = "Time (Years)",
risk.table.title = "Number at Risk",
legend.title = "",
legend.labs = c("Low", "High"))
risktab <- risktab[[2]]
# align on grid
plot_grid(srvplot,
risktab,
ncol = 1,
align = "v",
axis = "lr",
rel_heights = c(8,2))
}
### USER-INTERFACE
ui <- navbarPage(
# app title
titlePanel(title = div(img(src = "logo.png", width = 180)), windowTitle = "quicKM: Speedy Survival Analysis"),
# theme
theme = bs_theme(bootswatch = "cerulean"),
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
## HOME PAGE
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
tabPanel(title = "Home",
icon = icon("home"),
includeMarkdown(file.path("www", "homepage.md")),
setBackgroundImage("site_bg.png")),
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
## BREAST CANCER
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
navbarMenu(title = "Breast", icon = icon("fas fa-project-diagram"),
## ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
## TCGA-BRCA
## ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
tabPanel(title = "TCGA-BRCA", icon = icon("far fa-folder"),
tabsetPanel(
## -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
## TCGA-BRCA - CLNICAL DATA
## -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
tabPanel(title = "Clinical Data",
sidebarLayout(
## SIDEBAR
sidebarPanel(
pickerInput(inputId = "race_tcga_brca",
label = "Race or Ethnicity:",
choices = levels(tcga_brca_clinical$race),
multiple = TRUE,
options = list(title = "select one or more")),
pickerInput(inputId = "menopause_status_tcga_brca",
label = "Menopausal Status:",
choices = levels(tcga_brca_clinical$menopause_status),
multiple = TRUE,
options = list(title = "select one or more")),
pickerInput(inputId = "histological_type_tcga_brca",
label = "Histological Type:",
choices = levels(tcga_brca_clinical$histological_type),
multiple = TRUE,
options = list(title = "select one or more")),
pickerInput(inputId = "pathological_stage_tcga_brca",
label = "Pathological Stage:",
choices = levels(tcga_brca_clinical$stage),
multiple = TRUE,
options = list(title = "select one or more")),
pickerInput(inputId = "er_status_tcga_brca",
label = "Oestrogen Receptor (ER) Status:",
choices = levels(tcga_brca_clinical$er_status),
multiple = TRUE,
options = list(title = "select one or more")),
pickerInput(inputId = "pr_status_tcga_brca",
label = "Progesterone Receptor (PR) Status:",
choices = levels(tcga_brca_clinical$pr_status),
multiple = TRUE,
options = list(title = "select one or more")),
pickerInput(inputId = "her2_status_tcga_brca",
label = "HER2 Status:",
choices = levels(tcga_brca_clinical$her2_status),
multiple = TRUE,
options = list(title = "select one or more")),
actionButton(inputId = "ab_1_tcga_brca",
label = "Confirm Selection",
icon = icon("far fa-check-circle"),
style = "width:100%"),
uiOutput("dl_1_tcga_brca")
), # end sidebarPanel
## MAIN PANEL
mainPanel(
uiOutput("brca_summarybox", style = "margin-top:2em"),
fluidRow(
column(width = 12, style = "margin-top:2em", plotOutput("clinical_plots_tcga_brca"))
)
) # end mainPanel
) # end sidebarLayout
), # end tabPanel
## -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
## TCGA-BRCA - SURVIVAL ANALYSIS
## -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
tabPanel(title = "Survival Analysis",
sidebarLayout(
# SIDEBAR
sidebarPanel(
prettyRadioButtons(
inputId = "annotation_tcga_brca",
label = "Gene Annotation Type:",
choices = c("Ensembl ID", "Entrez ID", "HUGO Symbol"),
selected = "HUGO Symbol",
inline = TRUE,
status = "primary",
fill = TRUE
),
fluidRow(
column(width = 10,
style = "display:inline-block;vertical-align:top",
textAreaInput(inputId = "gene_filter_tcga_brca",
label = "Gene or Gene List:",
rows = 3)),
column(width = 2,
style = "display:inline-block;vertical-align:top",
circleButton(inputId = "genes_help_tcga_brca",
size = "xs",
color = "primary",
icon = icon("question")))
),
pickerInput(inputId = "define_cutoff_tcga_brca",
label = "Split Patient Cohort By:",
choices = c("lower quartile", "median", "upper quartile"),
selected = "median"),
fluidRow(
column(width = 10,
style = "display:inline-block;vertical-align:top",
sliderInput(inputId = "define_time_tcga_brca",
label = "Follow-Up Threshold:",
min = 2,
max = floor(max(tcga_brca_clinical$os_time)),
step = 1,
value = floor(max(tcga_brca_clinical$os_time)))),
column(width = 2,
style = "display:inline-block;vertical-align:top",
circleButton(inputId = "followup_help_tcga_brca",
size = "xs",
color = "primary",
icon = icon("question")))
),
materialSwitch(inputId = "censor_threshold_tcga_brca",
label = "Censor at Threshold:",
value = TRUE,
status = "primary"),
textInput(inputId = "plot_title_tcga_brca",
label = "Plot Title:",
width = "100%"),
materialSwitch(inputId = "show_conf_tcga_brca",
label = "Confidence Intervals:",
value = FALSE,
status = "primary"),
materialSwitch(inputId = "med_surv_line_tcga_brca",
label = "Median Survival Line:",
value = FALSE,
status = "primary"),
materialSwitch(inputId = "censor_tcga_brca",
label = "Censor Points:",
value = TRUE,
status = "primary"),
materialSwitch(inputId = "show_pval_tcga_brca",
label = "P-Value:",
value = TRUE,
status = "primary"),
actionButton(inputId = "ab_2_tcga_brca",
label = "Draw KM Plot",
icon = icon("fas fa-chart-line"),
style = "width:100%"),
fluidRow(
column(6, uiOutput("dl_2_tcga_brca")),
column(6, uiOutput("dl_3_tcga_brca"))
),
uiOutput("dl_4_tcga_brca")
), # end sidebarPanel
# MAIN PANEL
mainPanel(
plotOutput("tcga_brca_survival"),
style = "margin-top:1em"
) # end mainPanel
) # end sidebarLayout
) # end tabPanel
) # end tabsetPanel
), # end tabPanel
## ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
## STUDY 2
## ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
tabPanel(title = "UNDER DEVELOPMENT", icon = icon("far fa-folder"),
sidebarLayout(
sidebarPanel(
),
mainPanel(
)
)
) # end of tabset
), # end navbarMenu
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
## PROSTATE CANCER
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
navbarMenu(title = "Prostate", icon = icon("fas fa-project-diagram"),
# STUDY 1
tabPanel(title = "UNDER DEVELOPMENT", icon = icon("far fa-folder"),
sidebarLayout(
sidebarPanel(
),
mainPanel(
#imageOutput("under_development")
)
)
),
# STUDY 2
tabPanel(title = "UNDER DEVELOPMENT", icon = icon("far fa-folder"),
sidebarLayout(
sidebarPanel(
),
mainPanel(
)
)
)
), # end of prostate studies
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
## LUNG CANCER
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
navbarMenu(title = "Lung", icon = icon("fas fa-project-diagram"),
# STUDY 1
tabPanel(title = "UNDER DEVELOPMENT", icon = icon("far fa-folder"),
sidebarLayout(
sidebarPanel(
),
mainPanel(
)
)
),
# STUDY 2
tabPanel(title = "UNDER DEVELOPMENT", icon = icon("far fa-folder"),
sidebarLayout(
sidebarPanel(
),
mainPanel(
)
)
)
), # end of lung studies
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
## COLON CANCER
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
navbarMenu(title = "Colon", icon = icon("fas fa-project-diagram"),
# STUDY 1
tabPanel(title = "UNDER DEVELOPMENT", icon = icon("far fa-folder"),
sidebarLayout(
sidebarPanel(
),
mainPanel(
)
)
),
# STUDY 2
tabPanel(title = "UNDER DEVELOPMENT", icon = icon("far fa-folder"),
sidebarLayout(
sidebarPanel(
),
mainPanel(
)
)
)
), # end of colon studies
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
## FAQ PAGE
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
tabPanel(title = "FAQ", icon = icon("far fa-question-circle"),
includeMarkdown(file.path("www", "faq.md"))
),
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
## ABOUT PAGE
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
tabPanel(title = "About", icon = icon("fas fa-book"),
includeMarkdown(file.path("www", "about.md"))
)
) # end navbarPage
### SERVER
server <- function(input, output) {
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
## START-UP MODAL
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# showModal(modalDialog(title = "Welcome to quicKM",
# size = "xl",
# fade = TRUE,
# "PLACE HOLDER FOR USER DEMO",
# footer = modalButton("Get Started"),
# easyClose = TRUE))
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
## TCGA-BRCA - CLINICAL DATA
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
## filter clinical data
clinical_selection <- eventReactive(input$ab_1_tcga_brca, {
# race
fil_race <- if (is.null(input$race_tcga_brca)) {
tcga_brca_clinical %>%
pull(race) %>%
unique()
} else {
input$race_tcga_brca
}
# menopause status
fil_menopause <- if (is.null(input$menopause_status_tcga_brca)) {
tcga_brca_clinical %>%
pull(menopause_status) %>%
unique()
} else {
input$menopause_status_tcga_brca
}
# histological type
fil_histological <- if (is.null(input$histological_type_tcga_brca)) {
tcga_brca_clinical %>%
pull(histological_type) %>%
unique()
} else {
input$histological_type_tcga_brca
}
# pathological stage
fil_pathological <- if (is.null(input$pathological_stage_tcga_brca)) {
tcga_brca_clinical %>%
pull(stage) %>%
unique()
} else {
input$pathological_stage_tcga_brca
}
# er status
fil_er <- if (is.null(input$er_status_tcga_brca)) {
tcga_brca_clinical %>%
pull(er_status) %>%
unique()
} else {
input$er_status_tcga_brca
}
# pr status
fil_pr <- if (is.null(input$pr_status_tcga_brca)) {
tcga_brca_clinical %>%
pull(pr_status) %>%
unique()
} else {
input$pr_status_tcga_brca
}
# her2 status
fil_her2 <- if (is.null(input$her2_status_tcga_brca)) {
tcga_brca_clinical %>%
pull(her2_status) %>%
unique()
} else {
input$her2_status_tcga_brca
}
# filter clinical data
tcga_brca_clinical %>%
dplyr::filter(race %in% fil_race,
menopause_status %in% fil_menopause,
histological_type %in% fil_histological,
stage %in% fil_pathological,
er_status %in% fil_er,
pr_status %in% fil_pr,
her2_status %in% fil_her2)
})
## update action button 1 once run
observeEvent(input$ab_1_tcga_brca, {
updateActionButton(inputId = "ab_1_tcga_brca",
label = "Update Selection")
})
## clinical data case number summary box
output$brca_summarybox <- renderUI({
data <- clinical_selection()
case.number <- nrow(data)
fluidRow(
summaryBox(title = "Number of Cases Selected Using Current Parameters:",
value = case.number,
width = 12,
icon = "fas fa-file-medical",
style = "primary",
border = "left")
)
})
## clinical data plots
# reactive graphic object
clinical_plots_tcga_brca <- reactive({
p1 <- plot_clinical_svar(data = clinical_selection(), x.var = "race", x.lab = "Race")
p2 <- plot_clinical_svar(data = clinical_selection(), x.var = "menopause_status", x.lab = "Menopause Status")
p3 <- plot_clinical_svar(data = clinical_selection(), x.var = "histological_type", x.lab = "Histological Type")
p4 <- plot_clinical_svar(data = clinical_selection(), x.var = "stage", x.lab = "Pathological Stage")
input.data <- clinical_selection() %>%
dplyr::select(id, er_status, pr_status, her2_status) %>%
pivot_longer(cols = ends_with("status"),names_to = "receptor", values_to = "status") %>%
mutate(receptor = toupper(str_remove(receptor, "_status")),
receptor = factor(receptor, levels = c("ER", "PR", "HER2"), ordered = TRUE))
p5 <- plot_clinical_mvar(data = input.data,
x.var = "receptor",
grp.var = "status",
x.lab = "Receptor Type",
grp.lab = "Expression Status")
plot_grid(p1, p2, p3, p4, p5,
ncol = 2,
nrow = 3,
align = "v",
axis = "lr")
})
# render graphic object
output$clinical_plots_tcga_brca <- renderPlot({
print(clinical_plots_tcga_brca())
},
bg = "transparent",
width = 920,
height = 920
)
## clinical data plots download
output$download_clinical_plots_tcga_brca <- downloadHandler(
filename = function() {
paste0("tcga_brca_clinical_summary_plot.png")
},
content = function(file) {
ggsave(file,
plot = clinical_plots_tcga_brca(),
scale = 1.5,
width = 8.27,
height = 11.69,
units = "in",
dpi = 600,
bg = "#FFFFFF",
device = "png")
}
)
output$dl_1_tcga_brca <- renderUI({
req(clinical_plots_tcga_brca())
downloadButton(outputId = "download_clinical_plots_tcga_brca",
label = "Download Plots",
icon = icon("download"),
style = "width:100%;margin-top:1em")
})
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
## TCGA-BRCA - SURVIVAL ANALYSIS
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
## input gene(s) of interest and filter gene level expression data
counts_selection <- eventReactive(input$ab_2_tcga_brca, {
filter_count_data(clinical.data = clinical_selection(),
expr.data = tcga_brca_expression,
annotation = input$annotation_tcga_brca,
genes.input = input$gene_filter_tcga_brca)
})
## update action button 2 once run
observeEvent(input$ab_2_tcga_brca, {
if (nrow(counts_selection()) == 0) {
showModal(modalDialog(title = 'Input Error: "Gene or Gene List"',
includeMarkdown(file.path("www", "error_gene_input.md")),
easyClose = TRUE))
} else {
updateActionButton(inputId = "ab_2_tcga_brca",
label = "Update Parameters")
}
})
## gene selection help modal
observeEvent(input$genes_help_tcga_brca, {
showModal(modalDialog(title = "quicKM Help",
includeMarkdown(file.path("www", "help_gene_input.md")),
easyClose = TRUE)
)
})
## follow-up threshold help modal
observeEvent(input$followup_help_tcga_brca, {
showModal(modalDialog(title = "quicKM Help",
includeMarkdown(file.path("www", "help_followup.md")),
easyClose = TRUE)
)
})
## create survival analysis data frame
surv_data <- eventReactive(input$ab_2_tcga_brca, {
generate_survival_data(clinical.data = clinical_selection(),
expr.data = counts_selection(),
split.cohort = isolate(input$define_cutoff_tcga_brca),
censor.threshold = isolate(input$censor_threshold_tcga_brca),
threshold = isolate(input$define_time_tcga_brca))
})
## survival analysis plot
# reactive graphic object
surv_plot_tcga_brca <- reactive({
plot_survival(data = surv_data(),
title = isolate(input$plot_title_tcga_brca),
pval = isolate(input$show_pval_tcga_brca),
conf.int = isolate(input$show_conf_tcga_brca),
censor = isolate(input$censor_tcga_brca),
surv.median.line = isolate(input$med_surv_line_tcga_brca),
threshold = isolate(input$define_time_tcga_brca))
})
# render graphic object