Major Update on shiny part from Lloyd Izard M2

server.R

-# Shiny app allowing: plot and parameters for microbial growth dataset
-# Marc Garel, Severine Martini, Christian Tamburini
-### October 2017
-
-
-library(shiny)
-
-shinyServer(function(input, output) {
-  output$contents <- renderTable({
-    
-    inFile <- input$file1
-    
-    if (is.null(inFile))
-      return(NULL)
-    
-    dd<-read.csv(inFile$datapath, header=input$header, sep=input$sep, 
-             quote=input$quote)
-    do<-reactiveValues(data=dd)
-
-   #calcul coef pour affichage dans en dessous du graph
-    x=rep(dd[,1],ncol(dd)-1)
-    y=unlist(dd[,2:ncol(dd)])
-    crssce=data.frame(y,x)
-    fit=nls(y~SSlogis(x,Asym,xmid,scal),crssce)
-    param=summary(fit)$parameters
-    mu=round(1/param[3,1],digits=3)
-    sdmu=round((1/summary(fit)$parameters[3,1])-(1/(summary(fit)$parameters[3,1]+summary(fit)$parameters[3,2])), digits=3)
-    asym=round(param[1,1],digits=3)
-    sdasym=round(summary(fit)$parameters[1,2], digits=3)
-    #####Le graph
-    
-    
-    
-    output$plot <- renderPlot({
-      
-      
-      ##############
-      x=rep(dd[,1],ncol(dd)-1)
-      y=unlist(dd[,2:ncol(dd)])
-      crssce=data.frame(y,x)
-      require("quantreg")
-      # limit axes extended
-      maxx=max(x,na.rm=T)+(max(x,na.rm=T)/4)
-      maxy=max(y,na.rm=T)+(max(y,na.rm=T)/4)
-      w0=seq(0,maxx)
-      # modlog2=nls(x ~ SSlogis(y,Asym,xmid,scal),crssce)
-      # modlog=nls(y ~ SSlogis(x,Asym,xmid,scal),crssce)
-      coef=getInitial(y~SSlogis(x,asym,xmid,scal),data=cbind.data.frame(x,y))
-      mu=1/coef[3]
-      # output plot
-      plot(x,y)
-      points(x,y,xlim=c(0,maxx),ylim=c(0,maxy),xlab="temps(h)",ylab="DO600nm",las=1,pch=22,bg=1)
-      lines(w0,SSlogis(w0,coef[1],coef[2],coef[3]),lwd=1.5)
-      q1b=nlrq(y~SSlogis(x,K, xmid, r),tau=0.25,data=crssce)
-      q3b=nlrq(y~SSlogis(x,K, xmid, r),tau=0.75,data=crssce)
-      q05b=nlrq(y~SSlogis(x,K, xmid, r),tau=0.025,data=crssce)
-      q95b=nlrq(y~SSlogis(x,K, xmid, r),tau=0.975,data=crssce)
-      lines(w0, predict(q1b, newdata=list(x=w0)), col="blue",lty=2)
-      lines(w0, predict(q3b, newdata=list(x=w0)), col="blue",lty=2)
-      lines(w0, predict(q05b, newdata=list(x=w0)), col="blue",lty=3)
-      lines(w0, predict(q95b, newdata=list(x=w0)), col="blue",lty=3)
-
-      observeEvent(input$clicks, {print(as.numeric(do$data))})
-     
-    })
-    output$ex_out <- renderPrint({return(c(mu,sdmu,asym,sdasym))})
-    output$ex_out2 <- renderPrint({return(dd)})
-
-     })
-      output$txtout <- renderText({
-    
-    paste("Martini, S., B. Al Ali, M. Garel, and others. 2013. Effects of Hydrostatic Pressure on Growth and Luminescence of a Moderately-Piezophilic Luminous Bacteria Photobacterium phosphoreum ANT-2200 A. Driks [ed.]. PLoS One 8: e66580. doi:10.1371/journal.pone.0066580")
-  })
-
-})
-
-# shinyServer(function(input, output, session) {
-#output$main_plot <- renderPlot({
-#    plot(dd$H,dd$A, type=input$plotType)
-
-#   
-#   
-#   output$table <- DT::renderDataTable({
-#     DT::datatable(cars)
-#   })
-# })
\ No newline at end of file
+##################################################
+## Project: Shiny app allowing plot and parameters for microbial growth dataset
+## Script purpose: Perform logistic regression to estimate growth rate and maximum cells density.
+## Date: October 2017
+## Author: Marc Garel, Severine Martini, Christian Tamburini
+##################################################
+
+###  libraries
+library(shiny)
+library(DT)
+library(rgl)
+library(quantreg)
+library(SparseM)
+library(rmarkdown)
+
+# server script
+shinyServer(
+  
+  function(input, output, session){
+    
+    ####### Upload data #######
+    getData <- reactive({
+      inFile <- input$file1
+      if (is.null(inFile)) return(NULL)
+      df <- read.csv(inFile$datapath, header = input$header, sep = input$sep, dec = input$dec)
+      return(df)
+      })
+    
+    ####### Calcul des estimateurs #######
+    calculate <- reactive({
+      
+      df <- getData()
+      req(df)
+      
+      # calcul coef pour affichage dans en dessous du graph
+      
+      x <- rep(df[,1], ncol(df)-1)
+      y <- unlist(df[, 2:ncol(df)])
+      
+      crssce <- data.frame(x, y)
+      
+      fit <- nls(y ~ SSlogis(x, Asym, xmid, scal), crssce)
+      param <- summary(fit)$parameters
+      
+      # cat(param,'\n\n')
+      
+      scal <- param[3,1]
+      sdscal <- param[3,2]
+      
+      mu <- round(1 / scal, digits = 3)
+      sdmu <- round((1 / scal) - (1 / (scal + sdscal)), digits = 3)
+      
+      asym <- param[1,1]
+      sdasym <- param[1,2]
+      
+      xmid <- param[2,1]
+      sdxmid <- param[2,2]
+      
+      params <- as.data.frame(cbind(scal, sdscal, mu, sdmu, asym, sdasym, xmid, sdxmid))
+      colnames(params) <- c("scal", "sdscal", "Growth rate (Gr)", "Gr Standard deviation", "Maximum population (Mp)", "Mp Standard deviation",
+                            "Start expo. phase (Ste)", "Ste Standard deviation")
+      
+      return(params)
+      })
+    
+    ####### Raw_plot #######
+    output$raw_plot <- renderPlot({
+
+      df <- getData()
+      req(df)
+      
+      x <- rep(df[, 1], ncol(df) - 1)
+      y <- unlist(df[, 2:ncol(df)])
+      crssce <- data.frame(y, x)
+
+      # limit axes extended
+      maxx <- max(x, na.rm = T) + (max(x, na.rm = T) / 4)
+      maxy <- max(y, na.rm = T) + (max(y, na.rm = T) / 4)
+      w0 <- seq(0, maxx)
+      
+      coef <- getInitial(y ~ SSlogis(x, asym, xmid, scal), data = cbind.data.frame(x, y))
+      mu <- 1 / coef[3]
+
+      # output plot
+      plot(x, y, xlab = input$xlabel, ylab = input$ylabel, xlim = c (0, maxx),
+           ylim = c(0, maxy), las = 1, pch = 22, bg = 1)
+      lines(w0, SSlogis(w0, coef[1], coef[2], coef[3]), lwd = 1.5)
+      grid()
+
+      q1b <- nlrq(y ~ SSlogis(x, K, xmid, r), tau = 0.25, data = crssce)
+      q3b <- nlrq(y ~ SSlogis(x, K, xmid, r), tau = 0.75, data = crssce)
+      
+      q05b <- nlrq(y ~ SSlogis(x, K, xmid, r), tau = 0.025, data = crssce)
+      q95b <- nlrq(y ~ SSlogis(x, K, xmid, r), tau = 0.975, data = crssce)
+
+      lines(w0, predict(q1b, newdata = list(x = w0)), col = "blue", lty=2)
+      lines(w0, predict(q3b, newdata = list(x = w0)), col = "blue", lty=2)
+      
+      lines(w0, predict(q05b, newdata = list(x = w0)), col = "blue", lty=3)
+      lines(w0, predict(q95b, newdata = list(x = w0)), col = "blue", lty=3)
+      #
+      })
+    
+    ####### Affichage du tableau des parametres #######
+    output$parameters <- DT::renderDataTable({
+      params <- calculate()
+      paste("The table below gives the parameters estimation.")
+      DT::datatable(params[,3:8])
+      #
+      })
+
+    ####### Affichage du tableau initial #######
+    output$raw_data <- DT::renderDataTable({
+      df <- getData()
+      DT::datatable(df)
+      #
+      })
+    
+    ####### Affichage de la citation #######
+    output$citation <- renderText({
+      paste("Martini, S., B. Al Ali, M. Garel, and others. 2013. Effects of Hydrostatic Pressure on Growth and Luminescence of a Moderately-Piezophilic Luminous Bacteria\nPhotobacterium phosphoreum ANT-2200 A. Driks [ed.]. PLoS One 8: e66580. doi:10.1371/journal.pone.0066580")
+      #
+      })
+    
+    ####### Fonction de cout #######
+    cost <- function(Asym, scal, xmid, xdata, ydata){
+      
+      ymod <- SSlogis(xdata, Asym, xmid, scal)
+      SSE <- sum((ymod - ydata)^2, na.rm = T)
+      
+      return(SSE)
+      }
+    
+    ####### Verify_plot #######
+    output$verify_plot_2D <- renderPlot({
+
+      # on recupereles donnes de l'utilisateur
+      df <- getData()
+      req(df)
+
+      # on calcule les parametres
+      params <- calculate()
+      req(params)
+      
+      # on assigne les valeurs de params a nos differentes variables
+      scal <- params[1,1]
+      mu <- params[1,3]
+      Asym <- params[1,5]
+      xmid <- params[1,7]
+      
+      # cat(Asym, '\n\n')
+      # cat(mu, '\n\n')
+      # cat(scal, '\n\n')
+      # cat(xmid, '\n\n')
+      
+      x <- rep(df[, 1], ncol(df) - 1)
+      y <- unlist(df[, 2:ncol(df)])
+      
+      Asymmin <- Asym - (5*Asym) / 100
+      Asymmax <- Asym + (5*Asym) / 100
+
+      scal_min <- scal - (5*scal) / 100
+      scal_max <- scal + (5*scal) / 100
+      
+      mu_min <- 1/scal_max
+      mu_max <- 1/scal_min
+        
+      # cat(Asymmin, '\n\n')
+      # cat(Asymmax, '\n\n')
+      # 
+      # cat(scal_min, '\n\n')
+      # cat(scal_max, '\n\n')
+      # 
+      # cat(mu_min, '\n\n')
+      # cat(mu_max, '\n\n')
+      
+      SSE <- cost(Asym, scal, xmid, x, y)
+      
+      # cat(SSE, '\n\n')
+      
+      scal_Vector <- seq(scal_min, scal_max, length=100)
+      mu_Vector <- seq(mu_min, mu_max, length=100)
+      Asym_vector <- seq(Asymmin, Asymmax, length=100)
+      costV <- Vectorize(cost, c("scal", "Asym"))
+      z <- outer(Asym_vector, scal_Vector, FUN = "costV", xmid = xmid, xdata = x, ydata = y)
+      
+      # print(costV, '\n\n')
+      # cat(Asym, '\n\n')
+      # cat(mu, '\n\n')
+      # cat(head(z), '\n\n')
+      
+      contour(Asym_vector, mu_Vector, z, nlevels = 30, xlab = "K (en cellules/ml)", ylab = "Growth rate (h-1)", 
+              xlim = range(Asym_vector, finite = TRUE),
+              ylim = range(mu_Vector, finite = TRUE),
+              zlim = range(z, finite = TRUE))
+      points(Asym, mu, pch = 19, col = "lightseagreen")
+      })
+    
+    ####### Verify plot 3D ####### 
+    output$verify_plot_3D <- renderRglwidget({
+      
+      df <- getData()
+      req(df)
+      
+      params <- calculate()
+      req(params)
+      
+      scal <- params[1,1]
+      mu <- params[1,3]
+      Asym <- params[1,5]
+      xmid <- params[1,7]
+      
+      x <- rep(df[, 1], ncol(df) - 1)
+      y <- unlist(df[, 2:ncol(df)])
+
+      Asymmin <- Asym - (5*Asym) / 100
+      Asymmax <- Asym + (5*Asym) / 100
+      
+      scal_min <- scal - (5*scal) / 100
+      scal_max <- scal + (5*scal) / 100
+      
+      mu_min <- 1/scal_max
+      mu_max <- 1/scal_min
+      
+      SSE <- cost(Asym, scal, xmid, x, y)
+      
+      scal_Vector <- seq(scal_min, scal_max, length=100)
+      mu_Vector <- seq(mu_min, mu_max, length=100)
+      Asym_vector <- seq(Asymmin, Asymmax, length=100)
+      costV <- Vectorize(cost, c("scal", "Asym"))
+      z <- outer(Asym_vector, scal_Vector, FUN = "costV", xmid = xmid, xdata = x, ydata = y)
+      
+      rgl.open(useNULL=T)
+      
+      bg3d("white")
+      view3d( theta = 0, phi = -45)
+      
+      plot3d(rep(Asym, length(Asym)),
+             rep(mu, each = length(mu)), z,
+             xlim = range(Asym_vector, finite = TRUE),
+             ylim = range(mu_Vector, finite = TRUE),
+             zlim = range(z, finite = TRUE),
+             type = "n", xlab = "Maximum population", ylab = "Growth rate", zlab = "SSE")
+      
+      surface3d(Asym_vector, mu_Vector, z, col = "lightseagreen")
+      points3d(Asym, mu, SSE, size = 5, col = "black")
+      
+      rglwidget()
+      })
+    
+    output$downloadData <- downloadHandler(
+      filename = function() {
+        paste('data-', Sys.Date(), '.txt', sep='')
+      },
+      content = function(con) {
+        param <- calculate()
+        write.table((param[,3:8]), con, row.names = F, col.names = T, sep = '\t')
+        }
+      )
+    
+    })
\ No newline at end of file

ui.R

-# Shiny app allowing: plot and parameters for microbial growth dataset
-# Marc Garel, Severine Martini,  Christian Tamburini
-### October 2017
-library(shiny)
-
-
-shinyUI(fluidPage(
-  headerPanel("Application of a logistic model on microbial datasets"),
-# load dataset
-titlePanel("Upload file"),
-  sidebarLayout(
-    sidebarPanel("This is to perform logistic regression to estimate growth rate and maxium cells density.",
-      fileInput('file1', 'Choose CSV File',
-                accept=c('text/csv', 
-                         'text/comma-separated-values,text/plain', 
-                         '.csv', '.txt')),
-      tags$hr(),
-      checkboxInput('header', 'Header', TRUE),
-      radioButtons('sep', 'Separator',
-                   c(Comma=',',
-                     Semicolon=';',
-                     Tab='\t'),
-                   ','),
-      radioButtons('quote', 'Quote',
-                   c(None='',
-                     'Double Quote'='"',
-                     'Single Quote'="'"),
-                   '"')
-    ),
-    
-    mainPanel(
-      h4("Ref"),
-      verbatimTextOutput("txtout"),
-      withMathJax(),
-      helpText('Logistic equation $$x(t) =r.x_0.(1-\\frac{x_0}K)$$'),
-      tabsetPanel(
-      tabPanel("Details",tableOutput('contents'),  helpText('You have to format data with tab separartor and dot for decimal. Remove all space in the header from your dataset. Replace it by "_"'),helpText('In your data set there are two arrays, time and cells density (Optic density, Cells number, Biomass ....) ')),
-      tabPanel("Data",verbatimTextOutput('ex_out2')),
-      tabPanel("Plot",plotOutput('plot'), verbatimTextOutput('ex_out'))
-      )
-    )
-  )
-  
-))
-
+##################################################
+## Project: Shiny app allowing plot and parameters for microbial growth dataset
+## Script purpose: Perform logistic regression to estimate growth rate and maximum cells density
+## Date: October 2017
+## Author: Marc Garel, Severine Martini, Christian Tamburini
+##################################################
+
+###  libraries
+library(shiny)
+library(DT)
+library(rgl)
+library(quantreg)
+library(SparseM)
+library(rmarkdown)
+
+### ui script
+shinyUI(
+  
+  fluidPage(
+    
+    navbarPage("Application of a logistic model on microbial datasets",
+               tabPanel("Upload data",
+                        sidebarLayout(
+                          sidebarPanel("",
+                                       fileInput('file1', 'Upload your dataset (.csv or .txt)',
+                                                 accept=c('text/csv', 'text/comma-separated-values,text/plain', '.csv', '.txt')),
+                                       # header or not header
+                                       checkboxInput('header', 'Header', TRUE),
+                                       
+                                       #
+                                       radioButtons('sep', 'Dataset separator',
+                                                    c(Comma=',', Semicolon=';', Tab='\t'), selected="\t"),
+                                       
+                                       #
+                                       radioButtons('dec', 'Decimal separator', 
+                                                    c(Point='.', Comma=','), selected="."),
+                                       
+                                       #
+                                       textInput("xlabel", "xlabel", value = "Time (h)"),
+                                       
+                                       #
+                                       textInput("ylabel", "ylabel", value = "Biomass")
+                                       ),
+                          mainPanel(
+                                    helpText('This is to perform logistic regression to estimate growth rate and maximum cells density. In order to run this application, you have to format your dataset
+                                    with tabulation separators. Also, remove all spaces in the dataset header (prefer to use "_" when needed). 
+                                    Organise your dataset so that there is only two arrays. The first one being the time and the second the cells density 
+                                    (e. g. optic density, cell number, biomass). This application proposes a method to perform logistic regression to estimate growth rate as well as maximum cells density.'),
+                                    withMathJax(helpText('The logistic equation is define as $$x(t) = r.x_0.(1-\\frac{x_0}K)$$'))
+                                    )
+                          )
+                        ),
+               
+               tabPanel("Uploaded data", DT::dataTableOutput("raw_data")),
+               tabPanel("Plot", plotOutput("raw_plot"), DT::dataTableOutput("parameters"),
+                        downloadButton("downloadData", "Download")),
+               navbarMenu("Verify parameters estimation",
+                tabPanel("2D", plotOutput("verify_plot_2D")),
+                tabPanel("3D", rglwidgetOutput("verify_plot_3D", width = 800, height = 800))),
+               tabPanel("Citation", verbatimTextOutput("citation"))
+               )
+    )
+  )
+