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app.R
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app.R
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# Load packages----
getPackage <-
function(pkg,
check = TRUE,
load = TRUE,
silent = FALSE,
github = NULL) {
if (check) {
if (!suppressMessages(suppressWarnings(require(
pkg, character.only = TRUE, quietly = TRUE
)))) {
if (is.null(github)) {
try(install.packages(pkg), silent = TRUE)
}
else{
try(remotes::install_github(github))
}
}
}
if (load)
suppressPackageStartupMessages(library(pkg, character.only = TRUE, quietly = TRUE))
if (load & !silent)
message("Loaded ", pkg)
}
packages <-
c("shiny",
"shinydashboard",
"shinyWidgets",
"shinyjs",
"bsplus",
"DT",
"dplyr")
lapply(packages, getPackage)
getPackage(pkg = 'shinydashboardPlus', github = "RinteRface/shinydashboardPlus")
getPackage(pkg = 'shinysky', github = "AnalytixWare/ShinySky")
getPackage(pkg = 'shinyauthr', github = "paulc91/shinyauthr")
getPackage(pkg = 'shinyTree', github = "shinyTree/shinyTree")
# Load functions----
source("./dataprepare_utils.R")
source("./plot_utils.R")
# Close the window and shut down app----
jscode <- "shinyjs.closeWindow = function() { window.close(); }"
# Password for the website----
user_base <- read.csv("./data/accounts.csv")
# Initialize the website with a small dataset----
load("./data/Initialize_expression.rda", envir = .GlobalEnv)
load("./data/Initialize_metadata.rda", envir = .GlobalEnv)
load("./data/Saved_genes_panel.rda", envir = .GlobalEnv)
# Shiny UI----
# Shiny header----
header <- dashboardHeaderPlus(
title = span(
span("s", style = "color: #F3746C; font-size: 24px; font-weight: bold"),
span("ingle "),
span("c", style = "color: #F3746C; font-size: 24px; font-weight: bold"),
span("ell RNA-seq "),
span("D", style = "color: #F3746C; font-size: 24px; font-weight: bold"),
span("ata "),
span("V", style = "color: #F3746C; font-size: 24px; font-weight: bold"),
span("isualization and "),
span("A", style = "color: #F3746C; font-size: 24px; font-weight: bold"),
span("nalysis")
),
titleWidth = 550,
tags$li(
class = "dropdown",
extendShinyjs(text = jscode, functions = c("closeWindow")),
shiny::actionButton(
inputId = "close",
label = "Shut down",
width = "120px",
icon = icon("window-close"),
style = "color: #fff; background-color: #337ab7; border-color: #2e6da4"
)
)
)
# Shiny sidebar----
sidebar <- dashboardSidebar(
tags$head(
tags$style(
type = 'text/css',
".nav-tabs {font-size: 14px} ",
".main-sidebar {font-size: 16px}"
)
),
sidebarMenu(
menuItem("Embedding", tabName = "Embedding", icon = icon("spinner")),
menuItem(
"Distribution",
tabName = "Distribution",
icon = icon("bar-chart-o")
),
menuItem(
"Significance",
tabName = "Significance",
icon = icon("braille")
),
menuItem("Heatmap", tabName = "Heatmap", icon = icon("delicious")),
menuItem(
"In-silico FACS",
tabName = "FACS",
icon = icon("fas fa-fingerprint")
),
menuItem(
"Metadata",
tabName = "Metadata",
icon = icon("stats", lib = "glyphicon")
),
menuItem("DataTable", tabName = "DataTable", icon = icon("table")),
menuItem("Instructions", tabName = "Instructions", icon = icon("file-alt")),
menuItem("About", tabName = "About", icon = icon("envelope"))
)
)
# Shiny body----
body <- dashboardBody(
useShinyjs(),
# *Login page----
shinyauthr::loginUI("login"),
# *Working page----
conditionalPanel(condition = "output.Login_logged",
fluidRow(
column(
width = 9,
# **Head panel----
box(
title = "Gene input",
width = 4,
status = "warning",
collapsible = T,
collapsed = F,
tabBox(
id = "GeneInput_panel",
side = "left",
selected = "Genes",
width = 12,
tabPanel(
"Genes",
textAreaInput(
inputId = "GeneInput_text",
label = "Type a gene or geneset:",
value = "CD14"
)
),
tabPanel(
"Saved",
textAreaInput(
inputId = "GeneInput_saved_text",
label = NULL,
value = NULL
),
selectizeInput(
inputId = "GeneInput_saved",
label = "Select from the saved geneset:",
multiple = TRUE,
choice = Saved_genes_panel,
selected = NULL
)
),
tabPanel(
"Upload",
textAreaInput(
inputId = "GeneInput_file_text",
label = NULL,
value = NULL
),
fileInput(
inputId = "GeneInput_file",
label = "Choose a csv file:",
multiple = FALSE
) %>%
shinyInput_label_embed(
shiny_iconlink() %>%
bs_embed_popover(
title = "Note:",
placement = "left",
content = "There should be a column named as 'Symbol'."
)
)
)
),
shiny::actionButton(
inputId = "GeneInput_submit",
label = "Submit",
width = "120px",
icon = icon("paper-plane"),
style = "color: #fff; background-color: #337ab7; border-color: #2e6da4"
)
),
box(
title = "Plot size",
width = 4,
status = "warning",
collapsible = T,
collapsed = F,
numericInput(
inputId = "PlotSize_width",
label = "Plot width (px)",
value = 960,
step = 10
),
numericInput(
inputId = "PlotSize_height",
label = "Plot height (px)",
value = 960,
step = 10
),
p(
class = "text-muted",
paste(
"Note: Please click the submit button in 'Gene Input' box after you change the figure size."
)
)
),
box(
title = "Plot parameters",
width = 4,
status = "warning",
collapsible = T,
collapsed = F,
selectInput(
inputId = "PlotPar_facet",
label = "Multi gene",
choices = c("Seperate" = TRUE, "Geometric mean" = FALSE),
selected = "Seperate"
),
numericInput(
inputId = "PlotPar_nrow",
label = "Row number",
value = 2
),
numericInput(
inputId = "PlotPar_fontsize",
label = "Font size",
value = 16,
min = 9,
max = 26,
step = 1
)
),
# **Plot region----
box(
status = "warning",
width = 12,
tabItems(
# ****Embedding Plot----
tabItem(
tabName = "Embedding",
box(
status = NULL,
width = 12,
dropdownButton(
selectInput(
inputId = "Embedding_tag1",
label = "Embedding tag",
choices = c("Global", "Sub"),
selected = "Sub"
),
selectInput(
inputId = "Embedding_used1",
label = "Embedding used",
choices = c("tSNE", "UMAP"),
selected = "tSNE"
),
sliderInput(
inputId = "Embedding_dotsize1",
label = "Dot size",
value = 1,
min = 0,
max = 3,
step = 0.2
),
selectInput(
inputId = "Embedding_colorpanel",
label = "Color profile",
choices = c(
"YlOrRd",
"BrBG",
"RdYlBu",
"RdYlGn",
"Spectral",
"YlOrBr",
"YlGnBu",
"Reds",
"Blues",
"OrRd"
),
selected = "YlOrRd"
),
circle = TRUE,
status = "danger",
icon = icon("spinner"),
width = "250px",
tooltip = tooltipOptions(title = "Click to see inputs!")
),
uiOutput("Embedding_geneplot.ui")
),
box(
status = NULL,
width = 12,
dropdownButton(
selectInput(
inputId = "Embedding_colorby",
label = "Color by",
choices = c(
"Global_Cluster",
"Sub_Cluster",
"Tissue",
"Treatment",
"Day",
"Sample",
"nGene",
"nUMI"
),
selected = "Sub_Cluster"
),
selectInput(
inputId = "Embedding_tag2",
label = "Embedding tag",
choices = c("Global", "Sub"),
selected = "Sub"
),
selectInput(
inputId = "Embedding_used2",
label = "Embedding used",
choices = c("tSNE", "UMAP"),
selected = "tSNE"
),
sliderInput(
inputId = "Embedding_dotsize2",
label = "Dot size",
value = 1,
min = 0,
max = 3,
step = 0.2
),
circle = TRUE,
status = "danger",
icon = icon("spinner"),
width = "250px",
tooltip = tooltipOptions(title = "Click to see inputs!")
),
uiOutput("Embedding_metaplot.ui")
),
downloadButton(
outputId = 'Embedding_download',
label = 'Download',
width = "120px",
style = "color: #fff; background-color: #337ab7; border-color: #2e6da4"
)
),
# ****Distribution Plot----
tabItem(
tabName = "Distribution",
dropdownButton(
selectInput(
inputId = "Distribution_plottype",
label = "Plot type",
choices = c("Box Plot", "Violin Plot"),
selected = "Box Plot"
),
selectInput(
inputId = "Distribution_groupby",
label = "Group by",
choices = c(
"Global_Cluster",
"Sub_Cluster",
"Tissue",
"Treatment",
"Day",
"Sample"
),
selected = "Sub_Cluster"
),
selectInput(
inputId = "Distribution_colorby",
label = "Color by",
choices = c(
"Exp",
"Global_Cluster",
"Sub_Cluster",
"Tissue",
"Treatment",
"Day",
"Sample"
),
selected = "Exp"
),
selectInput(
inputId = "Distribution_colorpanel",
label = "Color profile",
choices = c(
"YlOrRd",
"BrBG",
"RdYlBu",
"RdYlGn",
"Spectral",
"YlOrBr",
"YlGnBu",
"Reds",
"Blues",
"OrRd",
"RdBu"
),
selected = "YlOrRd"
),
sliderInput(
inputId = "Distribution_dotsize",
label = "Dot size",
value = 0.6,
min = 0,
max = 1,
step = 0.1
),
circle = TRUE,
status = "danger",
icon = icon("bar-chart-o"),
width = "250px",
tooltip = tooltipOptions(title = "Click to see inputs!")
),
uiOutput("Distribution_plot.ui"),
downloadButton(
outputId = 'Distribution_download',
label = 'Download',
width = "120px",
style = "color: #fff; background-color: #337ab7; border-color: #2e6da4"
)
),
# ****Significance Plot----
tabItem(
tabName = "Significance",
dropdownButton(
selectInput(
inputId = "Significance_groupby",
label = "Group by",
choices = c(
"Global_Cluster",
"Sub_Cluster",
"Tissue",
"Treatment",
"Day",
"Sample"
),
selected = "Sub_Cluster"
),
numericInput(
inputId = "Significance_percutoff",
label = "Expression cutoff",
value = 0
),
numericInput(
inputId = "Significance_siglevel",
label = "Significant level",
value = 0.05
),
circle = TRUE,
status = "danger",
icon = icon("braille"),
width = "250px",
tooltip = tooltipOptions(title = "Click to see inputs!")
),
DT::dataTableOutput("Significance_group"),
tags$br(),
h4("Significance"),
verbatimTextOutput("Significance_statistic"),
tags$br(),
uiOutput("Significance_plot.ui"),
downloadButton(
outputId = 'Significance_download',
label = 'Download',
width = "120px",
style = "color: #fff; background-color: #337ab7; border-color: #2e6da4"
)
),
# ****Heatmap Plot----
tabItem(
tabName = "Heatmap",
dropdownButton(
selectInput(
inputId = "Heatmap_groupby",
label = "Group by",
choices = c(
"Global_Cluster",
"Sub_Cluster",
"Tissue",
"Treatment",
"Day",
"Sample"
),
selected = "Sub_Cluster"
),
selectInput(
inputId = "Heatmap_colorpanel",
label = "Color profile",
choices = c(
"YlOrRd",
"BrBG",
"RdYlBu",
"RdYlGn",
"Spectral",
"YlOrBr",
"YlGnBu",
"Reds",
"Blues",
"OrRd",
"RdBu"
),
selected = "RdBu"
),
circle = TRUE,
status = "danger",
icon = icon("delicious"),
width = "250px",
tooltip = tooltipOptions(title = "Click to see inputs!")
),
uiOutput("Heatmap_plot.ui"),
downloadButton(
outputId = 'Heatmap_download',
label = 'Download',
width = "120px",
style = "color: #fff; background-color: #337ab7; border-color: #2e6da4"
)
),
# ****In-silico FACS Plot----
tabItem(
tabName = "FACS",
tabBox(
id = "FACS",
side = "left",
selected = "FACS",
width = 12,
tabPanel("FACS",
dropdownButton(
numericInput(
inputId = "FACS_xcutoff",
label = "x cutoff",
value = 1
),
numericInput(
inputId = "FACS_ycutoff",
label = "y cutoff",
value = 1
),
selectInput(
inputId = "FACS_colorpanel",
label = "Color profile",
choices = c(
"YlOrRd",
"BrBG",
"RdYlBu",
"RdYlGn",
"Spectral",
"YlOrBr",
"YlGnBu",
"Reds",
"Blues",
"OrRd",
"RdBu"
),
selected = "RdBu"
),
selectInput(
inputId = "FACS_colorby",
label = "Color by",
choices = c(
"None",
"Global_Cluster",
"Sub_Cluster",
"Tissue",
"Treatment",
"Day",
"Sample"
),
selected = "None"
),
sliderTextInput(
inputId = "FACS_dotsize",
label = "Dot size",
choices = c(-1, seq(0, 3, 0.5)),
grid = TRUE,
selected = -1
),
circle = TRUE,
status = "danger",
icon = icon("fas fa-fingerprint"),
width = "250px",
tooltip = tooltipOptions(title = "Click to see inputs!")
),
uiOutput("FACS_plot.ui"),
downloadButton(
outputId = 'FACS_download',
label = 'Download',
width = "120px",
style = "color: #fff; background-color: #337ab7; border-color: #2e6da4")
),
tabPanel("DEG",
box(width = 12,
column(width = 4,
selectInput(
inputId = "FACS_DEG_group1",
label = "The 1st group",
choices = c("Group1", "Group2", "Group3", "Group4"),
selected = "Group1"
),
selectInput(
inputId = "FACS_DEG_group2",
label = "The 2nd group",
choices = c("Group1", "Group2", "Group3", "Group4"),
selected = "Group2"
),
p(
class = "text-muted",
paste(
"Note: The number of cells in each group will be downsampled to 1000 randomly."
)
)
),
column(width = 4,
numericInput(
inputId = "FACS_Volcano_Pvalue_cutoff",
label = "adj.P.Val cutoff",
value = 0.05
),
numericInput(
inputId = "FACS_Volcano_logFC_cutoff",
label = "logFC cutoff",
value = 0.5
),
numericInput(
inputId = "FACS_Volcano_ngenes_labeled",
label = "Labeled genes",
value = 25
)
),
column(width = 4,
numericInput(
inputId = "FACS_Volcano_fontsize",
label = "Font size",
value = 15
),
sliderTextInput(
inputId = "FACS_Volcano_dotsize",
label = "Dot size",
choices = seq(0, 10, 0.5),
grid = TRUE,
selected = 2
),
shiny::actionButton(
inputId = "FACS_DEG_submit",
label = "Calculate",
width = "120px",
icon = icon("calculator"),
style = "color: #fff; background-color: #337ab7; border-color: #2e6da4"
)
)
),
box(width = 12,
uiOutput("FACS_volcano_plot.ui"),
downloadButton(
outputId = 'FACS_volcano_download',
label = 'Download',
width = "120px",
style = "color: #fff; background-color: #337ab7; border-color: #2e6da4"
)
),
box(width = 12,
h3("Differentially expressed genes"),
p(
class = "text-muted",
paste(
"Note: Here only shows 2000 genes with the smallest adj.P.Val. If you want the full gene list, you can download it."
)
),
DT::dataTableOutput("FACS_DEGDataTable_output"),
downloadButton(
'FACS_DEGDataTable_download',
label = 'Download',
width = "120px",
style = "color: #fff; background-color: #337ab7; border-color: #2e6da4"
)
)
)
)
),
# ****Metadata Plot----
tabItem(
tabName = "Metadata",
dropdownButton(
selectInput(
inputId = "Metadata_plottype",
label = "Plot type",
choices = c("Bar Plot", "Pie Plot"),
selected = "Bar Plot"
),
selectInput(
inputId = "Metadata_groupby1",
label = "Group by1",
choices = c(
"Global_Cluster",
"Sub_Cluster",
"Tissue",
"Treatment",
"Day",
"Sample"
),
selected = "Sub_Cluster"
),
selectInput(
inputId = "Metadata_colorby",
label = "Color by",
choices = c(
"Global_Cluster",
"Sub_Cluster",
"Tissue",
"Treatment",
"Day",
"Sample"
),
selected = "Sample"
),
selectInput(
inputId = "Metadata_groupby2",
label = "Group by2",
choices = c(
"None",
"Global_Cluster",
"Sub_Cluster",
"Tissue",
"Treatment",
"Day",
"Sample"
),
selected = "None"
),
selectInput(
inputId = "Metadata_quantify",
label = "Quantified by",
choices = c("Proportion", "Count"),
selected = "Proportion"
),
materialSwitch(
inputId = "Metadata_flip",
label = "Coordinates flipped",
value = TRUE,
status = "primary",
right = TRUE
),
numericInput(
inputId = "Metadata_cutoff",
label = "Proportion shown in pie plot >",
value = 10
),
circle = TRUE,
status = "danger",
icon = icon("stats", lib = "glyphicon"),
width = "250px",
tooltip = tooltipOptions(title = "Click to see inputs!")
),
uiOutput("Metadata_plot.ui"),
downloadButton(
outputId = 'Metadata_download',
label = 'Download',
width = "120px",
style = "color: #fff; background-color: #337ab7; border-color: #2e6da4"
)
),
# ****DataTable Output----
tabItem(
tabName = "DataTable",
DT::dataTableOutput("DataTable_output"),
downloadButton(
'DataTable_download',
label = 'Download',
width = "120px",
style = "color: #fff; background-color: #337ab7; border-color: #2e6da4"
)
),
# ****Instructions Output----
tabItem(
tabName = "Instructions",
tags$iframe(style="height:1200px; width:100%",
src="User Manual of scDVA.pdf")
),
# ****About Output----
tabItem(
tabName = "About",
h2("scDVA"),
HTML(
"<p style = 'margin-left:3%'><font size='4'>We developed this interactive web server scDVA (short for single cell RNA-seq data visualization and analyzation) for users to explore and analyze the single cell RNA-seq data produced in our lab.</font></p>"
),
HTML(
"<p style = 'margin-left:3%'><font size='4'>This tool is developed by Ziyi Li, Tao Wang of <a href = 'http://cancer-pku.cn/'>Zemin Zhang's lab</a>, Peking University.</font></p>"
),
h2("GEPIA2"),
HTML(
"<p style = 'margin-left:3%'><font size='4'>Please try <a href = 'http://gepia2.cancer-pku.cn/#index')>GEPIA2</a> to analyze the bulk RNA-seq data from the TCGA and GTEx projects.</font></p>"
),
h2("Contact us"),
HTML(
"<p style = 'margin-left:3%'><font size='4'>If any question about the use of scDVA or find any bug, please contact us: <a href = 'mailto:liziyie@pku.edu.cn'>liziyie@pku.edu.cn</a></font></p>"
)
)
)
),
busyIndicator(text = "Please wait...", wait = 1000)
),
# **Right dataset selected panel----
column(
width = 3,
box(
title = "Load data",
width = NULL,
status = "danger",
collapsible = T,
collapsed = F,
shinyTree(
"LoadDataTree", checkbox = TRUE, search = TRUE,
searchtime = 1000, theme = "proton"
),
p(
class = "text-muted",
paste("Note: please select one or more dataset(s) to load.")
),
shiny::actionButton(
inputId = "LoadButton",
label = "Load",
width = 120,
icon = icon("upload"),
style = "color: #fff; background-color: #337ab7; border-color: #2e6da4"
),
shinyalert("LoadDataset_alert", FALSE, auto.close.after = 5)
),
box(
title = "Select a dataset",
width = NULL,
status = "warning",
collapsible = T,
collapsed = F,
selectInput(
inputId = "SelectData_dataset",
label = "Dataset",
choices = "Initialize"
),
selectInput(
inputId = "SelectData_normalization",
label = "Normalization",
choices = c("tpm", "counts")
)
),
box(
title = "Subset the dataset",
width = NULL,
status = "warning",
pickerInput(
inputId = "SubsetData_globalcluster",
label = "Global cluster",
choices = NULL,
multiple = TRUE,
options = list(
`actions-box` = TRUE,
`live-search` = TRUE,
size = 8
)
),
pickerInput(
inputId = "SubsetData_subcluster",
label = "Sub cluster",
choices = NULL,
multiple = TRUE,
options = list(
`actions-box` = TRUE,
`live-search` = TRUE,
size = 8
)
),
pickerInput(
inputId = "SubsetData_tissue",
label = "Tissue",
choices = NULL,
multiple = TRUE,
options = list(
`actions-box` = TRUE,
`live-search` = TRUE,
size = 8
)
),
pickerInput(
inputId = "SubsetData_treatment",
label = "Treatment",
choices = NULL,
multiple = TRUE,
options = list(
`actions-box` = TRUE,
`live-search` = TRUE,
size = 8
)
),
pickerInput(
inputId = "SubsetData_day",
label = "Day",
choices = NULL,
multiple = TRUE,
options = list(
`actions-box` = TRUE,
`live-search` = TRUE,
size = 8
)
),
pickerInput(
inputId = "SubsetData_sample",
label = "Sample",
choices = NULL,
multiple = TRUE,
options = list(
`actions-box` = TRUE,
`live-search` = TRUE,
size = 8
)
),
p(
class = "text-muted",
paste(
"Note: Please click the submit button in 'Gene Input' box after you subset the dataset."
)
)
)
)
))
)
# Define UI----
ui <- dashboardPagePlus(header, sidebar, body, use_bs_popover())
# Shiny Server----
server <- function(input, output, session) {
# Close the server----
observeEvent(input$close, {
js$closeWindow()
stopApp()
})
# # Loggin Page----
credentials <- callModule(
shinyauthr::login,
"login",
data = user_base,
user_col = user,
pwd_col = password,
log_out = reactive(logout_init())
)
logout_init <-
callModule(shinyauthr::logout,
"logout",
reactive(credentials()$user_auth))
output$Login_logged <- reactive({
req(credentials()$user_auth)
})
outputOptions(output, 'Login_logged', suspendWhenHidden = FALSE)
# Load data tree-----
output$LoadDataTree <- renderTree({
dataset_tree
})
# Interactive load button----
observeEvent(input$LoadButton, {
req(input$LoadDataTree)
LoadData_selected <- get_selected(input$LoadDataTree, format = "classid") # Record the dataset to load
LoadData_selected <- LoadData_selected[LoadData_selected %in% dataset_map$DatasetName]
if (is.null(LoadData_selected)) {
LoadData_selected <- character(0)
}
LoadData.status <-
loadDataSet(LoadData_selected) # Load the dataset
showshinyalert(session, "LoadDataset_alert", "Done.",
styleclass = "info")
updateSelectInput(
session,
inputId = "SelectData_dataset",
choices = LoadData_selected,
selected = LoadData_selected[1]
)
})
# Update the panel----
# Update the SubsetData panel
observe({
SubsetData_panel_list <- getPanelList(input$SelectData_dataset)
updatePickerInput(
session = session,