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app.R
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app.R
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# Installation of the necessary packages
pkg <- c("shiny", "ggplot2", "stringr", "shinydashboard", "shinyFiles", "shinycssloaders", "ijtiff", "RImageJROI",
"plotly", "BiocManager", "shinyjs", "V8", "Rcpp", "pillar", "readtext", "magick", "png", "shinyWidgets","fpc",
"dbscan","reticulate") # Necessary packages
new.pkg <- pkg[!(pkg %in% installed.packages())]
if (length(new.pkg)) { # If any necessary packages not installed, install them
install.packages(new.pkg, dependencies=TRUE)
}
if (!"EBImage" %in% installed.packages()) {
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("EBImage") # Specific installation for EBImage
}
library(readtext)
library(EBImage)
library(shinyjs)
library(shiny)
library(ggplot2)
library(stringr)
library(shinydashboard)
library(shinycssloaders)
library(ijtiff)
library(RImageJROI)
library(plotly)
library(V8)
library(shinyFiles)
library(shinyWidgets)
library(fpc)
library(dbscan)
library(reticulate)
np <- import("numpy", convert=FALSE)
# environnment to use
reticulate::use_condaenv("saphir_env", required = FALSE)
#see python versions and environments
#reticulate::py_config()
# path to the python script
reticulate::source_python('segment_3D.py', convert = FALSE)
# User interface
ui <- dashboardPage(
## Title of the page
dashboardHeader(title = "S A P H I R"),
## Sidebar
dashboardSidebar (
## Menu with 3 items
sidebarMenu(
id = "menu",
menuItem("A) ImageJ segmentation", tabName = "segmentation", icon=icon("images")),
menuItem("B) Select your results", tabName = "image", icon=icon("file-import")),
menuItem("AB') Segmentation", tabName = "seg", icon = icon("draw-polygon")),
menuItem("C) Clustering", tabName = "clustering", icon = icon("arrows-h")),
menuItem("D) Plot to image", tabName = "plotToImage", icon = icon("poll")),
menuItem("E) Image to plot", tabName = "imageToPlot", icon = icon("image")),
menuItem("F) Annotate your data", tabName = "annotation", icon = icon("edit"))
)
),
dashboardBody(
## Hide disabled elements
tags$head(tags$style(HTML("input[type='search']:disabled {visibility:hidden}"))),
shinyjs::useShinyjs(),
shinyjs::extendShinyjs(text = "shinyjs.refresh = function() { location.reload(); }", functions = c()),
# FIRST ITEM : Choose image to analyse & select values to remove
tabItems(
tabItem(tabName = "segmentation",
fluidRow(
box( width = 12, solidHeader=TRUE, status="primary",
title = "Type of analysis",
radioButtons("os", "Select your OS :", choices=c("Windows", "MacOs", "Linux"), selected="Windows", inline=TRUE),
tags$hr(),
radioButtons("software", "Select the software you use :", choices=c("Fiji", "ImageJ"), selected="ImageJ", inline=TRUE),
uiOutput("imageJ"),
textOutput("softwarePath"),
tags$br(),
actionLink("changeIJ", "Change the path to your software.", icon=icon("sync")),
tags$hr(),
uiOutput("macro"),
textOutput("macroPath"),
tags$br(),
actionLink("changeMacro", "Change the path to your macro.", icon=icon("sync")),
tags$br(),
actionButton("launch", "Launch macro",
style="color: #fff; background-color: #337ab7; border-color: #2e6da4"),
tags$hr(),
helpText("If necessary, choose a second macro to launch."),
uiOutput("macro2"),
textOutput("macro2Path"),
tags$br(),
actionLink("changeMacro2", "Change the path to your macro.", icon=icon("sync")),
tags$br(),
actionButton("launch2", "Launch second macro",
style="color: #fff; background-color: #337ab7; border-color: #2e6da4")
)
)),
tabItem(tabName= "image",
# Image browser
fluidRow(
box (width = 12, solidHeader=TRUE, status = "primary",collapsible = TRUE, collapsed=TRUE,
title = "Use files stored in the www directory",
helpText("To use this button, you will need 4 files stored in a repertory \"www\" in your working directory.
For prerequisites, click on the \"Prerequisites\" link."),
actionLink("help", "Prerequisites"),
tags$br(),
actionButton("default", "Use default files",
style="color: #fff; background-color: #337ab7; border-color: #2e6da4"),
verbatimTextOutput("errorDefaultFiles")
),
box (width = 12, solidHeader=TRUE, status = "primary",collapsible = TRUE, collapsed=TRUE,
title = "Select the different files to use",
helpText("Select the image you want to analyse. (Format .tif)"),
fileInput("imgFile", "Choose Image", multiple=FALSE),
tags$hr(),
helpText("Select the file containing the legend for the channels in the image."),
radioButtons("sepLegend", label="Type of separator in the file", choices = c("Tab", "Comma", "Semicolon"), selected="Tab", inline=TRUE),
checkboxInput("headerLegend", label = "Header", value = TRUE),
fileInput("legendFile", "Choose legend file.", multiple=FALSE),
tags$hr(),
helpText("Select the file containing the datas to analyse. (Format .txt)"),
radioButtons("sep", label="Type of separator in the file", choices = c("Tab", "Comma", "Semicolon"), selected="Tab", inline=TRUE),
radioButtons("dec", label="Type of decimals in the file", choices = c("Point", "Comma"), selected="Point", inline=TRUE),
checkboxInput("header", label = "Header", value = TRUE),
fileInput("dataFile", "Choose Data file", multiple=FALSE),
tags$hr(),
helpText("Select the zip file containing your ROIs."),
fileInput("zipFile", "Choose ROIs .zip file", multiple=FALSE),
),
box (width = 12, solidHeader=TRUE, status="primary", collapsible = TRUE, collapsed=TRUE,
title = "Combine multiple images",
numericInput("multiImages_nb", "Number of files to use", 1, min=0, max=5, step=1),
uiOutput("multiImages_selectors"),
verbatimTextOutput("errorMultiImages")
)
),
actionButton("refresh", "Reset",
style="color: #fff; background-color: #337ab7; border-color: #2e6da4")
),
## Tab Segmentation
tabItem(tabName = "seg",
fluidRow(
column(width = 6,
box(width = NULL, solidHeader = TRUE, status = "primary", title = "Parameters",
helpText("Select the image you want to analyse. (Format .tif)"),
fileInput("seg_imgFile", "Choose Image", multiple = FALSE),
verbatimTextOutput("test"),
uiOutput("seg_channel"),
uiOutput("seg_frame"),
checkboxInput("seg_brightnessImg", "Enhance brightness in image"),
uiOutput("seg_brightnessSlider"),
withSpinner(EBImage::displayOutput("seg_img")),
tags$br(),
radioButtons("seg_algo", "Choose segmentation algorithm : ", choices = c("Cellpose","Watershed"), selected = "Cellpose")
)
),
column (width=6,
uiOutput("seg_algo_para"),
uiOutput("seg_mask_display"),
uiOutput("seg_load_files"))
)
),
## Tab Clustering
tabItem(tabName = "clustering",
fluidRow(
column(width = 6,
box(width = NULL, solidHeader = TRUE, status = "primary",
title = "DBSCAN clustering",
helpText("Select DBSCAN parameters : "),
verbatimTextOutput("clustering_advise"),
tags$br(),
sliderInput("eps", "Epsilon (in micron) :", value = "10", min = 0, max = 200, step = 0.1),
sliderInput("mp", "Min Points : ", value = 5, min = 0, max = 100, step = 1),
actionButton("godbs", "Run DBSCAN"),
plotlyOutput("clustering_plot")
)
),
column(width=6,
box(width = NULL, solidHeader = TRUE, status = "primary",
title = "View results",
downloadLink('downloadClusterDataUI','Download'),
tableOutput('clustering_table')
)
)
)
),
## Tab Plot to image
tabItem(tabName = "plotToImage",
fluidRow(
column( width =6,
# First box : Plot & Datas
box (width = NULL, solidHeader=TRUE, status="primary",collapsible = TRUE,
title = "Filtering",
radioButtons("plotToImgFilter_plotType", "Number of parameters you want to filter", choices=c("One", "Two"), selected="One", inline=TRUE),
uiOutput("plotToImgFilter_colsX"),
uiOutput("plotToImgFilter_colsY"),
uiOutput("plotToImgFilter_pointSize"),
uiOutput("plotToImgFilter_choosePointSize"),
radioButtons("plotToImgFilter_selectionType", "Type of selection", choices=c("Single selection", "Multiple selection"), selected="Single selection"),
helpText("Select the cells (click or brush) to analyze in the interactive Plot"),
plotlyOutput("plotToImgFilter_plot"),
uiOutput("plotToImgFilter_validateSelection"),
uiOutput("plotToImgFilter_reset")
),
box( width = NULL,
title = "Interactive Plot", solidHeader=TRUE, status="primary",
helpText("Select parameters to use for the scatter plot."),
uiOutput("plotToImg_colsX"),
uiOutput("plotToImg_colsY"),
checkboxInput(inputId = "plotToImg_modifyPointSize",
label = "Modify point size on scatter plot",
value = FALSE),
uiOutput("plotToImg_choosePointSize"),
uiOutput("plotToImg_colShape"),
uiOutput("plotToImg_shapeThreshold"),
helpText("Select cell(s), obtain statistics and visualize selected subsets in the image (option below)"),
withSpinner(
plotlyOutput("plotToImg_plot", height = "600px")),
uiOutput("plotToImg_nextSel"),
uiOutput("plotToImg_resetAllSel"),
useShinyjs(),
extendShinyjs(text = "shinyjs.resetSelect = function() { Shiny.onInputChange('.clientValue-plotly_selected', 'null'); }", functions=c()),
extendShinyjs(text = "shinyjs.resetClick = function() { Shiny.onInputChange('.clientValue-plotly_click', 'null'); }", functions=c()),
radioButtons("plotToImg_selectionType", "Type of selection",
choices=c("Single selection", "Multiple selection", "Z slice"),
selected="Single selection"),
uiOutput("plotToImg_specificFrame"),
tags$h5(tags$strong("Options")),
checkboxInput("plotToImg_associated", "Associate with slice", value=TRUE),
uiOutput("plotToImg_displayImg"),
uiOutput("plotToImg_colorType"),
uiOutput("plotToImg_validateAndAnnote")
),
),
# Second box : Image displayer
column (width=6,
uiOutput("plotToImg_imageDisplayers")
)
),
fluidRow(
box ( width = 12, solidHeader=TRUE, status = "primary",collapsible = TRUE,
title="Statistics",
actionLink("plotToImg_downloadDataLink", "Download results"),
tags$br(),
tags$br(),
tabsetPanel (id="infosGroup", selected="Global",
tabPanel("Global",
verbatimTextOutput("plotToImg_groups")
),
tabPanel("Selected",
tags$br(),
verbatimTextOutput("plotToImg_infosSelection"),
tags$br(),
tableOutput("plotToImg_tableSelected")
)
)
)
)
),
## Tab Image to plot
tabItem(tabName = "imageToPlot",
fluidRow(
# First box : image displayer
box( width = 7,
title = "Image display", solidHeader = TRUE, status = "primary",
helpText("Select channel and frame to display."),
tags$h5(tags$strong("Channel legend : ")),
tableOutput("imgToPlot_legend"),
uiOutput("imgToPlot_channel"),
uiOutput("imgToPlot_frame"),
helpText("Select the color of the ROIs."),
uiOutput("imgToPlot_color"),
checkboxInput("imgToPlot_modifyThickness", "Modify thickness of cell's highlight"),
uiOutput("imgToPlot_thicknessSlider"),
checkboxInput("imgToPlot_brightnessImg", "Enhance brightness in image"),
uiOutput("imgToPlot_brightnessSlider"),
radioButtons("imgToPlot_selectionType", "Type of selection", choices=c("Single selection", "Multiple selection"), selected="Single selection"),
helpText("Click or select cells on the image and see their correspondance in the plot."),
withSpinner(plotlyOutput("imgToPlot_img", height = "600px")),
uiOutput("imgToPlot_validateSelection"),
uiOutput("imgToPlot_reset"),
verbatimTextOutput("imgToPlot_selected")
),
# Second box : corresponding plot
box( width = 5, title = "Plot", solidHeader=TRUE, status="primary",
helpText("Select the columns to use on the scatter plot."),
uiOutput("imgToPlot_colsX"),
uiOutput("imgToPlot_colsY"),
checkboxInput(inputId = "imgToPlot_modifyPointSize",
label = "Modify point size on scatter plot",
value = FALSE),
uiOutput("imgToPlot_choosePointSize"),
plotOutput("imgToPlot_plot"))
)
),
tabItem(tabName = "annotation",
fluidRow(
column( width =6,
# First box : Plot & Datas
box (width = NULL, solidHeader=TRUE, status="primary",collapsible = TRUE,
title = "Parameters - Select ROIs",
helpText("Select the variable to annotate."),
uiOutput("annote_variable")
),
box (width = NULL, solidHeader = TRUE, status="primary", collapsible=TRUE,
title="Select ROIs",
uiOutput("annote_selectionType"),
uiOutput("annote_idSelectionType"),
uiOutput("annote_idSelection"),
uiOutput("annote_useVariable"),
uiOutput("annote_plotType"),
uiOutput("annote_variableHisto"),
uiOutput("annote_variableScatter"),
uiOutput("annote_plotUI"),
uiOutput("annote_validateSelection"),
tags$br(),
verbatimTextOutput("annote_selection")
)
),
column( width = 6,
box (width = NULL, solidHeader=TRUE, status="primary", collapsible = TRUE,
title = "Image of the ROI",
uiOutput("annote_channel"),
uiOutput("annote_frame"),
uiOutput("annote_cropSize"),
checkboxInput("annote_associate", "Associate with slice", value=TRUE),
checkboxInput("annote_overlay", "Overlay channels (up to 3)"),
checkboxInput("annote_addBrightness", "Enhance brightness in image", value=FALSE),
uiOutput("annote_brightnessSlider"),
checkboxInput("annote_modifyThickness", "Modify cell's highlight thickness", value=FALSE),
uiOutput("annote_thicknessSlider"),
uiOutput("annote_channelOverlay"),
withSpinner(EBImage::displayOutput("annote_cropImg")),
verbatimTextOutput("annote_actualValue"),
tags$br(),
fluidRow(column(6, uiOutput("annote_modifyValue"), uiOutput("annote_inputNewValue")),
column (6, div(style="display: inline-block;vertical-align:top;",uiOutput("annote_previous")),
div(style="display: inline-block;vertical-align:top;",uiOutput("annote_next")))),
tags$br(),
tags$br(),
uiOutput("annote_validateModif"),
tags$br(),
uiOutput("downloadAnnotDataUI"),
tags$br(),
verbatimTextOutput("annoteData"),
)
)
)
)
)
)
)
server <- function(input, output, session) {
#=============================================================================
### MENU SEGMENTATION
observeEvent(input$refresh, {
shinyjs::js$refresh()
})
# Reactive variables
segmentation <- reactiveValues(ijPath="", fijiPath="", macroPath="", macro2Path="")
global <- reactiveValues(data = NULL, dataPath = "" , zipPath = "", legendPath="", legend=NULL, imgPath = "", img=list(), zip=NULL, nFrame=1,
nChan=1, resolution=NULL, resize = FALSE, coeff_prop = 1, xcenters=NULL, ycenters=NULL)
seg <- reactiveValues(imgPath= "", img = list(), nFrame = 1, nChan = 1, resolution = NULL, resize = FALSE, coeff_prop = 1,imgFrame = 1, imgPNG = NULL, imgToSegment = NULL,
maskFrame =1, maskToDisplay =NULL, mask_cp = NULL, mask_ws= NULL, maskTo = NULL, Path= "", chan1 = 1, chan2 = 2)
clustering <- reactiveValues(imgFrame = 1, imgChan = 1, actualImg = NULL, imgPNG = NULL)
plotToImg <- reactiveValues(imgFrame=1, imgChan=1, actualImg=NULL, imgPNG=NULL, imgPNG2=NULL,crops = list(), totalCrops = NULL , subData=NULL, selected=NULL, filtered=NULL)
imgToPlot <- reactiveValues(imgFrame=1, imgChan=1, actualImg=NULL, imgPNG=NULL, selected=NULL)
annote <- reactiveValues(selected = NULL, actual = NULL, index=1, imgChan=1, imgFrame=1, imgPNG=NULL, data=NULL, ID=NULL)
# Roots for shinyfiles chooser
if (.Platform$OS.type=="unix") {
roots = c(home='/')
}
else if (.Platform$OS.type=="windows") {
roots = c(home='C:')
}
## MENU IMAGEJ
## File & Dir chooser
# ImageJ/Fiji dir chooser
shinyDirChoose(input, 'imageJ', roots=roots)
# First macro file chooser
shinyFileChoose(input, 'macro', roots=roots, filetypes=c("ijm", "txt"))
# Second macro file chooser
shinyFileChoose(input, 'macro2', roots=roots, filetypes=c("ijm", "txt"))
## If file containing path to IJ software does not exist -> Shiny dir chooser
observeEvent(eventExpr=input$changeIJ,
handlerExpr={
# Directory browser if no path registered
output$imageJ <- renderUI ({
if (((!file.exists("www/ijpath.txt")) & (input$software=="ImageJ")) | ((!file.exists("www/fijipath.txt")) & (input$software=="Fiji"))) {
shinyDirLink('imageJ', 'Select ImageJ.app / Fiji.app', 'Please select the repository ImageJ.app/Fiji.app', FALSE, icon=icon("folder"))
}
})
# Text w/ path
output$softwarePath <- renderText({
if (input$software=="Fiji") {
paste("Your Fiji app is in the directory : ",segmentation$fijiPath, "/", sep="")
}
else if (input$software=="ImageJ") {
paste("Your ImageJ app is in the directory : ", segmentation$ijPath,"/", sep="")
}
})
}, ignoreNULL=FALSE)
## If ImageJ browser needed (no path registered) -> store the path in global variable & in a file containing the path
observeEvent(eventExpr = {
input$imageJ
},
handlerExpr = {
if (!"path" %in% names(input$imageJ)) return()
if (input$software=="ImageJ") {
segmentation$ijPath <-
normalizePath(parseDirPath(roots, input$imageJ), winslash="/")
if (!dir.exists("www")) {dir.create("www")}
f <- file("www/ijpath.txt", open = "w")
cat(normalizePath(parseDirPath(roots, input$imageJ), winslash="/"), file = f)
close(f)
}
if (input$software=="Fiji") {
segmentation$fijiPath <-
normalizePath(parseDirPath(roots, input$imageJ), winslash="/")
if (!dir.exists("www")) {dir.create("www")}
f <- file("www/fijipath.txt", open = "w")
cat(normalizePath(parseDirPath(roots, input$imageJ), winslash="/"), file = f)
close(f)
}
})
## If button "Change" clicked : remove file containing registered path and possibility to choose your directory again.
observeEvent(eventExpr=input$changeIJ,
handlerExpr={
if (input$software=="Fiji") {
file.remove("www/fijipath.txt")
segmentation$fijiPath <- ""
}
else if (input$software=="ImageJ") {
file.remove("www/ijpath.txt")
segmentation$ijPath <- ""
}
})
## If file containing path exists : store path to IJ in global variable.
if (file.exists("www/ijpath.txt")) {
segmentation$ijPath <- readtext("www/ijpath.txt")$text
}
if (file.exists("www/fijipath.txt")) {
segmentation$fijiPath <- readtext("www/fijipath.txt")$text
}
## If no path registered : File browser to choose your macro
observeEvent(eventExpr=input$changeMacro,
handlerExpr={
output$macro <- renderUI ({
# File chooser for the macro
if (!file.exists("www/macropath.txt")) {
shinyFilesLink('macro', 'Select the path to your macro', 'Please select the path to your macro', FALSE, icon=icon("file"))
}
})
# Text w/ path
output$macroPath <- renderText({
paste("The path to your macro is : ", segmentation$macroPath, sep="")
})
}, ignoreNULL=FALSE)
## If macro choosed with browser : store the path in a variable global
observeEvent(eventExpr = {input$macro},
handlerExpr = {
segmentation$macroPath <- normalizePath(parseFilePaths(roots, input$macro)$datapath, winslash="/")
})
## If change macro : remove file containing registered path
observeEvent(eventExpr=input$changeMacro,
handlerExpr={
file.remove("www/macropath.txt")
segmentation$macroPath <- ""
})
## If file exists containing registered path, read the path from the file.
if (file.exists("www/macropath.txt")) {
segmentation$macroPath <- readtext("www/macropath.txt")$text
}
## Launch the first macro
observeEvent(eventExpr={
input$launch},
handlerExpr={
f <- file("www/macropath.txt", open = "w")
cat(segmentation$macroPath, file = f)
close(f)
if (" " %in% str_split(segmentation$macroPath, "")[[1]]) {
segmentation$macroPath <- str_replace_all(segmentation$macroPath, " ", "\" \"")
}
if (" " %in% str_split(segmentation$ijPath, "")[[1]]) {
segmentation$ijPath <- str_replace_all(segmentation$ijPath, " ", "\" \"")
}
if (" " %in% str_split(segmentation$fijiPath, "")[[1]]) {
segmentation$fijiPath <- str_replace_all(segmentation$fijiPath, " ", "\" \"")
}
if (input$os == "MacOs") {
if (input$software=="ImageJ") {
system(str_c("java -Xmx4096m -jar ", segmentation$ijPath, "/Contents/Java/ij.jar -ijpath ", segmentation$ijPath, " -macro ", segmentation$macroPath, sep=""))
}
else if (input$software=="Fiji") {
system(str_c(segmentation$fijiPath, "/Contents/MacOS/ImageJ-macosx -port2 &", sep=""))
Sys.sleep(5)
system(str_c(segmentation$fijiPath, "/Contents/MacOS/ImageJ-macosx -port2 --no-splash -macro ", segmentation$macroPath, sep=""))
}
}
else if (input$os == "Windows") {
if (input$software == "ImageJ") {
system(str_c(segmentation$ijPath, "/jre/bin/java -jar -Xmx1024m ", segmentation$ijPath, "/ij.jar -macro ", segmentation$macroPath, sep=""))
}
else if (input$software == "Fiji") {
system(str_c(segmentation$fijiPath, "/ImageJ-win64.exe -port1 &", sep="" ), wait=FALSE)
system(str_c(segmentation$fijiPath, "/ImageJ-win64.exe -port1 --no-splash -macro ", segmentation$macroPath, sep="" ))
}
}
}, once=TRUE)
## If no second path registered : file browser to choose your second macro
observeEvent(eventExpr=input$changeMacro2,
handlerExpr={
output$macro2 <- renderUI ({
if (!file.exists("www/macro2path.txt")) {
shinyFilesLink('macro2', 'Select the path to your second macro', 'Please select the path to your macro', FALSE, icon=icon("file"))
}
})
output$macro2Path <- renderText({
paste("The path to your macro is : ", segmentation$macro2Path, sep="")
})
}, ignoreNULL=FALSE)
## If second macro browser : store it in a global variable
observeEvent(eventExpr = {input$macro2},
handlerExpr = {
segmentation$macro2Path <- normalizePath(parseFilePaths(roots, input$macro2)$datapath, winslash="/")
})
## If change macro2 : remove file containing macro path
observeEvent(eventExpr=input$changeMacro2,
handlerExpr={
file.remove("www/macro2path.txt")
segmentation$macro2Path <- ""
})
## If file containing macro path2 exists : read from this file
if (file.exists("www/macro2path.txt")) {
segmentation$macro2Path <- readtext("www/macro2path.txt")$text
}
## Second launcher
observeEvent(eventExpr={
input$launch2},
handlerExpr={
req(input$launch)
# Store the path in a file
f <- file("www/macro2path.txt", open = "w")
cat(segmentation$macro2Path, file = f)
close(f)
# Deal with spaces in paths
if (" " %in% str_split(segmentation$macro2Path, "")[[1]]) {
segmentation$macro2Path <- str_replace(segmentation$macro2Path, " ", "\" \"")
}
if (input$os == "MacOs") {
if (input$software=="ImageJ") {
system(str_c("java -Xmx4096m -jar ", segmentation$ijPath, "/Contents/Java/ij.jar -ijpath ", segmentation$ijPath, " -macro ", segmentation$macro2Path, sep=""))
}
else if (input$software=="Fiji") {
system(str_c(segmentation$fijiPath, "/Contents/MacOS/ImageJ-macosx -port2 &", sep=""))
Sys.sleep(5)
system(str_c(segmentation$fijiPath, "/Contents/MacOS/ImageJ-macosx -port2 --no-splash -macro ", segmentation$macro2Path, sep=""))
}
}
else if (input$os == "Windows") {
if (input$software == "ImageJ") {
system(str_c(segmentation$ijPath, "/jre/bin/java -jar -Xmx1024m ", segmentation$ijPath, "/ij.jar -macro ", segmentation$macro2Path, sep=""))
}
else if (input$software == "Fiji") {
system(str_c(segmentation$fijiPath, "/ImageJ-win64.exe -port1 &", sep="" ), wait=FALSE)
system(str_c(segmentation$fijiPath, "/ImageJ-win64.exe -port1 --no-splash -macro ", segmentation$macro2Path, sep="" ))
}
}
}, once=TRUE)
#=============================================================================
## MENU SELECT YOUR RESULTS
# Prerequisites button for www files
observeEvent(input$help, {
showModal(modalDialog(
title = "Prerequisites for default files",
"4 files needed : ", tags$br(), "- file .tif containing your image in TIF format", tags$br(),
"- file .txt containing your intensity results in csv format, with a TAB separator and a HEADER", tags$br(),
"- file .csv containing your legends result in csv format, with a TAB separator and a HEADER", tags$br(),
"- file .zip containing your ImageJ ROIs. ", tags$br(),
"Store these files in a repository named www in your working directory and click on the button, you won't have to choose your files after,
the files in the directory will be used.",
easyClose = TRUE
))
})
# Use www files -> search if there is one file of each type in the repository
observeEvent(eventExpr=input$default, handlerExpr = {
if (length(dir(path = paste(getwd(), "/www/", sep=""), pattern = "*.zip$", recursive=TRUE))==1 & length(dir(path = paste(getwd(), "/www/", sep=""), pattern = "*.tif$", recursive=TRUE))==1 &
length(dir(path = paste(getwd(), "/www/", sep=""), pattern = "*.txt$", recursive=TRUE))==1 & length(dir(path = paste(getwd(), "/www/", sep=""), pattern = "*.csv$", recursive=TRUE))==1) {
global$imgPath <- paste("www/", dir(path = paste(getwd(), "/www/", sep=""), pattern = "*.tif$", recursive=TRUE), sep="")
global$dataPath <- paste("www/", dir(path = paste(getwd(), "/www/", sep=""), pattern = "*.txt$", recursive=TRUE), sep="")
global$zipPath <- paste("www/", dir(path = paste(getwd(), "/www/", sep=""), pattern = "*.zip$", recursive=TRUE), sep="")
global$legendPath <- paste("www/", dir(path = paste(getwd(), "/www/", sep=""), pattern = "*.csv$", recursive=TRUE), sep="")
} # If yes, store the path in variables
else {
output$errorDefaultFiles <- renderPrint ({
if (length(dir(path = paste(getwd(), "/www/", sep=""), pattern = "*.zip$", recursive=TRUE))>1 | length(dir(path = paste(getwd(), "/www/", sep=""), pattern = "*.tif$", recursive=TRUE))>1 |
length(dir(path = paste(getwd(), "/www/", sep=""), pattern = "*.txt$", recursive=TRUE))>1 | length(dir(path = paste(getwd(), "/www/", sep=""), pattern = "*.csv$", recursive=TRUE))>1) {
paste0("ERROR : Multiple files with the same extension. Please read prerequisites. ")
}
else if (length(dir(path = paste(getwd(), "/www/", sep=""), pattern = "*.zip$", recursive=TRUE))==0 | length(dir(path = paste(getwd(), "/www/", sep=""), pattern = "*.tif$", recursive=TRUE))==0 |
length(dir(path = paste(getwd(), "/www/", sep=""), pattern = "*.txt$", recursive=TRUE))==0 | length(dir(path = paste(getwd(), "/www/", sep=""), pattern = "*.csv$", recursive=TRUE))==0) {
paste0("ERROR : Missing files. Please read prerequisites. ")
}
})
} # If no, send an error message with the problem to solve
})
## If use of the file input buttons, store the path of the selected file in a variable
# Image variables
observeEvent(eventExpr= input$imgFile, handlerExpr = { global$imgPath <- input$imgFile$datapath }, label = "files")
# Legend variable
observeEvent(eventExpr=input$legendFile, handlerExpr = { global$legendPath <- input$legendFile$datapath })
# Datas variables
observeEvent(eventExpr= input$dataFile, handlerExpr = { global$dataPath <- input$dataFile$datapath }, label = "files")
# ROIs variables
observeEvent(eventExpr= input$zipFile, handlerExpr = { global$zipPath <- input$zipFile$datapath }, label = "files")
## Once the paths have been stored in their respective variables, read files
observeEvent ({
global$imgPath
global$legendPath
global$dataPath
global$zipPath
},
{ req(global$imgPath, global$legendPath, global$dataPath, global$zipPath)
if ((dim(read_tif(global$imgPath)))[4]==1) { # If only one frame
global$img <- read_tif(global$imgPath) # store the image in a single variable
global$img <- as_EBImage(global$img) # transform the image in a EBImage object
EBImage::colorMode(global$img) <- "Grayscale"
global$nChan <- dim(global$img)[3] # number of channel on the image
global$resolution <- attr(read_tif(global$imgPath), "x_resolution") # resolution of the image (number of microns corresponding to 1 pixel)
if (dim(global$img)[1] > 1024 | dim(global$img)[2] > 1024) { # if the image is too big (more than 1024*1024), resize it proportionaly
global$coeff_prop <- 1024/max(dim(global$img)[1], dim(global$img)[2]) # the highest dimension is resized to 1024
global$img <- EBImage::resize(global$img, dim(global$img)[1] * global$coeff_prop, dim(global$img)[2]*global$coeff_prop) # resize the image
global$resize <- TRUE
global$resolution <- global$resolution*global$coeff_prop # adapt the resolution
}
}
else if ((dim(read_tif(global$imgPath)))[4] > 1) { # If multiple frame
global$nFrame <- (dim(read_tif(global$imgPath)))[4] # number of frames on the image
global$resolution <- attr(read_tif(global$imgPath, frames=1), "x_resolution") # resolution of the image (number of microns corresponding to 1 pixel)
for (i in c(1:global$nFrame)) { # for any frame of the image
global$img[[i]] <- read_tif(global$imgPath, frames=i) # store each frame as an element of a list
global$img[[i]] <- as_EBImage(global$img[[i]]) # and transform it in a EBImage object
EBImage::colorMode(global$img[[i]]) <- "Grayscale"
if (dim(global$img[[i]])[1] > 1024 | dim(global$img[[i]])[2] > 1024) { # if the image is too big (more than 1024*1024), resize it proportionaly
global$coeff_prop <- 1024/max(dim(global$img[[i]])[1], dim(global$img[[i]])[2]) # the highest dimension is resized to 1024
global$img[[i]] <- EBImage::resize(global$img[[i]], dim(global$img[[i]])[1]* global$coeff_prop, dim(global$img[[i]])[2]*global$coeff_prop) # resize the image
global$resize <- TRUE
}
}
if (global$resize == TRUE) {
global$resolution <- global$resolution*global$coeff_prop # adapt the global resolution of the image
}
global$nChan <- dim(global$img[[1]])[3] # number of channel on the image
}
separator <- switch (input$sep, "Tab"="\t", "Comma"=",", "Semicolon"=";")
decimal <- switch (input$dec, "Point"=".", "Comma"=",")
global$data <- read.table(global$dataPath,header=input$header, sep=separator, dec=decimal) # read intensity file
global$zip <- read.ijzip(global$zipPath) # read roi.zip file
if (global$resize == TRUE) { # if resize of the image, modify the coordinates of the ROIs
for (i in c(1:length(global$zip))) {
global$zip[[i]]$coords <- global$zip[[i]]$coords/global$coeff_prop
}
}
sepLegend <- switch( input$sepLegend, "Tab"="\t", "Comma"=",", "Semicolon"=";")
global$legend <- read.table(global$legendPath, header=TRUE, sep=sepLegend, dec=".") # read legend file
})
## Observer which modify the actual image of each menu depending on the actual frame selected
observe({
req(length(global$img) > 0)
if (global$nFrame > 1) {
plotToImg$actualImg <- global$img[[plotToImg$imgFrame]]
imgToPlot$actualImg <- global$img[[imgToPlot$imgFrame]]
annote$actualImg <- global$img[[annote$imgFrame]]
}
else {
plotToImg$actualImg <- global$img
imgToPlot$actualImg <- global$img
annote$actualImg <- global$img
}
})
observeEvent( {
plotToImg$imgFrame
imgToPlot$imgFrame
annote$imgFrame
}
,{
req(length(global$img) > 0)
if (global$nFrame > 1) {
plotToImg$actualImg <- global$img[[plotToImg$imgFrame]]
imgToPlot$actualImg <- global$img[[imgToPlot$imgFrame]]
annote$actualImg <- global$img[[annote$imgFrame]]
}
})
## Multi image selectors: UI with the necessary file input for the number of images wanted
output$multiImages_selectors <- renderUI ({
if (input$multiImages_nb > 1) {
tagList(radioButtons("multiImages_legendSep", label="Type of separator in the file", choices = c("Tab", "Comma", "Semicolon"), selected="Tab", inline=TRUE),
fileInput("multiImages_legendFile", "Choose legend file", multiple=FALSE), # Only one legend file for all images, they must all have the same legend
lapply(1:input$multiImages_nb, function(i)
{ tagList(fileInput(paste0("multiImages_imgFile", i), paste0("Choose image number ", i), multiple=FALSE), # Image input
radioButtons(paste0("multiImages_sep", i), label="Type of separator in the file", choices = c("Tab", "Comma", "Semicolon"), selected="Tab", inline=TRUE),
radioButtons(paste0("multiImages_dec", i), label="Type of decimals in the file", choices = c("Point", "Comma"), selected="Point", inline=TRUE),
checkboxInput(paste0("multiImages_header", i), label = "Header", value = TRUE),
fileInput(paste0("multiImages_dataFile", i), paste0("Choose data file number ", i), multiple=FALSE), # Data input
fileInput(paste0("multiImages_zipFile", i), paste0("Choose Roi set number ", i), multiple=FALSE),)}), # ROI.zip input
actionButton("multiImages_validate", "Combine files", style="color: #fff; background-color: #337ab7; border-color: #2e6da4"))
}
})
multiImages <- reactiveValues(img = list(), legend = NULL, data = list(), zip = list(), resize=FALSE, fullImg = list())
observeEvent({
input$multiImages_validate
}, {
req(input[[paste0("multiImages_imgFile", input$multiImages_nb)]],
input[[paste0("multiImages_dataFile", input$multiImages_nb)]],
input[[paste0("multiImages_zipFile", input$multiImages_nb)]],
input$multiImages_legendFile, is.null(input$imgFile), is.null(input$dataFile),
is.null(input$legendFile), is.null(input$zipFile))
global$resolution <- attr(read_tif(input[[paste0("multiImages_imgFile", 1)]]$datapath), "x_resolution")
sepLegend <- switch(input$multiImages_legendSep, "Tab"="\t", "Comma"=",", "Semicolon"=";")
multiImages$legend <- read.table(input$multiImages_legendFile$datapath, header=TRUE, sep=sepLegend, dec=".")
lapply(1:input$multiImages_nb, function(i) { # For each image downloaded, store them in a list (same as in normal input images with a z dimension)
multiImages$img[[i]] <- read_tif(input[[paste0("multiImages_imgFile", i)]]$datapath) # Image
multiImages$img[[i]] <- as_EBImage(multiImages$img[[i]])
if (dim(multiImages$img[[i]])[1] > 1200 & dim(multiImages$img[[i]])[2] > 1200) {
multiImages$img[[i]] <- EBImage::resize(multiImages$img[[i]], dim(multiImages$img[[i]])[1]/2, dim(multiImages$img[[i]])[2]/2)
multiImages$resize <- TRUE
}
separator <- switch (input[[paste0("multiImages_sep",i)]], "Tab"="\t", "Comma"=",", "Semicolon"=";")
decimal <- switch (input[[paste0("multiImages_dec",i)]], "Point"=".", "Comma"=",")
multiImages$data[[i]] <- read.table(input[[paste0("multiImages_dataFile", i)]]$datapath,header=input[[paste0("multiImages_header", i)]], sep=separator, dec=decimal)
multiImages$zip[[i]] <- read.ijzip(input[[paste0("multiImages_zipFile", i)]]$datapath)
if (multiImages$resize == TRUE) {
multiImages$resolution <- multiImages$resolution*2
for (j in c(1:length(multiImages$zip[[i]]))) {
multiImages$zip[[i]][[j]]$coords <- multiImages$zip[[i]][[j]]$coords/2
}
}
})
})
observeEvent({
multiImages$img
multiImages$data
multiImages$zip
multiImages$legend
},{
req(length(multiImages$img)==input$multiImages_nb,
length(multiImages$data)==input$multiImages_nb,
length(multiImages$zip)==input$multiImages_nb,
multiImages$legend)
multiImages$fullData <- multiImages$data[[1]]
multiImages$fullData$Slice <- 1
multiImages$fullZip <- multiImages$zip[[1]]
multiImages$fullImg[[1]] <- multiImages$img[[1]]
for (i in 2:input$multiImages_nb) { # Add a slice column to the data depending on the position of the image in the list
if (ncol(multiImages$data[[i]])==ncol(multiImages$fullData)-1) {
multiImages$data[[i]]$Slice <- i
multiImages$fullData <- rbind(multiImages$fullData, multiImages$data[[i]])
}
if (dim(multiImages$img[[i]])[1] == dim(multiImages$fullImg[[1]])[1] & dim(multiImages$img[[i]])[2] == dim(multiImages$fullImg[[1]])[2] &
dim(multiImages$img[[i]])[3] == dim(multiImages$fullImg[[1]])[3]) {
multiImages$fullImg[[i]] <- multiImages$img[[i]] # if all dimensions are corresponding, store the image in the fullImg variable
}
multiImages$fullZip <- append(multiImages$fullZip, multiImages$zip[[i]]) # add the zip corresponding to the fullZip variable
}
if (length(multiImages$fullZip)==nrow(multiImages$fullData) & length(multiImages$fullImg)==input$multiImages_nb) { # if all images have been add in the full variables
global$legend <- multiImages$legend
global$img <- multiImages$fullImg
global$data <- multiImages$fullData
global$data$ID <- 1:nrow(global$data)
global$zip <- multiImages$fullZip
global$nChan <- dim(global$img[[1]])[3]
global$nFrame <- input$multiImages_nb
global$resolution <- multiImages$resolution
} # store them in the global variables
else {
output$errorMultiImages <- renderText ({
if ((length(multiImages$fullZip) != nrow(multiImages$fullData) | length(multiImages$fullImg) !=input$multiImages_nb)) {
paste0("Your files don't match with each other. Please select files with the same dimensions (images with same dimensions, data files with the same number of columns)")
}
})
}
})
#=============================================================================
## MENU SEGMENTATION
# store the path of the image file in a variable
observeEvent(eventExpr= input$seg_imgFile, handlerExpr = { seg$imgPath <- input$seg_imgFile$datapath }, label = "files")
# read image and resize it if necessary
observeEvent({seg$imgPath},
{req(seg$imgPath)
if ((dim(read_tif(seg$imgPath)))[4] ==1) {
seg$img <- read_tif(seg$imgPath)
seg$img <- as_EBImage(seg$img)
EBImage::colorMode(seg$img) <- "Grayscale"
seg$nChan <- dim(seg$img)[3]
seg$resolution <- attr(read_tif(seg$imgPath), "x_resolution")
if (dim(seg$img)[1] > 1024 | dim(seg$img)[2] > 1024) { # if the image is too big (more than 1024*1024), resize it proportionaly
seg$coeff_prop <- 1024/max(dim(seg$img)[1], dim(seg$img)[2]) # the highest dimension is resized to 1024
seg$img <- EBImage::resize(seg$img, dim(seg$img)[1] * seg$coeff_prop, dim(seg$img)[2]*seg$coeff_prop) # resize the image
seg$resize <- TRUE
seg$resolution <- seg$resolution*seg$coeff_prop # adapt the resolution
}
}
else if ((dim(read_tif(seg$imgPath)))[4] > 1) { # If multiple frame
seg$nFrame <- (dim(read_tif(seg$imgPath)))[4] # number of frames on the image
seg$resolution <- attr(read_tif(seg$imgPath, frames=1), "x_resolution") # resolution of the image (number of microns corresponding to 1 pixel)
for (i in c(1:seg$nFrame)) { # for any frame of the image
seg$img[[i]] <- read_tif(seg$imgPath, frames=i) # store each frame as an element of a list
seg$img[[i]] <- as_EBImage(seg$img[[i]]) # and transform it in a EBImage object
EBImage::colorMode(seg$img[[i]]) <- "Grayscale"
if (dim(seg$img[[i]])[1] > 1024 | dim(seg$img[[i]])[2] > 1024) { # if the image is too big (more than 1024*1024), resize it proportionaly
seg$coeff_prop <- 1024/max(dim(seg$img[[i]])[1], dim(seg$img[[i]])[2]) # the highest dimension is resized to 1024
seg$img[[i]] <- EBImage::resize(seg$img[[i]], dim(seg$img[[i]])[1]* seg$coeff_prop, dim(seg$img[[i]])[2]*seg$coeff_prop) # resize the image
seg$resize <- TRUE
}
}
if (seg$resize == TRUE) {
seg$resolution <- seg$resolution*seg$coeff_prop # adapt the global resolution of the image
}
seg$nChan <- dim(seg$img[[1]])[3] # number of channel on the image
}
})
# Observer which modify the actual image of each menu depending on the actual frame selected
observe({req(input$seg_imgFile)
if (seg$nFrame > 1) {
seg$actualImg <- seg$img[[seg$imgFrame]]
}
else {
seg$actualImg <- seg$img
}
})
observeEvent( {
seg$imgFrame
}
,{
req(length(seg$img) > 0)
if (seg$nFrame > 1) {
seg$actualImg <- seg$img[[seg$imgFrame]]
}
})
# UI to choose channel to display for the image
output$seg_channel <- renderUI({
req(length(seg$img) != 0)
radioGroupButtons(inputId = "seg_channel_in", label = "Channel to segment", choices=c(1:seg$nChan), selected=seg$imgChan, justified=TRUE)
})
# Modification of channel when modification of channel slider
observeEvent(eventExpr=input$seg_channel_in,
handlerExpr={seg$imgChan = as.numeric(input$seg_channel_in)})
# UI to choose slice to display
output$seg_frame <- renderUI ({
req(length(seg$img) != 0, seg$nFrame > 1)
radioGroupButtons(inputId = "seg_frame_in", label = "Slice to display", choices=c(1:seg$nFrame), selected=seg$imgFrame, justified=TRUE)
})
# Modification of frame when modification of frame slider
observeEvent(eventExpr=input$seg_frame_in,
handlerExpr={
if (seg$nFrame > 1) {
seg$imgFrame <- as.numeric(input$seg_frame_in)
seg$imgChan <- as.numeric(input$seg_channel_in)
}
})
# Brightness slider
output$seg_brightnessSlider <- renderUI ({
if (input$seg_brightnessImg) {
sliderInput("seg_brightnessRate", "% of initial brightness", min=100, max=500, value=100)
}
})
# PNG Image
observeEvent(eventExpr= {
input$seg_imgFile
input$seg_channel_in
input$seg_frame_in
seg$imgFrame
seg$imgChan
input$seg_brightnessRate
input$seg_brightnessImg
#seg$actualImg
},
handlerExpr=
{ if ((length(seg$img) != 0)) {
req(seg$actualImg)
out <- tempfile(fileext='.png') # temporary png file
png(out, height=dim(seg$actualImg)[2], width=dim(seg$actualImg)[1]) # creates a png image in this temporary file with the same dimensions as the global image
seg$prevImg <- seg$actualImg[,,seg$imgChan,1]
if (input$seg_brightnessImg==TRUE) {
req(input$seg_brightnessRate)
seg$prevImg <- magick::image_read(seg$actualImg[,,seg$imgChan,1])
seg$prevImg <- magick::image_modulate(seg$prevImg,saturation=100,
brightness = as.numeric(input$seg_brightnessRate),
hue=100)
seg$prevImg <- magick::as_EBImage(seg$prevImg)
}
display(seg$prevImg, method="raster") # display actual image
width = 1
dev.off() # end modification of the png file
out <- normalizePath(out, "/") # normalize path
seg$imgPNG <- EBImage::readImage(out) # read the PNG image
}}, ignoreNULL=FALSE)
# display image
observeEvent(eventExpr =
{seg$imgPNG},
handlerExpr = {
output$seg_img <- EBImage::renderDisplay({
req(!is.null(seg$imgPNG))
EBImage::display(seg$imgPNG, method = 'browser')
})
})
# define watershed and cellpose parameters
observe({req(seg$actualImg)
if (input$seg_algo== "Watershed"){
output$seg_algo_para <- renderUI(
tagList(
box(width= NULL,solidHeader = TRUE, status = "primary", title = "Watershed segmentation parameters",
sliderInput("seg_sigma", "Standard deviation for gaussian filter : ", value = 2, min = 0, max = 20, step = 0.1),
sliderInput("seg_threshold","Threshold : ", value = 25, min = 0, max = 100, step = 0.1 ),
sliderInput("seg_min_size","Size of the smallest allowable object : ", value = 45, min = 0, max = 200, step = 0.1 ),
actionButton("seg_ws","Run segmentation")
)
)
)
}
else {
output$seg_algo_para <- renderUI(
tagList(
box(width = NULL, solidHeader = TRUE, status = "primary", title = "Cellpose segmentation parameter",
selectInput("seg_cp_mode","Select cellpose model : ", choices = list("nuclei segmentation" = 'nuclei', "cytoplasm segmentation" = 'cyto'), selected = 'cyto'),
actionButton("seg_cp","Run segmentation")
)
)
)
}
})
# resize matrix for python
observeEvent(eventExpr= {