################################################################################ # 4PL and Linear bioassay evaluation # Author: Franz Innerbichler # 1.4.2026 ################################################################################ library(shiny) library(shinydashboard) library(shinyjs) library(shinyAce) library(shinydashboard) library(shinycssloaders) library(shinyBS) library(purrr) library(gslnls) library(tidyverse) library(ggplot2) library(reshape2) library(openxlsx) library(DT) library(ggpubr) library(gridExtra) library(drc) library(twopartm) library(car) library(dplyr) library(scales) source("Global.R") #### ui ---- ui <- dashboardPage( dashboardHeader(title = "Plateflow"), dashboardSidebar( sidebarMenu( img(src="logo.png", width=230), menuItem("Home", tabName="home", icon=icon("home")), menuItem("Data template", tabName = "template", icon=icon("table"), menuSubItem( tags$li(a("EXCEL File", target="self",href="TestFile.xlsx"))) ), # menuItem("User Manual /Validation", tabName = "manual", icon=icon("book"), # tabName here and in dashboard body need to be identical # menuSubItem(icon = NULL, tags$li(a("Document", target="self",href="UserManual.pdf"))) # ), menuItem("EXCEL upload", tabName="Dataupload", icon=icon("magnet", lib="glyphicon")), menuItem("4PL simulation", tabName="fourPL", icon=icon("chart-line", lib="font-awesome")), #menuItem("XLSX diagnostics", tabName="XLdiagn", icon=icon("chart-bar", lib="font-awesome")), # menuItem("Linear regression + report", tabName="pla", icon=icon("pencil", lib="glyphicon")), menuItem("Wizard", tabName="wizard", icon=icon("chart-column", lib="font-awesome"))#, #menuItem("Documentation", tabName="documentation", icon=icon("chart-area", lib="font-awesome")) ), tags$footer(HTML(paste(tags$strong(tags$u("InnerAnalytics")), paste(rep(" ",9), collapse=""), "Developer:", paste(rep(" ",9), collapse=""), "Host on:", paste(rep(" ",9), collapse=""), "Version:")), align="left", style= "position:fixed; bottom:0;width=100%; background: #FFC337BB; font-family: Times New Roman; font-size:100%; padding: 5px; color:#4545BA; box-sizing: border-box; z-index: 1000;")), dashboardBody( fluidPage( tabItems( tabItem(tabName = "home", htmlOutput("homePage")), tabItem(tabName = "Dataupload", uiOutput("Dataupload")), tabItem(tabName = "fourPL", uiOutput("fourPL")), #tabItem(tabName = "XLdiagn", uiOutput("XLdiagn")), #tabItem(tabName = "pla", uiOutput("pla")), tabItem(tabName = "wizard", uiOutput("wizard"))#, #tabItem(tabName = "documentation", uiOutput("docu")) ) ) ), skin="blue" ) #### server ---- server <- function(input, output, session) { #### reactive values ---- Dat <- reactiveValues() REP <- reactiveValues() REPlin <- reactiveValues() #### renderUIs ---- output$homePage <- renderUI({ navbarPage("Home", tabPanel("Limit setting", tags$img(src="logo.png", class="adv_logo"), h4("Introduction to the bioassay software"), #tags$mark("linear regression"), br(), column(3, tags$table(id="dose-table", numericInput("lEACdiffla","lower EAC for diff. of LA", -0.175, step=0.001), numericInput("uEACdiffla","upper EAC for diff. of LA", 0.189, step=0.001), numericInput("lEACratiola","lower EAC ratio of LAs", 0.005, step=0.001), numericInput("uEACratiola","upper EAC for ratio of LAs", 100, step=1), numericInput("lEACratioSlope","lower EAC for ratio of slopes", 0.55, step=0.01), numericInput("uEACratioSlope","upper EAC for ratio of slopes", 1.84, step=0.1), numericInput("lEACratioua","lower EAC for ratio of UAs", 0.75, step=0.1), numericInput("uEACratioua","upper EAC for ratio of UAs", 1.33, step=0.1), numericInput("lowerPot","lower EAC for potency", 75, step=1), numericInput("upperPot","upper EAC for potency", 133, step=1), numericInput("lEACratioAdiff","lower EAC of ratio of asymptote differences", 0.75, step=0.01), numericInput("uEACratioAdiff","upper EAC of ratio of asymptote differences", 1.33, step=0.01) )) ), tabPanel("Documentation", h4("Introduction "), h4("Information on dilution setting"), "(for details see:", a(href="ADONIS.pdf","Whitepaper", download=NA, target="_blank"),")",tags$br(), "Bend points are calculated according to following formula:", withMathJax(" $$bp_{1/2} = \\pm\\frac{1.31696}{Hill's slope}$$")), tabPanel("Configuration", verbatimTextOutput("sessioninfo")) ) }) ##### UI XL ---- output$Dataupload <- renderUI({ navbarPage(title="Information", tabPanel(title = "Real data", tabsetPanel( tabPanel("Data input", column(3, #img(src="Screenshot.png", width=200), box(title = "Upload", status="warning",solidHeader = T, width=12, "Please upload your EXCEL file here", fileInput("iFile",'',accept=".xlsx")), uiOutput(outputId = "sheetName"), "For data format in the EXCEL file see Data template", "If no data are uploaded, the settings to the right are used for calculations.", tags$head(tags$style(HTML("label {font-size:80%;margin-bottom: 3px;margin-top: 3px;}"))), div(checkboxInput("PureErr", "Should pure error be used for calculation of CIs?", FALSE), style = "font-size: 24px !important;color: #C2173F"), #actionLink("selectall","SelectAll"), h5("\n\n\n Author: Franz Innerbichler, InnerAnalytics")), column(4, h4("Suitability tests for 4-parametric logistic regression"), checkboxGroupInput("selectedSSTs", "Which suitability tests to be used?", choices= c("F-test on Regr."="1", "EQ-test on lower asymptote difference"= "2", "EQ-test on ratio of lower asymptote"= "3","EQ-test on ratio of Hill slopes"= "4", "EQ-test on ratio of upper asymptote"= "5", "F-test on non-linearity"="6", "EQ-test on ratio of asymptote differences"= "7"), selected= c("1","4","5","6","7")), h4("Suitability tests for Parallel Line Assay"), checkboxGroupInput("selectedSSTsLinear", "Which suitability tests to be used?", choices= c("F-test on Regr."="1", "F-test on non-linearity"= "2", "F-test on R^2 A"= "3","F-test on R^2 B"= "4", "F-test on slope A"= "5", "F-test on slope B"="6", "F-test on non-parallelism"= "7", "F-test on preparation"="8"), selected= c("1","2","3","4","5","6","7","8")) ), column(4, h4("Example of EXCEL file "), h4("with column of dilutions and at least 2 columns of reference and the same amount of columns with test sample readouts."), tags$img(src="ExampleXL.png", class="adv_logo", width="100%"), plotOutput("plotSing", width="400px", height="300px")) ), tabPanel("4pl-Analysis", tags$style(HTML("pre { color: black; background-color: #FFE1FF; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif;font-size: 12px;} ")), wellPanel( fluidRow( column(4, #h5("Diagnostics only shown if EXCEL is uploaded"), htmlOutput("PureErrW2"), tags$head(tags$style("#PureErrW2{color: red; font-size: 16px; font_style: italic;}")), tableOutput("Filesampl"), tableOutput("relpotTestTab"), plotOutput("relpotTestPlot", width="300px", height="150px"), # Pot CI plot h4("Akaike Information Criterion"), tableOutput("AIC"), h5("First row: restricted model; 2nd row: unrestricted model"), h5("Smaller values of AIC indicate better fit to the data"), tableOutput("VarDiagn") ), column(8, plotOutput("XLplot"), "Footnote: bendpoints (linear part) and asymptote points (point where asymptote is reached) are plotted in dashed and dotted lines. They indicate whether the linear part and asymptotes are catched with the current dilutions.", "Black line is the true slope at EC50 of REF.", DTOutput("pottab4plXL"), plotOutput("diagnplot"), DTOutput("EQtests"), DTOutput("pottab4plTransXL"), tableOutput("ANOVAXLS") ) ))), tabPanel("linear Analysis", sidebarLayout( sidebarPanel( width=2, fluidRow( column(12, numericInput("EACLinlow","Potency CL to be > than",value=80), numericInput("EACLinupp","Potency CL to be < than", value=125) ) )), mainPanel( tabsetPanel(id="tabs", tabPanel("Plot and models", column(12, htmlOutput("PureErrWLinXL"), tags$head(tags$style("#PureErrWLinXL{color: red; font-size: 16px; font_style: italic;}")), plotOutput("plotLin"), "Delta method is used for potency CIs", DT::dataTableOutput("pottab"), h4("Unrestricted linear model (SSSI):"), tableOutput("SummaryModABu"), h4("Restricted linear model (CSSI):"), tableOutput("SummaryModAB"), )), tabPanel("Tests and ANOVAA", column(12, h3("Tests for linear PLA:"), box(title="Suitability tests", status="primary",solidHeader = T, width=12, DTOutput("TESTSlin")), h5("The estimate is the p-value of the test"), h5("F-tests on regression, significance of slopes, and preparation need to have a p-value <0.05 to pass"), h5("All other tests pass if p-value > 0.05"), "SST CI for difference of slopes:", tableOutput("SlopeDiffCI"), h3("ANOVA for parallel line assay"), DTOutput("ANOVAlin")) ), # tabPanel("Report", # h4("Settings for report"), # # ) ) ) ) ), tabPanel("parameter estimates", htmlOutput("PureErrWParEst"), tags$head(tags$style("#PureErrWParEst{color: red; font-size: 16px; font_style: italic;}")), column(3,style = "background: #4FCBD922", br(), h4("Regression results restricted"), tableOutput("coeffs_r"), "Bend points restricted", tableOutput("bends_r2")), column(3,style = "background: #B5C74022", br(), h4("Regression results unrestricted"), tableOutput("coeffs_unr")), column(3,style = "background: #F9545422", h4("Regression results (ln-transformed)"), tableOutput("logcoeffs_r"), tableOutput("bends_unr2"), tableOutput("logcoeffs_unr")) ), tabPanel("Report", h4("Settings for report"), downloadButton("downloadXLReport", label="Download 4PL PDF report", class="butt"), tags$style(type="text/css","#downloadXLReport {background-color: orange; color: black;font-family: Courier New}"), downloadButton("downloadXLReportLin", label="Download linear PLA PDF report", class="butt"), tags$style(type="text/css","#downloadXLReportLin {background-color: #4FCBD9; color: black;font-family: Courier New}"), textInput("Author", "Author", value=""), textInput("RepIdentifier", "Report name", value=""), textInput("NoP","Product name", value=""), textInput("Assay", "Assay name",value="") ) ) ) ) }) ##### UI Meta ---- output$fourPL <- renderUI({ navbarPage(title="4PL+linear reg", tabPanel("Analysis and Plots", #sidebarLayout( # sidebarPanel( # width=4, # fluidRow( # ) # ), mainPanel(width=12, tabsetPanel(id="tabs", tabPanel("Settings", h4("Settings of 4PL regression"), div(checkboxInput("PureErrMeta", "Should pure error be used for calculation of CIs?", FALSE), style = "font-size: 24px !important;color: #C2173F"), h4("User help"), h5("If new dilutions are entered, please adjust EC50 to avoid calculation errors"), # numericInput("Limits",p("limit to be >", bsButton("q4",label="", icon=icon("info"), style="primary", size="extra-small")), # bsPopover(id="q4", title="", content="The calculated limits ...")), #h5("Diagnostics only shown if EXCEL is uploaded"), column(2, h3("Settings"), helpText("Vary the slider to see the effect of special cause"), sliderInput("sdfac","Variability as % of lower to upper asymptote", max=10, value=3, min=0.1, step=0.1), checkboxInput("heterosked","heteroskedastic noise", FALSE), sliderInput("potencydiff","potency of test (%)", max=200, min=50, value=100, step=1), # sliderInput("outlL","outlier in lower asymptote", min=0, max=1.5, value=0, step=0.1), # sliderInput("outlM","outlier in mid part", min=0, max=1.5,value=0, step=0.1), # sliderInput("outlU","outlier in upper asymptote", min=0, max=1.5,value=0, step=0.1) ), column(2,style = "background: #7FAEFF", #actionButton("StartCalc", "Click, when calculations to be started"), h4("curve settings"), numericInput("lowAsymptREF", "lower asymptote REF",10,step=1,min=0), numericInput("lowAsymptTEST", "lower asymptote TEST",10,step=1,min=0), numericInput("uppAsymptREF", "upper asymptote REF",110,step=1,min=0), numericInput("uppAsymptTEST", "upper asymptote TEST",110,step=1,min=0) ), column(2,style = "background: #7FAEFF", numericInput("slopeREF", "slope REF",1,step=0.1,min=-10), numericInput("slopeTEST", "slope TEST",1,step=0.1,min=-10), numericInput("EC50", "EC50 REF",-3.5), numericInput("potDiff", "potency difference",0) ), column(2,style = "background: #627ADD", h4("dilutions"), numericInput("CONC1", "highest concentration",0.3, min=-3.5), numericInput("CONC2", "2nd concentration",0.15), numericInput("CONC3", "3rd concentration",0.075), numericInput("CONC4", "4th concentration",0.0375), numericInput("CONC5", "5th concentration",0.01875), numericInput("CONC6", "6th concentration",0.00938) ), column(2,style = "background: #627ADD", numericInput("CONC7", "7th concentration",0.00469), numericInput("CONC8", "8thd concentration",0.00235), numericInput("CONC9", "9thd concentration",value=NA), numericInput("CONC10", "10th concentration",value=NA), numericInput("CONC11", "11th concentration",value=NA), numericInput("CONC12", "lowest concentration",NA) ), column(2,style = "background: #4FCBD9", h4("geometric dilution scheme"), numericInput("ConcStart", "starting concentration",value=NA, min=0), numericInput("dilutionFac", "dilution factor",value=NA, min=0, max=10), numericInput("NoDil", "no. of dilutions",value=NA, min=8), numericInput("NoDilSer", "no. of dil. series",value=NA), verbatimTextOutput("dilutions") ), h4("log-dilutions from settings above"), column(8, box(title = "Simulated data per log-concentration", status="warning",solidHeader = T, width=12, "incl. mean, sd and CV%", DT::dataTableOutput("ConctabMeta")), verbatimTextOutput("logdil")) #) ), #### 4pl fits ---- tabPanel("4pl-fit", wellPanel( fluidRow( column(10, htmlOutput("PureErrW4plMeta"), tags$head(tags$style("#PureErrW4plMeta{color: red; font-size: 16px; font_style: italic;}")), plotOutput("plot4plMeta", width = "80%"), DTOutput("pottab4pl"), "Footnote: test performed on relative CIs.", DTOutput("EQtests4pl"), # SSTs h5("*...The estimate for F-test on regression and on non-linearity is the p-value"), h5("F-test on regression passes if F-value > F-crit and thus p < 0.05"), h5("F-test on non-linearity passes if F-value < F-crit and thus p > 0.05"), h5("Test results outcome: 0 ... test passed (for EQ tests: CI within limits); 1 ... test failed (for EQ tests CI not within limits); -1 ... calculations unbound/denominator too close to 0"), #plotOutput("CIplot, height=50%") ), column(8, "4 PL ANOVA unrestricted", box(title = "ANOVA unrestricted", status="warning",solidHeader = T, width=12, "", DT::dataTableOutput("ANOVA")), h5("Please note that for the CIs of rel. potency the RSS of the restricted model is used"), h5("RSS ... 'Residual error' in the SumSquares column"), h5("MSE ... 'Residual error' in the MeanSquaress column"), h5("SSE ... 'Pure error' in the SumSquares column"), h5("RMSE ... Square root of the 'Residual Error' in the MeanSquares column"), verbatimTextOutput("RMSE") ) )) ), tabPanel("ln-transformed y", htmlOutput("PureErrWLogMeta"), tags$head(tags$style("#PureErrWLogMeta{color: red; font-size: 16px; font_style: italic;}")), h4("ln-transformed y-axis plots"), plotOutput("plot4plTransMeta", width = "80%"), DT::dataTableOutput("pottab4plTrans"), ), tabPanel("linear regression", htmlOutput("PureErrW3"), h4("Evaluations from meta-data"), tags$head(tags$style("#PureErrW3{color: red; font-size: 16px; font_style: italic;}")), column(12, plotOutput("plotLinMeta"), "Delta method is used for potency CIs", DTOutput("pottabMeta") ), column(5, h3("Tests for linear PLA:"), box(title="Suitability tests", status="primary",solidHeader = T,collapsible=T, width=12, DTOutput("TESTSlinMeta")), h5("The estimate is the p-value of the test"), h5("F-tests on regression, significance of slopes, and preparation need to have a p-value <0.05 to pass"), h5("All other tests pass if p-value > 0.05"), box(title="Unrestricted linear model (SSSI):", status="primary",solidHeader = T,collapsible=T, width=12, tableOutput("SummaryModABuMeta")), h4("Restricted linear model (CSSI):"), box(title="Restricted linear model (CSSI):", status="primary",solidHeader = T,collapsible=T, width=12, tableOutput("SummaryModABMeta")) ), column(6, h3("ANOVA for parallel line assay"), box(title="ANOVA for simultated data", status="primary",solidHeader = T, collapsible=T, width=12, DTOutput("ANOVAlinMeta")), " CI for difference of slopes:", tableOutput("SlopeDiffCIMeta"), ) ), tabPanel("Report", h4("Settings for report"), downloadButton("downloadXLReport", label="Download PDF report", class="butt"), tags$style(type="text/css","#downloadXLReport {background-color: orange; color: black;font-family: COurier New}"), ) ) ) #) ) ) }) output$pla <- renderUI({ navbarPage(title="pla", tabPanel("Analysis and Plots", ) ) }) output$wizard <- renderUI({ navbarPage(title="Dilution setting", tabPanel("Plots", sidebarLayout( sidebarPanel( width=3, fluidRow( column(6, numericInput("Limits",p("limit to be >", bsButton("q4",label="", icon=icon("info"), style="primary", size="extra-small")), bsPopover(id="q4", title="", content="The calculated limits ..."))) )), mainPanel( tabsetPanel(id="tabs", tabPanel("4pl", box(title="ANOVA table", status="primary",solidHeader = T, width=12, tableOutput("Anovatab")), column(4, h3("Confidence intervals"), tableOutput("CIs"), "The confidence interval table is interaactive for changes in: variability slider (%SD), potency of test-slider, and 'Adjust the dilutions'-slider", tableOutput("optimalDils"), selectInput(inputId="scenario", label= "Select an 'optimal' scenario:", choices = c("scenario 2","scenario 3","scenario 6","steep slope"))), column(5, plotOutput("plotfordilutions"), h4("in grey: most extreme bend point lines of theoretical samples with 50% and 200% potency"), sliderInput("dilslider", "Adjust the dilutions(+-change in %)", min = -100,max=100, value=0, step=1, round=0), checkboxInput("fixupper","Fix highest concentration (if unticked, the center is fixed)",FALSE), h5("Dilution factors"), tableOutput("adjlogdil"), "Short guidance: wider dilution ranges increase the CIs of rel. potency, and decrease the CIs of upper and lower asymptote ratios, as well as Hill's slope ratios", br(), "Narrower dilution ranges decrease the CIs of rel. potency, and increase the CIs of upper and lower asymptote ratios, ands Hill's slope ratios",), column(3, h3("Bend points"), tableOutput("bps"), tableOutput("extremebps"), h4("Explanation of the plots") )), tabPanel("Report", h4("Settings for report") )) ) ))) }) #output$sessioninfo <- renderPrint(sessioninfo()) v <- reactiveValues(num_dose=0, next.dose.t=0) sigmoid <- reactive({ sig <- c(input$lowAsymptREF, input$lowAsymptTEST,input$uppAsymptREF,input$uppAsymptTEST, input$slopeREF,input$slopeTEST,input$EC50,input$potDiff) sig }) CONC <- reactive({ Konz_ <- c(input$CONC1,input$CONC2,input$CONC3,input$CONC4, input$CONC5,input$CONC6,input$CONC7,input$CONC8, input$CONC9,input$CONC10,input$CONC11,input$CONC12) if (any(na.omit(Konz_)==0)) Konz_[Konz_ ==0] <- 0.0000001 Konz <- na.omit(Konz_) }) Dils <- reactive({ Dilutions <- c(input$ConcStart,input$dilutionFac,input$NoDil,input$NoDilSer) }) #### input EXCEL file ---- observe({ if (!is.null(input$iFile)) { inFile <- input$iFile ext <- tools::file_ext(inFile$name) file.rename(inFile$datapath, paste(inFile$datapath, ".xlsx",sep="")) t.filelocation <- gsub('\\\\','/',paste(inFile$datapath, ext,sep=".")) sheets <- openxlsx::getSheetNames(t.filelocation) dat <- lapply(sheets, openxlsx::read.xlsx, xlsxFile = t.filelocation) names(dat) <- sheets Dat$wb <- dat names(Dat$wb) <- sheets Dat$sheets <- sheets Dat$FileName <- input$iFile[["name"]] } }) output$sheetName <- renderUI({ if (!is.null(Dat$wb)) { #browser() cnSheets <- Dat$sheets cnSheets2 <- c("please choose", cnSheets) selectInput(inputId = "sheet", label="Select a sheet:",choices = cnSheets) } }) observeEvent(input$sign_out, { unlink(input$iFile$datapath) reset(id = "") # from shinyjs package }) #### process XLSX file ---- observe({ if (!is.null(input$iFile)) { if (!is.null(input$sheet)) { if (input$sheet != "please choose") { #browser() Dat$RepIdentifier <- input$RepIdentifier Dat$Author <- input$Author Dat$NoP <- input$NoP Dat$Assay <- input$Assay XLdat <- Dat$wb[input$sheet][[1]] if (is.null(XLdat)) XLdat <- Dat$wb[Dat$sheets[1]][[1]] cn <- colnames(XLdat) logI <- grep("log", cn) logDoseI <- grep("log_dose", cn) if (length(logI)>0 & length(logDoseI)==0) { XLdat$log_dose <- XLdat[,logI] XLdat2 <- XLdat[,-logI] CORro <- cor(XLdat$log_dose, XLdat[,3]) } else if (length(logI)==0 & length(logDoseI)==0) { Ind <- grep("dilu|dose|Dose|Conc|conc",cn) XLdat$log_dose <- log(XLdat[,Ind]) CORro <- cor(XLdat[,Ind], XLdat[,3]) XLdat2 <- XLdat[,-Ind] } else if (length(logI)>0 & length(logDoseI)>0) { XLdat2 <- XLdat CORro <- cor(XLdat[,logI], XLdat[,3]) } Dat$EXCEL <- XLdat2 PureErrFlag <- input$PureErr warning_text2 <- reactive({ ifelse(PureErrFlag, 'Pure Error is selected', '') }) output$PureErrW2 <- renderText(warning_text2()) warning_textParEst <- reactive({ ifelse(PureErrFlag, 'Pure Error is selected', '') }) output$PureErrWParEst <- renderText(warning_textParEst()) REP$PureErr <- PureErrFlag noDilSeries <-(ncol(XLdat2)-1)/2 noDils <- nrow(XLdat2) Dat$noDilSeriesXL <- noDilSeries all_l <- melt(data.frame(XLdat2), id.vars="log_dose",variable.name = "replname", value.name = "readout") isRef <- rep(c(1,0),1,each=nrow(XLdat2)*noDilSeries) isSample <- rep(c(0,1),1,each=nrow(XLdat2)*noDilSeries) all_l$isRef <- isRef all_l$isSample <- isSample all_l$Conc <- exp(all_l$log_dose) # all_l$readout[all_l$readout < 0] <- 0.01 REP$all_l <- all_l #### XLSX eval ---- if (CORro<0) SLOPE <- -1 else SLOPE <- 1 FITs <- Fitting_FUNC(XLdat2, TransFlag = FALSE) #### if no 4pl fit is possible ---- if (!is.null(FITs)) { if (is.character(FITs)) { Dat$FITsFlag <- TRUE pSing <- plotSingularity(XLdat2) output$plotSing <- renderPlot({ pSing }) output$XLplot <- renderPlot({ REP$XLplotSing <- pSing pSing }) output$relpotTestTab <- renderTable({ NULL }) output$relpotTestPlot<- renderPlot({ NULL }) output$AIC <- renderTable({ NULL }) output$VarDiagn <- renderTable({ NULL }) output$pottab4plXL <- renderDT({ NULL }) output$diagnplot <- renderPlot({ NULL }) output$EQtests <- renderDT({ NULL }) # output$pottab4plTransXL <- renderDT({ NULL }) output$ANOVAXLS <- renderTable({ NULL }) #) return(NULL) } } Smr <- FITs[[1]] # summary(mr) Smu <- FITs[[2]] # summary(mu) coeffsMR <- Smr$coefficients[,1] coeffsMU <- Smu$coefficients[,1] Dat$coeffsMRes <- coeffsMR Dat$coeffsMUnr <- coeffsMU Dat$coeffs_UN <- coeffsMU names(coeffsMU) <- c("lowAsym REF", "slope REF","upperAsym REF","EC50 REF","lowAsym TEST","slope TEST","upperAsym TEST","r") # browser() XbendMUlREF <- coeffsMU[4] - 1.5434/abs(coeffsMU[2]) XbendMUuREF <- coeffsMU[4] + 1.5434/abs(coeffsMU[2]) XbendMUlTEST <- coeffsMU[4]-coeffsMU[8] - 1.5434/abs(coeffsMU[6]) XbendMUuTEST <- coeffsMU[4]+coeffsMU[8] + 1.5434/abs(coeffsMU[6]) XbendMRlREF <- coeffsMR[4] - 1.5434/abs(coeffsMR[2]) XbendMRuREF <- coeffsMR[4] + 1.5434/abs(coeffsMR[2]) XbendMRlTEST <- coeffsMR[4]-coeffsMR[5] - 1.5434/abs(coeffsMR[2]) XbendMRuTEST <- coeffsMR[4]-coeffsMR[5] + 1.5434/abs(coeffsMR[2]) XasymlREF <- coeffsMR[4] - 3/abs(coeffsMR[2]) XasymuREF <- coeffsMR[4] + 3/abs(coeffsMR[2]) XasymlTEST <- coeffsMR[4]-coeffsMR[5] - 3/abs(coeffsMR[2]) XasymuTEST <- coeffsMR[4]-coeffsMR[5] + 3/abs(coeffsMR[2]) #browser() BPsMR_MU <- data.frame(points = c("lower bendpoint REF", "upper bendpoint REF","lower bendpoint TEST" ,"upper bendpoint TEST", "lower asymp. point REF", "upper asymp. point REFr", "lower asymp. point TEST", "upper asymp. point TEST", "bendREF_lower_unrestr", "bendREF_upper_unrestr", "bendTESTE_lower_unrestr", "bendTEST_upper_unrestr"), estimates = c(round(XbendMRlREF,3), round(XbendMRuREF,3),round(XbendMRlTEST,3),round(XbendMRuTEST,3), round(XasymlREF,3),round(XasymuREF,3),round(XasymlTEST,3),round(XasymuTEST,3), round(XbendMRlREF,3),round(XbendMRuREF,3),round(XbendMRlTEST,3),round(XbendMRuTEST,3))) Dat$bendsAll <- BPsMR_MU REP$bendsAll <- BPsMR_MU if (!PureErrFlag) { pot_est <- FITs[[3]] potU_est <- FITs[[4]] colnames(pot_est) <- c("estimate","lowerCI","upperCI") colnames(potU_est) <- c("estimate","lowerCI","upperCI") } else { FitAnova <- anova(lm(readout ~ factor(log_dose)*isSample, all_l)) meanPureErr <- FitAnova[4,3] DFsPure <- FitAnova[4,1] #VCOV <- vcov(mr) V_V <- Smr$cov.unscaled #VCOV/Smr$sigma^2 #VCOVpure <- V_V*meanPureErr SEsPure <- sqrt(diag(V_V)*meanPureErr) pot_est <- data.frame(estimate=exp(coeffsMR[5]), lowerCI = exp(coeffsMR[5]-qt(0.975,DFsPure)*SEsPure[5]), upperCI = exp(coeffsMR[5]+qt(0.975,DFsPure)*SEsPure[5])) #browser() #VCOVu <- vcov(mu) V_Vu <- Smu$cov.unscaled #VCOVpure <- V_Vu*meanPureErr SEsPureU <- sqrt(diag(V_Vu)*meanPureErr) potU_est <- data.frame(estimate=exp(coeffsMU[8]), lowerCI = exp(coeffsMU[8]-qt(0.975,DFsPure)*SEsPureU[8]), upperCI = exp(coeffsMU[8]+qt(0.975,DFsPure)*SEsPureU[8])) } #browser() Dat$potDiffXL <- potU_est[1]*100 RMSE_unr_diagn <- Smu$sigma # sqrt(SU$resVar) RMSE_res_diagn <- Smr$sigma #sqrt(SR$resVar) up_lowDiffDiagn <- Smu$coefficients["ds",1]-Smu$coefficients["as",1] ProzSD_diagn <- RMSE_unr_diagn*100/up_lowDiffDiagn Dat$ProzSD_XL <- ProzSD_diagn observe({ pot_est3 <- data.frame(pot_est*100) MaxPl <- max(input$upperPot, pot_est3$upperCI) MinPl <- min(input$lowerPot, pot_est3$lowerCI) MaxPl_ <- MaxPl*1.2 MinPl_ <- MinPl*0.8 #browser() p_relCI <- ggplot(data=pot_est3, aes(xmin=lowerCI, xmax=upperCI, y=1)) + geom_linerange(size=4, col="darkseagreen",alpha=0.5) + geom_point(aes(x=estimate, y=1), col="grey15", shape=13, size=10) + geom_vline(xintercept = c(input$lowerPot, input$upperPot), col="indianred") + annotate("text", x=input$lowerPot-13, y=1.040, label=paste("lower EAC:", input$lowerPot), col="indianred") + annotate("text", x=input$upperPot+13, y=1.040, label=paste("upper EAC:", input$upperPot), col="indianred") + annotate("text", x=pot_est3$lowerCI-10, y=1.020, label=paste("lower CL:", round(pot_est3$lowerCI,1)), col="darkgreen") + annotate("text", x=pot_est3$upperCI+10, y=1.020, label=paste("upper CL:", round(pot_est3$upperCI,1)), col="darkgreen") + annotate("text", x=pot_est3$estimate, y=0.98, label=paste("rel. potency:", round(pot_est3$estimate,1)), col="black") + ylim(c(0.95, 1.05)) + xlim(c(MinPl_,MaxPl_)) + xlab("relative potency + confidence interval") + theme_bw() + theme(axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) output$relpotTestPlot <- renderPlot({ p_relCI }) REP$relpotTestPlot <- p_relCI output$relpotTestTab <- renderTable({ pot_est3 }) }) ANOVAtab2 <- ANOVA4plUnresfunc(ro_new = XLdat2) output$ANOVAXLS <- renderTable({ ANOVAtab2 }) REP$ANOVAXLS <- ANOVAtab2 #browser() FITsTrans <- Fitting_FUNC(XLdat2, TransFlag = TRUE) # SUlog <- FITsTrans[[2]] SRlog <- FITsTrans[[1]] RMSE_unrlog_diagn <- SUlog$sigma RMSE_reslog_diagn <- SRlog$sigma up_lowDifflogDiagn <- SUlog$coefficients["ds",1]-SUlog$coefficients["as",1] ProzSDlog_diagn <- RMSE_unrlog_diagn * 100 / up_lowDifflogDiagn #### Diagnostic RMSE table #### DiagnTable <- data.frame(parameter = c("RMSE unrestricted", "RMSE_restr.", "Diff_upper-lowerAsymp", "%SD (unrestricted)", "RMSE log_unrestricted", "RMSE log_restr", "diff_up-lowAsymp_log", "%SD (log unrestricted)"), result = c(round(RMSE_unr_diagn, 4), round(RMSE_res_diagn, 4), round(up_lowDiffDiagn, 4), round(ProzSD_diagn, 4), round(RMSE_unrlog_diagn, 4), round(RMSE_reslog_diagn, 4), round(up_lowDifflogDiagn, 4), round(ProzSDlog_diagn, 4))) Dat$DiagnTable <- DiagnTable REP$DiagnTable <- DiagnTable logpotest <- FITsTrans[[3]] #exp(confintd(mrlog, "r", method = "asymptotic")) # compParm(logpot, "c") logpotUest <- FITsTrans[[4]] # exp(confintd(mulog, "r", method = "asymptotic")) # compParm(logpotu, "c") # Berechnung der Konfidenzintervalle (CI) # logpotCI <- c(exp(Smrlog[5,1] - qt(0.975, nrow(all_1)-5) * Smrlog[5,2]), exp(Smrlog[5,1]), exp(Smrlog[5,1] + qt(0.975, nrow(all_1)-5) * Smrlog[5,2])) colnames(logpotest) <- c("estimate", "lowerCI", "upperCI") colnames(logpotUest) <- c("estimate", "lowerCI", "upperCI") #browser() cnXL <- colnames(XLdat2) Filesample <- data.frame(Test = c("FILE NAME:", "SAMPLES"), Test2=c(Dat$FileName, paste(cnXL[1], " vs ", cnXL[4]))) colnames(Filesample) <- c("", "") output$Filesampl <- renderTable({ Filesample }, rownames = F) UnRPLAausw <- data.frame(Information = c("model", "lower asymptote Ref", "Hill's slope Ref", "upper asymptote Ref","EC50 Ref", "lower asymptote Test", "Hill's slope Test", "upper asymptote Test","EC50 Difference", "relative potency", "lower CI", "upper CI"), Results = unlist(c("UNRESTRICTED", round(coeffsMU, 3), round(potU_est*100, 3)))) # von psl_nls # "log relative potency", "log lower CI", "log upper CI", round(logpotest, 3), round(compParm(potu, "c", display = F), 3) output$coeffs_unr <- renderTable({ UnRPLAausw }) #browser() UnRPLAausw2 <- data.frame(Dat$bendpointsTRANS) if (length(UnRPLAausw2) > 0) { colnames(UnRPLAausw2) <- c("bendpoints log") UnrBendLog <- data.frame(Bendpoint = c("REF_lower","REF_upper", "TEST_lower","REF_lower"), bendpoints_logscale = UnRPLAausw2) output$bends_unr2 <- renderTable({ UnrBendLog }) } REP$UnRPLAausw <- UnRPLAausw REP$UnRPLAausw2 <- UnRPLAausw2 # browser() coeffs_R <- coeffsMR # pot$coefficients coeffs_R[5] <- coeffs_R[4] - coeffs_R[5] names(coeffs_R) <- c("lower A", "slope", "upper A", "EC50 REF", "EC50 TEST") # --- Ergebnistabelle: RESTRICTED --- PLAAusw <- data.frame( Information = c("model", "lower asymptote", "Hill's slope", "upper asymptote","EC50 Ref", "EC50 Test", "relative potency", "lower CI", "upper CI"), Results = unlist(c("RESTRICTED", round(coeffs_R, 3), round(pot_est[1, ] * 100, 3)))) # von gs1_nls output$coeffs_r <- renderTable({ PLAAusw }) bendsAll <- data.frame(Dat$bendsAll[1:8,]) output$bends_r2 <- renderTable({ bendsAll }, digits = 3, rownames = T) REP$PLAausw <- PLAAusw REP$PLBend <- bendsAll #### Parameter extraktion ---- logcoeffs_R <- SRlog$coefficients[, 1] # logpot$coefficients names(logcoeffs_R) <- c("lower A", "Hill's slope", "upper A", "EC50 REF","EC50 DIFF") # --- Ergebnistabelle: LOG RESTRICTED --- LogPLAAusw <- data.frame( Information = c("model", "lower asymptote", "Hill's slope", "upper asymptote","EC50 Ref", "EC50 difference", "log relative potency", "log lower CI", "log upper CI"), Results = unlist(c("LOG RESTRICTED", round(logcoeffs_R, 3), round(logpotest * 100, 3)))) # von gsl_nls output$logcoeffs_r <- renderTable({ LogPLAAusw }) REP$LogPLAausw <- LogPLAAusw logcoeffs_UNR <- SUlog$coefficients[,1] names(logcoeffs_UNR) <- c("lower asymptote Ref", "Hill's slope Ref", "upper asymptote Ref","EC50 Ref", "lower asymptote Test", "Hill's slope Test", "upper asymptote Test","EC50 Diff" ) # --- Ergebnistabelle: LOG UNRESTRICTED --- LogUnrPLAAusw <- data.frame( Information = c("model", "lower asymptote Ref", "Hill's slope Ref", "upper asymptote Ref","EC50 Ref", "lower asymptote Test", "Hill's slope Test", "upper asymptote Test","EC50 Diff" , "relative potency", "lower CI", "upper CI"), Results = unlist(c("LOG UNRESTRICTED", round(logcoeffs_UNR, 3), round(logpotUest * 100, 3)))) # von gsl_nls output$logcoeffs_unr <- renderTable({ LogUnrPLAAusw }) REP$LogUnrPLAausw <- LogUnrPLAAusw #browser() if (exists("Ind")) { Dat$dilution <- XLdat[,Ind] } else Dat$dilution <- exp(XLdat[,logI]) ##### Plot XL 4PL ---- output$XLplot <- renderPlot({ XLplot4pl <- plot_f(XLdat2, TransFlag=F) REP$XLplot4pl <- XLplot4pl XLplot4pl }) REP$XLdat2 <- XLdat2 # --- Diagnose-Plots (Residualanalyse) --- output$diagnplot <- renderPlot({ op <- par(mfrow = c(2, 2), mar = c(3.2, 3.2, 2, .5), mgp = c(2, .7, 0)) PREDs <- FITs[[5]] PREDsU <- FITs[[6]] # 1. Residuals vs Fitted plot(Smr$residuals ~ PREDs, main = "Residuals restricted") abline(h = 0) qqnorm(Smr$residuals) qqline(Smr$residuals) plot(Smu$residuals ~ PREDsU, main = "Residuals unrestricted") abline(h = 0) qqnorm(Smu$residuals) qqline(Smu$residuals) par(op) # Parameter zurücksetzen }) pot <- drm(readout ~ Conc, isSample, data=all_l, fct=LL.4(names=c("b","d","a","c")), pmodels=data.frame(1,1,1,isSample)) potU <- drm(readout ~ Conc, isSample, data=all_l, fct=LL.4(names=c("b","d","a","c")), pmodels=data.frame(isSample, isSample,isSample,isSample)) output$AIC <- renderTable({ AIC <- AIC(pot, potU) }) output$VarDiagn <- renderTable({ DiagnTable }, digits=4) output$relpotplot <- renderPlot({ relpot(potU, intervall="fieller", bty="l", main="Quality of rel. potency over response") }) } # !please choose } # input$sheet } # input$iFile }) #### make geomDils reactive ---- observe({ #browser() if (is.null(input$ConcStart)) return(NULL) if (!is.na(input$ConcStart) & !is.na(input$dilutionFac) &!is.na(input$NoDil) &!is.na(input$NoDilSer)) { upR <- input$ConcStart noDil <- input$NoDil noDilSer <- input$NoDilSer Div <- input$dilutionFac res <- c() N_ <- 1 Conc <- c(upR, divFUN(upR,Div,N=N_,res,noDil)) Dat$MetaConc <- Conc } }) observe({ if(!is.null(Dat$MetaConc)) { updateNumericInput(session, "CONC1", value=as.numeric(Dat$MetaConc[1])) updateNumericInput(session, "CONC2", value=as.numeric(Dat$MetaConc[2])) updateNumericInput(session, "CONC3", value=as.numeric(Dat$MetaConc[3])) updateNumericInput(session, "CONC4", value=as.numeric(Dat$MetaConc[4])) updateNumericInput(session, "CONC5", value=as.numeric(Dat$MetaConc[5])) updateNumericInput(session, "CONC6", value=as.numeric(Dat$MetaConc[6])) updateNumericInput(session, "CONC7", value=as.numeric(Dat$MetaConc[7])) updateNumericInput(session, "CONC8", value=as.numeric(Dat$MetaConc[8])) updateNumericInput(session, "CONC9", value=as.numeric(Dat$MetaConc[9])) updateNumericInput(session, "CONC10", value=as.numeric(Dat$MetaConc[10])) updateNumericInput(session, "CONC11", value=as.numeric(Dat$MetaConc[11])) updateNumericInput(session, "CONC12", value=as.numeric(Dat$MetaConc[12])) } }) #### render logDilsText ---- output$logdil <- renderText({ if (!is.null(Dat$MetaConc)) { Conc <- Dat$MetaConc } else Conc <- CONC() logdilu <-log(Conc) logdilu }) #### reactive dataset sim ---- sim <- reactive({ #browser() if(is.null(sigmoid())) return(NULL) sd_fac_ <- as.numeric(input$sdfac) r_ <- log(as.numeric(input$potencydiff)/100) as = sigmoid()[1]; bs = sigmoid()[5];cs = sigmoid()[7];ds = sigmoid()[3];at = sigmoid()[2]; bt = sigmoid()[6];r = sigmoid()[8]; ct = cs-r_; dt = sigmoid()[4]; if (!is.null(Dat$MetaConc)) Conc <- Dat$MetaConc else Conc <- CONC() log_conc <- log(Conc) av_test <- as + (ds-as)/(1+exp(bs*(cs - log_conc))) av_ref <- at + (dt-at)/(1+exp(bt*(ct - log_conc))) #browser() if (!is.na(input$NoDilSer)) { noDilSer <- input$NoDilSer } else if (!is.null(Dat$noDilSeriesXL)) noDilSer <- Dat$noDilSeriesXL else noDilSer <- 3 if (!is.na(input$NoDil)) noDil <- input$NoDil else noDil <- length(log_conc) isRef <- rep(c(1,0), 1,each=noDilSer*noDil) isSample <- rep(c(0,1), 1,each=noDilSer*noDil) #if (is.null(Dat$EXCEL)) { ro_new <- Calc_DilRes(as=as,at=at,ds=ds,dt=dt,cs=cs,ct=ct,r=r_,bt=bt,bs=bs, log_conc = log_conc, sd_fac=sd_fac_, # auslenkU=outlierU, # auslenkM=outlierM, # auslenkL=outlierL, heteroNoise = input$heterosked, noDilSeries = noDilSer, noDils = noDil) #} else ro_new <- Dat$EXCEL }) # }) ####sim2 ---- sim2 <- reactive({ tab <- sim() #if (is.null(Dat$EXCEL)) return(tab) else return(Dat$EXCEL) }) #### Plot 4pl Meta ---- output$plot4plMeta <- renderPlot({ PureErrFlag <- input$PureErrMeta warning_text3 <- reactive({ ifelse(PureErrFlag, 'Pure error selected','') }) output$PureErrW4plMeta <- renderText(warning_text3()) sigmoid <- sigmoid() det_sig=NULL plot_f(sim2(), TransFlag = F) }) #### Plot 4pl Meta Transformed ---- output$plot4plTransMeta <- renderPlot({ PureErrFlag <- input$PureErrMeta warning_text3 <- reactive({ ifelse(PureErrFlag, 'Pure error selected','') }) output$PureErrWLogMeta <- renderText(warning_text3()) #browser() sigmoid <- sigmoid() det_sig=NULL plot_f(sim2(), TransFlag = T) }) #### Testergebnisse für 4PL Meta ---- observe({ if (is.null(sim2())) return(NULL) if (is.null(input$PureErrMeta)) return(NULL) #observeEvent(input$StartCalc,{ PureErrFlag <- input$PureErrMeta warning_text3 <- reactive({ ifelse(PureErrFlag, 'Pure error selected','') }) #browser() output$PureErrW3 <- renderText(warning_text3()) Limite <- list(as.numeric(input$lEACdiffla), as.numeric(input$uEACdiffla), as.numeric(input$lEACratiola), as.numeric(input$uEACratiola), as.numeric(input$lEACratioSlope), as.numeric(input$uEACratioSlope), as.numeric(input$lEACratioua), as.numeric(input$uEACratioua), as.numeric(input$lowerPot), as.numeric(input$upperPot), as.numeric(input$lEACratioAdiff), as.numeric(input$uEACratioAdiff)) Dat$limite <- Limite #browser() tab <- tests_FUNC(sim2(), Limite, PureErrFlag = PureErrFlag) if (length(tab)>1) { tab[1,6:7] <- c("-","-") Dat$tests_FUNC <- tab REP$testsTab <- tab tab2 <- tab[1:7,] dat <- datatable(tab2, rownames=F, options = list( paging=TRUE, dom="t", rownames=FALSE )) %>% formatStyle("test_results", target='row', backgroundColor = styleEqual(c(-1,0,1), c("pink",'lightgreen','lightgrey'))) } else { dat <- datatable(data.frame(test_results = "Convergeance failed for the uploaded dataset")) } #browser() output$EQtests4pl <- renderDT({ dat}) }) # observe #### Testergebnisse 4PL für XLSX ---- observe({ if (is.null(Dat$EXCEL)) return(NULL) if (is.null(input$PureErr)) return(NULL) if (!is.null(Dat$FITsFlag)) return(NULL) #observeEvent(input$StartCalc,{ PureErrFlag <- input$PureErr warning_text3 <- reactive({ ifelse(PureErrFlag, 'Pure error selected','') }) output$PureErrW3 <- renderText(warning_text3()) Limite <- list(as.numeric(input$lEACdiffla), as.numeric(input$uEACdiffla), as.numeric(input$lEACratiola), as.numeric(input$uEACratiola), as.numeric(input$lEACratioSlope), as.numeric(input$uEACratioSlope), as.numeric(input$lEACratioua), as.numeric(input$uEACratioua), as.numeric(input$lowerPot), as.numeric(input$upperPot), as.numeric(input$lEACratioAdiff), as.numeric(input$uEACratioAdiff)) Dat$limite <- Limite #browser() SelTests <- as.numeric(input$selectedSSTs) tab <- tests_FUNC(Dat$EXCEL, Limite, PureErrFlag = PureErrFlag) tab[1,6:7] <- c("-","-") tab2 <- tab[SelTests,] Dat$tests_FUNC <- tab2 REP$testsTab <- tab2 dat <- datatable(tab2, rownames=FALSE, options = list( paging=TRUE, dom="t" )) %>% formatStyle("test_results", target='row', backgroundColor = styleEqual(c(-1,0,1), c("pink",'lightgreen','lightgrey'))) output$EQtests <- renderDT({ dat }) }) # observe #### plot CIs XL---- # observe({ # tab <- Dat$tests_FUNC # if (is.null(tab)) return(NULL) # # tab2 <- tab[-c(1,2,3,6),] # tab2[,3:ncol(tab2)] <- apply(tab2[,3:ncol(tab2)],2,as.numeric) # tab2[4:5,3:7] <- tab2[4:5,3:7]/100 # # p_CIs <- ggplot(tab2,aes(x=test,y=estimate, color=test,group=test)) + # geom_point() + # geom_errorbar(aes(ymin=lower_CI, ymax=upper_CI), width=0.4) + # geom_crossbar(aes(ymin=lower_limit, ymax=upper_limit), size=0.8) + # coord_flip() + # theme_bw() + # theme(legend.position = "none",text = element_text(size=20)) # # output$CIplot <- renderPlot({ p_CIs}, height=200) # # REP$CIplot <- p_CIs # }) #### simulated data tab Meta ---- output$simdat <- DT::renderDataTable({ tab <- sim2() if (is.character(tab)) stop(tab) tab2 <- round(tab, 5) colnames(tab2) <- c(paste("T", seq(1,(ncol(tab2)-1)/2)), paste("R", seq(1,(ncol(tab2)-1)/2)), "log_conc" ) dat <- datatable(tab2, options=list( paging=T, pageLength=20, dom="t" )) }) ##### Concentrationtab Meta ---- output$ConctabMeta <- DT::renderDataTable({ if (!is.na(Dils()[1]) & is.na(Dils()[4])) return(NULL) tab <- sim2() if (is.character(tab)) stop(tab) if (!is.na(Dils()[4])) { noDilSer <- Dils()[4] } else if (!is.null(Dat$noDilSeriesXL)) { noDilSer <- Dat$noDilSeriesXL } else { noDilSer <- 3 } Conc <- CONC() Conctab <- perConcTab(tab, noDilSeries = noDilSer) Dat$Conctab <- Conctab dat <- datatable(Conctab, options=list( paging=T, pageLength=12, dom="t" )) %>% formatStyle(0, target='row', backgroundColor = styleEqual(c("avs","sds","cv", "avs_test","sds_test","cv_test"), c('lightgrey','lightgreen','pink','lightgrey','lightgreen','pink')) ) %>% formatRound(columns=colnames(Conctab), digits=3) }) output$Conctab <- DT::renderDataTable({ if (!is.na(Dils()[1]) & is.na(Dils()[4])) return(NULL) tab <- sim2() if (is.character(tab)) stop(tab) if (!is.na(Dils()[4])) { noDilSer <- Dils()[4] } else if (!is.null(Dat$noDilSeriesXL)) { noDilSer <- Dat$noDilSeriesXL } else { noDilSer <- 3 } Conc <- CONC() Conctab <- perConcTab(tab, noDilSeries = noDilSer) Dat$Conctab <- Conctab dat <- datatable(Conctab, options=list( paging=T, pageLength=12, dom="t" )) %>% formatStyle(0, target='row', backgroundColor = styleEqual(c("avs","sds","cv", "avs_test","sds_test","cv_test"), c('lightgrey','lightgreen','pink','lightgrey','lightgreen','pink')) ) %>% formatRound(columns=colnames(Conctab), digits=3) }) #### process XL linearly, Plot output ---- output$plotLin <- renderPlot({ if (is.null(Dat$EXCEL)) return(NULL) tab <- Dat$EXCEL # tab <- sim2() if (is.character(tab)) stop(tab) #browser() log_conc <- tab$log_dose noDilSer = (ncol(tab)-1)/2 noDil <- nrow(tab) Conctab <- perConcTab(tab, noDilSer) # if (!is.na(Dils()[3])) noDil <- Dils()[3] else noDil = length(Conc) # slopeSt <- slopeTe <- matrix(NA, nrow=noDil-2,ncol=2) for (i in 1:(noDil-2)) { avs <- Conctab[noDilSer+1,] threes <- data.frame(lnC=log_conc[i:(i+2)], resp=avs[i:(i+2)]) lm3St <- lm(resp ~ lnC, data=threes) slopeSt[i,] <- lm3St$coefficients avt <- Conctab[noDilSer*2+4,] threet <- data.frame(lnC=log_conc[i:(i+2)], resp=avt[i:(i+2)]) lm3Te <- lm(resp ~ lnC, data=threet) slopeTe[i,] <- lm3Te$coefficients } indS <- which(abs(slopeSt[,2]) == max(abs(slopeSt[,2]))) indT <- which(abs(slopeTe[,2]) == max(abs(slopeTe[,2]))) pl_ <- slopeSt[indS,1]+slopeSt[indS,2]*log_conc pl_T <- slopeTe[indT,1]+slopeTe[indT,2]*log_conc pl_df <- data.frame(lnC=log_conc, plotS=pl_, plotT=pl_T) all_l <- melt(data.frame(tab), id.vars="log_dose",variable.name="replname",value.name="readout") isRef <- rep(c(1,0), 1,each=nrow(all_l)/2) isSample <- rep(c(0,1), 1,each=nrow(all_l)/2) all_l2 <- cbind(all_l,isRef, isSample) all_l2S <- all_l2[all_l2$isRef == 1,] all_l2T <- all_l2[all_l2$isRef == 0,] all_mS <- all_l2S[order(all_l2S$log_dose, decreasing=TRUE),] all_mT <- all_l2T[order(all_l2T$log_dose, decreasing=TRUE),] circleS <- all_mS[(indS*noDilSer-(noDilSer-1)):((indS+2)*noDilSer),] circleT <- all_mT[(indT*noDilSer-(noDilSer-1)):((indT+2)*noDilSer),] circle <- rbind(circleS,circleT) Dat$circles <- circle REPlin$circles <- circle #browser() sigmoid <- Dat$coeffsMUnr pLin <- PlotLinPLA_FUNC(circle, sigmoid = sigmoid, all_l2, pl_df, indS, indT) REPlin$pLin <- pLin pLin }) #### process metadata, Plot output ---- output$plotLinMeta <- renderPlot({ tab <- sim2() if(is.null(tab)) return(NULL) if (is.character(tab)) stop(tab) #browser() if (!is.na(Dils()[4])) noDilSer <- Dils()[4] else noDilSer = (ncol(tab)-1)/2 Conc <- CONC() log_conc <- log(Conc) Conctab <- perConcTab(tab, noDilSer) if (!is.na(Dils()[3])) noDil <- Dils()[3] else noDil = length(Conc) slopeSt <- slopeTe <- matrix(NA, nrow=noDil-2,ncol=2) for (i in 1:(noDil-2)) { avs <- Conctab[noDilSer+1,] threes <- data.frame(lnC=log(Conc[i:(i+2)]), resp=avs[i:(i+2)]) lm3St <- lm(resp ~ lnC, data=threes) slopeSt[i,] <- lm3St$coefficients avt <- Conctab[noDilSer*2+4,] threet <- data.frame(lnC=log(Conc[i:(i+2)]), resp=avt[i:(i+2)]) lm3Te <- lm(resp ~ lnC, data=threet) slopeTe[i,] <- lm3Te$coefficients } indS <- which(abs(slopeSt[,2]) == max(abs(slopeSt[,2]))) indT <- which(abs(slopeTe[,2]) == max(abs(slopeTe[,2]))) pl_ <- slopeSt[indS,1]+slopeSt[indS,2]*log_conc pl_T <- slopeTe[indT,1]+slopeTe[indT,2]*log_conc pl_df <- data.frame(lnC=log_conc, plotS=pl_, plotT=pl_T) all_l <- melt(data.frame(tab), id.vars="log_dose",variable.name="replname",value.name="readout") isRef <- rep(c(1,0), 1,each=nrow(all_l)/2) isSample <- rep(c(0,1), 1,each=nrow(all_l)/2) all_l2 <- cbind(all_l,isRef, isSample) all_l2S <- all_l2[all_l2$isRef == 1,] all_l2T <- all_l2[all_l2$isRef == 0,] all_mS <- all_l2S[order(all_l2S$log_dose, decreasing=TRUE),] all_mT <- all_l2T[order(all_l2T$log_dose, decreasing=TRUE),] circleS <- all_mS[(indS*noDilSer-(noDilSer-1)):((indS+2)*noDilSer),] circleT <- all_mT[(indT*noDilSer-(noDilSer-1)):((indT+2)*noDilSer),] circle <- rbind(circleS,circleT) Dat$circlesMeta <- circle sigmoid <- sigmoid() #browser() pLin2 <- PlotLinPLA_FUNC(circle, sigmoid = sigmoid, all_l2, pl_df,indS, indT) pLin2 }) #### linear PLA tests Metadata ---- output$TESTSlinMeta <- DT::renderDataTable({ tab <- sim2() if (is.null(tab)) return(NULL) Conc <- CONC() Limite <- Dat$limite circlesMeta <- Dat$circlesMeta PureErrFlag <- input$PureErrMeta warning_text <- reactive({ ifelse(PureErrFlag, 'Pure error is selected','') }) output$PureErrW <- renderText(warning_text()) #browser() LIN <- ANOVAlintests(tab,circlesMeta,Limite,PureErrFlag=PureErrFlag) df <- LIN[[1]] su_modU <- LIN[[2]] su_mod2 <- LIN[[4]] output$SummaryModABuMeta <- renderTable({ su_modU }, digits=5) output$SummaryModABMeta <- renderTable({ su_mod2 }, digits=5) slopeDiffCI <- t(data.frame(LIN[[3]])) colnames(slopeDiffCI) <- c("slope difference","lower CI","upper CI") output$SlopeDiffCIMeta <- renderTable({ slopeDiffCI },digits=5) SelTestsL <- as.numeric(input$selectedSSTsLinear) df2 <- df Dat$ANOVAMeta <- df[,4:length(df)] dat <- datatable(df2[,1:3], options=list( paging=T, dom="t",rownames=F )) %>% formatStyle("test_results", target="row",backgroundColor = styleEqual(c(-1,0,1), c("pink","lightgreen","lightgrey"))) }) #### linear PLA tests XLinput ---- #output$TESTSlin <- DT::renderDataTable({ observe({ if (is.null(Dat$EXCEL)) return(NULL) tab <- Dat$EXCEL if (is.character(tab)) stop(tab) Conc <- exp(tab$log_dose) Limite <- list(as.numeric(input$lEACdiffla), as.numeric(input$uEACdiffla), as.numeric(input$lEACratiola), as.numeric(input$uEACratiola), as.numeric(input$lEACratioSlope), as.numeric(input$uEACratioSlope), as.numeric(input$lEACratioua), as.numeric(input$uEACratioua), as.numeric(input$EACLinlow), as.numeric(input$EACLinupp), as.numeric(input$lEACratioAdiff), as.numeric(input$uEACratioAdiff)) noDil <- nrow(tab) noDilSer <- Dat$noDilSeriesXL Conctab <- perConcTab(tab, noDilSeries = noDilSer) #browser() slopeSt <- slopeTe <- matrix(NA, nrow=noDil-2,ncol=2) for (i in 1:(noDil-2)) { avs <- Conctab[noDilSer+1,] threes <- data.frame(lnC=log(Conc[i:(i+2)]), resp=avs[i:(i+2)]) lm3St <- lm(resp ~ lnC, data=threes) slopeSt[i,] <- lm3St$coefficients avt <- Conctab[noDilSer*2+4,] threet <- data.frame(lnC=log(Conc[i:(i+2)]), resp=avt[i:(i+2)]) lm3Te <- lm(resp ~ lnC, data=threet) slopeTe[i,] <- lm3Te$coefficients } indS <- which(abs(slopeSt[,2]) == max(abs(slopeSt[,2]))) indT <- which(abs(slopeTe[,2]) == max(abs(slopeTe[,2]))) # pl_ <- slopeSt[indS,1]+slopeSt[indS,2]*log_conc # pl_T <- slopeTe[indT,1]+slopeTe[indT,2]*log_conc # pl_df <- data.frame(lnC=log_conc, plotS=pl_, plotT=pl_T) all_l <- melt(data.frame(tab), id.vars="log_dose",variable.name="replname",value.name="readout") isRef <- rep(c(1,0), 1,each=nrow(all_l)/2) isSample <- rep(c(0,1), 1,each=nrow(all_l)/2) all_l2 <- cbind(all_l,isRef, isSample) all_l2S <- all_l2[all_l2$isRef == 1,] all_l2T <- all_l2[all_l2$isRef == 0,] all_mS <- all_l2S[order(all_l2S$log_dose, decreasing=TRUE),] all_mT <- all_l2T[order(all_l2T$log_dose, decreasing=TRUE),] circleS <- all_mS[(indS*noDilSer-(noDilSer-1)):((indS+2)*noDilSer),] circleT <- all_mT[(indT*noDilSer-(noDilSer-1)):((indT+2)*noDilSer),] circle <- rbind(circleS,circleT) PureErrFlag <- input$PureErr warning_text <- reactive({ ifelse(PureErrFlag, 'Pure error is selected','') }) output$PureErrW3 <- renderText(warning_text()) #browser() LIN <- ANOVAlintests(tab,circle,Limite,PureErrFlag=PureErrFlag) df <- LIN[[1]] su_modU <- LIN[[2]] su_mod2 <- LIN[[4]] output$SummaryModABu <- renderTable({ su_modU }, digits=5) output$SummaryModAB <- renderTable({ su_mod2 }, digits=5) REPlin$SuModABu <- su_modU REPlin$SuModAB <- su_mod2 slopeDiffCI <- t(data.frame(LIN[[3]])) colnames(slopeDiffCI) <- c("slope difference","lower CI","upper CI") output$SlopeDiffCI <- renderTable({ slopeDiffCI },digits=5) SelTestsL <- as.numeric(input$selectedSSTsLinear) df2 <- df[SelTestsL,] REPlin$LinTests <- df2 Dat$ANOVA <- df[,4:length(df)] dat <- datatable(df2[,1:3], options=list( paging=T, dom="t",rownames=F )) %>% formatStyle("test_results", target="row",backgroundColor = styleEqual(c(-1,0,1), c("pink","lightgreen","lightgrey"))) output$TESTSlin <- DT::renderDataTable({ dat }) }) #### output 4PL ANOVA tests Meta ---- output$ANOVA <- DT::renderDataTable({ sigmoid <- sigmoid() tab <- ANOVA4plUnresfunc(sim2()) # ,sigmoid dat <- datatable(tab, options=list( dom="t",rownames=F )) %>% formatStyle("p_value", target="row", backgroundColor = styleEqual(c("p_value"), c("lightgrey"))) }) #### output 4PL ANOVA tests XL ---- # not needed # output$ANOVA_XL <- DT::renderDataTable({ # tab <- Dat$EXCEL # tab <- ANOVA4plUnresfunc(sim2()) # ,sigmoid # dat <- datatable(tab, # options=list( # dom="t",rownames=F # )) %>% formatStyle("p_value", target="row", # backgroundColor = styleEqual(c("p_value"), # c("lightgrey"))) # }) #### output RMSEs ---- output$RMSE <- renderText({ paste("RMSE (unrestricted model):", Dat$RMSE_unr, "(Use it to compare against RMSE restr. model for non-parallelism)\n", "RMSE (restricted model):", Dat$RMSE_r, "\n", "Pure RMSE (unrestricted model):", Dat$RMSE_pure, "\n", "%SD (unr. model): ", Dat$RMSE_unr*100/Dat$up_lowAs, "(calculated as: RMSE/(upper-lower Asymptote)*100\n", "RMSE (log restr. model): ", Dat$RMSE_Rlog, "\n", "RMSE (log unrestr. model): ", Dat$RMSE_Ulog, "\n", "%SDlog (unr model): ", Dat$RMSE_Ulog*100/Dat$up_lowAslog ) }) output$ANOVAlin <- DT::renderDataTable({ if (is.null(Dat$ANOVA)) return(NULL) ANOVAlin <- Dat$ANOVA dat <- datatable(ANOVAlin, options=list( dom="t",rownames=F )) %>% formatStyle("p.value", target='cell', backgroundColor = styleEqual(c("p.value"), c("lightgrey"))) }) output$ANOVAlinMeta <- DT::renderDataTable({ ANOVAlin <- Dat$ANOVAMeta dat <- datatable(ANOVAlin, options=list( dom="t",rownames=F )) %>% formatStyle("p.value", target='cell', backgroundColor = styleEqual(c("p.value"), c("lightgrey"))) }) #### output Lin pot tab XL ---- output$pottab <- DT::renderDataTable({ if (is.null(Dat$circles)) return(NULL) Lim <- list(as.numeric(input$lEACdiffla), as.numeric(input$uEACdiffla), as.numeric(input$lEACratiola), as.numeric(input$uEACratiola), as.numeric(input$lEACratioSlope), as.numeric(input$uEACratioSlope), as.numeric(input$lEACratioua), as.numeric(input$uEACratioua), as.numeric(input$lowerPot), as.numeric(input$upperPot)) circles <- Dat$circles PureErrFlag <- input$PureErr warning_text2 <- reactive({ ifelse(PureErrFlag, 'Pure Error is selected', '') }) output$PureErrWLinXL <- renderText(warning_text2()) LinPotTab <- LinPotTab(circles,Lim,PureErrFlag = PureErrFlag) REPlin$LinPotTab <- LinPotTab dat <- datatable(LinPotTab, options=list( dom="t",rownames=F )) %>% formatStyle("test_result", target='row', backgroundColor = styleEqual(c(0,1), c("#B5C74055","#F9545488"))) }) ### output pot tab Meta ---- output$pottabMeta <- DT::renderDataTable({ Lim <- list(as.numeric(input$lEACdiffla), as.numeric(input$uEACdiffla), as.numeric(input$lEACratiola), as.numeric(input$uEACratiola), as.numeric(input$lEACratioSlope), as.numeric(input$uEACratioSlope), as.numeric(input$lEACratioua), as.numeric(input$uEACratioua), as.numeric(input$lowerPot), as.numeric(input$upperPot)) #browser() circles <- Dat$circlesMeta PureErrFlag <- input$PureErrMeta pottab <- LinPotTab(circles,Lim,PureErrFlag = PureErrFlag) #browser() dat <- datatable(pottab, options=list( dom="t",rownames=F )) %>% formatStyle("test_result", target='row', backgroundColor = styleEqual(c(0,1), c("#B5C74055","#F9545488"))) }) #### 4pl potency table Meta ---- observe({ #browser() if (is.null(sim2()) | is.null(Dils())) return(NULL) ro_new <- sim2() Dils_ <- Dils() if (!is.na(Dils()[4])) noDilSer <- Dils()[4] else noDilSer <- 3 PureErrFl <- input$PureErrMeta pottab4 <- pot4plFUNC(ro_new = ro_new, PureErrFlag = PureErrFl) #browser() Lim <- list(as.numeric(input$lEACdiffla), as.numeric(input$uEACdiffla), as.numeric(input$lEACratiola), as.numeric(input$uEACratiola), as.numeric(input$lEACratioSlope), as.numeric(input$uEACratioSlope), as.numeric(input$lEACratioua), as.numeric(input$uEACratioua), as.numeric(input$lowerPot), as.numeric(input$upperPot)) pottab4_ <- data.frame(pottab4) pottab4_$potency <- round(as.numeric(pottab4[,2])*100,1) pottab4_$`lower95%CI` <- round(as.numeric(pottab4[,3])*100,2) pottab4_$`upper95%CI` <- round(as.numeric(pottab4[,4])*100,2) pottab4_$relative_lowerCL <- round(pottab4_[,6]/pottab4_[,5]*100,2) pottab4_$relative_upperCL <- round(pottab4_[,7]/pottab4_[,5]*100,2) if (as.numeric(pottab4_$relative_lowerCL[1]) > Lim[[9]] & as.numeric(pottab4_$relative_upperCL[1]) < Lim[[10]] ) { test_potCI <- 0 } else {test_potCI <- 1 } if (as.numeric(pottab4_$relative_lowerCL[2]) > Lim[[9]] & as.numeric(pottab4_$relative_upperCL[2]) < Lim[[10]] ) { test_potUCI <- 0 } else {test_potUCI <- 1 } if (as.numeric(pottab4_$relative_lowerCL[3]) > Lim[[9]] & as.numeric(pottab4_$relative_upperCL[3]) < Lim[[10]] ) { test_potCI_t <- 0 } else {test_potCI_t <- 1 } if (as.numeric(pottab4_$relative_lowerCL[4]) > Lim[[9]] & as.numeric(pottab4_$relative_upperCL[4]) < Lim[[10]] ) { test_potUCI_t <- 0 } else {test_potUCI_t <- 1 } pottab4_ <- cbind(pottab4_[,-(2:4)], data.frame(tests=c(test_potCI, test_potUCI,test_potCI_t,test_potUCI_t))) colnames(pottab4_) <- c("model","potency","lower95%CI","upper95%CI","relative_lower95%CI","relative_upper95%CI","test_result") output$pottab4pl <- DT::renderDataTable({ dat <- datatable(pottab4_[1:2,], options=list(digits=3, paging=T, dom="t",rownames=F )) %>% formatStyle("test_result", target="row",backgroundColor = styleEqual(c(0,1), c("lightgreen","pink"))) }) output$pottab4plTrans <- DT::renderDataTable({ dat <- datatable(pottab4_[3:4,], options=list(digits=3, paging=T, dom="t",rownames=F )) %>% formatStyle("test_result", target="row",backgroundColor = styleEqual(c(0,1), c("lightgreen","pink"))) }) }) #### 4pl potency table XL ---- observe({ #browser() if (is.null(Dat$EXCEL)) return(NULL) if (!is.null(Dat$FITsFlag)) return(NULL) ro_new <- Dat$EXCEL noDilSer <- Dat$noDilSeriesXL PureErrFl <- input$PureErr pottab4 <- pot4plFUNC(ro_new = ro_new, PureErrFlag = PureErrFl) #browser() Lim <- list(as.numeric(input$lEACdiffla), as.numeric(input$uEACdiffla), as.numeric(input$lEACratiola), as.numeric(input$uEACratiola), as.numeric(input$lEACratioSlope), as.numeric(input$uEACratioSlope), as.numeric(input$lEACratioua), as.numeric(input$uEACratioua), as.numeric(input$lowerPot), as.numeric(input$upperPot)) REP$Lim <- Lim pottab4_ <- data.frame(pottab4) pottab4_$potency <- round(as.numeric(pottab4[,2])*100,1) pottab4_$`lower95%CI` <- round(as.numeric(pottab4[,3])*100,2) pottab4_$`upper95%CI` <- round(as.numeric(pottab4[,4])*100,2) pottab4_$relative_lowerCL <- round(pottab4_[,6]/pottab4_[,5]*100,2) pottab4_$relative_upperCL <- round(pottab4_[,7]/pottab4_[,5]*100,2) if (as.numeric(pottab4_$relative_lowerCL[1]) > Lim[[9]] & as.numeric(pottab4_$relative_upperCL[1]) < Lim[[10]] ) { test_potCI <- 0 } else {test_potCI <- 1 } if (as.numeric(pottab4_$relative_lowerCL[2]) > Lim[[9]] & as.numeric(pottab4_$relative_upperCL[2]) < Lim[[10]] ) { test_potUCI <- 0 } else {test_potUCI <- 1 } if (as.numeric(pottab4_$relative_lowerCL[3]) > Lim[[9]] & as.numeric(pottab4_$relative_upperCL[3]) < Lim[[10]] ) { test_potCI_t <- 0 } else {test_potCI_t <- 1 } if (as.numeric(pottab4_$relative_lowerCL[4]) > Lim[[9]] & as.numeric(pottab4_$relative_upperCL[4]) < Lim[[10]] ) { test_potUCI_t <- 0 } else {test_potUCI_t <- 1 } pottab4_ <- cbind(pottab4_[,-(2:4)], data.frame(tests=c(test_potCI, test_potUCI,test_potCI_t,test_potUCI_t))) colnames(pottab4_) <- c("model","potency","lower95%CI","upper95%CI","relative_lower95%CI","relative_upper95%CI","test_result") row.names(pottab4_) <- NULL REP$pottab4plXL <- pottab4_[1:2,] output$pottab4plXL <- DT::renderDataTable({ dat <- datatable(pottab4_[1:2,],rownames=F, options=list(digits=3, paging=T, dom="t" )) %>% formatStyle("test_result", target="row",backgroundColor = styleEqual(c(0,1), c("#B5C74055","#F9545455"))) }) output$pottab4plTransXL <- DT::renderDataTable({ dat <- datatable(pottab4_[3:4,],rownames=F, options=list(digits=3, paging=T, dom="t" )) %>% formatStyle("test_result", target="row",backgroundColor = styleEqual(c(0,1), c("#B5C74055","#F9545455"))) }) }) #### Dilutions Simulator ---- output$plotfordilutions <- renderPlot({ tab <- sim2() #browser() tab <- as.data.frame(tab) dils <- tab$log_dose min_y <- min(tab[,1:3]) max_y <- max(tab[,1:3]) if (input$fixupper) { dils_av <- dils-max(dils) dils_av_ <- dils_av*(input$dilslider/100+1) dils2 <- round(dils_av_ + max(dils),4) dilfactors <- 1/exp(dils2-lag(dils2)) } else { if (!is.null(Dat$cfordils)) { av <- Dat$cfordils } else { av <- (min(dils) + max(dils))/2 } dils_av <- dils-av dils_avsc <- dils_av*(input$dilslider/100+1) dils2 <- dils_avsc+av dilfactors <- 1/exp(dils2-lag(dils2)) } Dat$newDils <- dils2 sigmoid <- sigmoid() #browser() BPs <- Dat$bendpoints EC50REF <- (BPs[2]+BPs[1])/2 Einh <- abs((BPs[2]-BPs[1])/5) asyml <- EC50REF-2*(EC50REF-BPs[1]) asymu <- EC50REF+2*(EC50REF-BPs[1]) det_sig <- Dat$coeffs_UN if (is.null(Dat$coeffs_UN)) { SAMPLE50 <- sigmoid[1] + (sigmoid[3] - sigmoid[1])/(1+exp(sigmoid[5]*( (sigmoid[7]+0.693147)- dils2))) SAMPLE200 <- sigmoid[1] + (sigmoid[3] - sigmoid[1])/(1+exp(sigmoid[5]*( (sigmoid[7]-0.693147)-dils2))) Xbend50l <- sigmoid[7] + 0.693147-1.5434/sigmoid[5] Xbend200l <- sigmoid[7] - 0.693147-1.5434/sigmoid[5] Xbend50u <- sigmoid[7] + 0.693147+1.5434/sigmoid[5] Xbend200u <- sigmoid[7] - 0.693147+1.5434/sigmoid[5] Xbend50 <- max(Xbend50l, Xbend50u) Xbend200 <- min(Xbend200l, Xbend200u) dummy <- plot_f(tab) } else { #browser() SAMPLE50 <- det_sig[3] + (det_sig[5] - det_sig[3])/(1+exp(det_sig[1]*(det_sig[7]+0.693147-dils2))) SAMPLE200 <- det_sig[3] + (det_sig[5] - det_sig[3])/(1+exp(det_sig[1]*(det_sig[7]-0.693147-dils2))) Xbend50l <- det_sig[7] + 0.693147-1.5434/det_sig[1] Xbend200l <- det_sig[7] - 0.693147-1.5434/det_sig[1] Xbend50u <- det_sig[7] + 0.693147+1.5434/det_sig[1] Xbend200u <- det_sig[7] - 0.693147+1.5434/det_sig[1] Xbend50 <- max(Xbend50l, Xbend50u) Xbend200 <- min(Xbend200l, Xbend200u) dummy <- plot_f(tab) } pl_df <- cbind(dils2, SAMPLE50, SAMPLE200) #browser() # scenario2 eqSpac <- abs((BPs[1]-BPs[2])/5) optdils <- c((asyml+BPs[1])/2, BPs[1], BPs[1]+1*eqSpac, BPs[1]+2*eqSpac,BPs[1]+3*eqSpac,BPs[1]+4*eqSpac,BPs[2], (asymu+BPs[2])/2) # scenario 3 eqSpac_3 <- abs((BPs[1]-BPs[2])/3) optdils_3 <- c(BPs[1]-2*eqSpac_3, BPs[1]-eqSpac_3, BPs[1], BPs[1]+1*eqSpac_3, BPs[1]+2*eqSpac_3,BPs[2], BPs[2]+eqSpac_3, BPs[2]+2*eqSpac_3) # scenario 6 Einh2 <- abs(((BPs[2]-BPs[1])*0.7)/5) eqSpac2 <- (2*0.7/Einh)/3 optdils2 <- c((asyml+BPs[1])/2, BPs[1], EC50REF-1.5*Einh2, EC50REF-0.5*Einh2,EC50REF+0.5*Einh2,EC50REF+1.5*Einh2, BPs[2], (asymu+BPs[2])/2) # steep slope eqSpac3 <- (abs(Xbend200-Xbend50))/5 optdils3 <- c(Xbend200-eqSpac3,Xbend200, Xbend200+1*eqSpac3, Xbend200+2*eqSpac3,Xbend200+3*eqSpac3,Xbend200+4*eqSpac3,Xbend50, Xbend50+eqSpac3) output$extremebps <- renderTable({ ExtremeBPs <- c(Xbend50,Xbend200) DF2 <- data.frame(sample=c("50% sample (right)", "200% sample (left)"), Extreme_BPs=ExtremeBPs) DF2 }) optD <- data.frame(cbind(optdils, optdils_3,optdils2, optdils3)) colnames(optD) <- c("scenario2","scenario3","scenario6","steep slope") output$optimalDils <- renderTable({ optD }) output$adjlogdil <- renderTable({ adjlogdilfactors <- round(dilfactors,3) adjlogdils <- round(dils2,3) adjdils <- round(exp(dils2),3) DilsTable <- data.frame('adjusted ln(dilutions)' = adjlogdils, 'adjusted ln_dilution_factors' = adjlogdilfactors, 'adjusted dilutions' = adjdils) DilsTable }) if (!is.null(Dat$p2)) { p2 <- Dat$p2 p_dil <- p2 + annotate("pointrange",x=dils2,y=rep(min_y, length(dils2)), xmin=min(dils2), xmax=max(dils2)) + annotate("text", x=dils2,y=rep(min_y+(max_y-min_y)*0.05, length(dils2)), label=as.character(round(dils2,3))) + annotate("text", x=dils2[-1]+(max(dils2)-min(dils2))*0.05, y=rep(min_y+(max_y-min_y)*0.1, length(dils2[-1])), label=as.character(round(dilfactors[-1],3))) + geom_line(data=as.data.frame(pl_df),aes(x=dils2,y=SAMPLE50), color="grey15", linetype=2, inherit.aes = F) + geom_line(data=as.data.frame(pl_df),aes(x=dils2,y=SAMPLE200), color="grey15", linetype=2, inherit.aes = F) + geom_vline(xintercept=c(Xbend50,Xbend200), col="grey15", linetype=2) + {if (input$scenario =="scenario 6") annotate("pointrange",x=optdils2,y=rep(min_y+(max_y-min_y)*0.2, length(optdils2)), xmin=min(optdils2), xmax=max(optdils2), color="seagreen")} + {if (input$scenario =="scenario 6") annotate("text",x=optdils2,y=rep(min_y+(max_y-min_y)*0.25, length(optdils2)), label=as.character(round(optdils2,3)), color="seagreen")} + {if (input$scenario =="scenario 2") annotate("pointrange",x=optdils,y=rep(min_y+(max_y-min_y)*0.2, length(optdils)), xmin=min(optdils), xmax=max(optdils), color="seagreen")} + {if (input$scenario =="scenario 2") annotate("text",x=optdils,y=rep(min_y+(max_y-min_y)*0.25, length(optdils)), label=as.character(round(optdils,3)), color="seagreen")} + {if (input$scenario =="scenario 3") annotate("pointrange",x=optdils_3,y=rep(min_y+(max_y-min_y)*0.2, length(optdils_3)), xmin=min(optdils_3), xmax=max(optdils_3), color="seagreen")} + {if (input$scenario =="scenario 3") annotate("text",x=optdils_3,y=rep(min_y+(max_y-min_y)*0.25, length(optdils_3)), label=as.character(round(optdils_3,3)), color="seagreen")} + {if (input$scenario =="steep slope") annotate("pointrange",x=optdils3,y=rep(min_y+(max_y-min_y)*0.2, length(optdils3)), xmin=min(optdils3), xmax=max(optdils3), color="seagreen")} + {if (input$scenario =="steep slope") annotate("text",x=optdils3,y=rep(min_y+(max_y-min_y)*0.25, length(optdils3)), label=as.character(round(optdils3,3)), color="seagreen")} + annotate("text",x=optdils[1],y=(max_y+min_y)*0.5, label=paste("in green: optimal \n dilutions acc. to Whitepaper\n", input$scenario), color="seagreen", size=14/.pt,fontface="bold") } print(p_dil) }) #### Dilutions CI table ---- observe({ if (is.null(input$potencydiff)) return(NULL) output$CIs <- renderTable({ PureErrFlag <- input$PureErr if (is.null(Dat$coeffs_UN)) { # checks if an EXCEL was uploaded sigmoid <- sigmoid() det_sig=NULL ast = sigmoid()[1];bst = sigmoid()[5];cst = sigmoid()[7];dst = sigmoid()[3];ate = sigmoid()[2]; bte = sigmoid()[6];r_ = sigmoid()[8]; cte = cst-r_;dte = sigmoid()[4]; } else { sigmoid <- NULL det_sig <- Dat$coeffs_UN ast <- det_sig[3] ate <- det_sig[4] bst <- det_sig[1] bte <- det_sig[2] cst <- det_sig[7] cte <- det_sig[7] -log(input$potencydiff/100) dst <- det_sig[5] dte <- det_sig[6] r_ <- log(input$potencydiff/100) } if (!is.na(input$NoDilSer)) { noDilSer <- input$NoDilSer } else if (!is.null(Dat$NoDilSeriesXL)) noDilSer <- Dat$noDilSeriesXL else noDilSer <- 3 if (!is.na(input$NoDil)) noDil <- input$NoDil else noDil <- length(Dat$newDils) #browser() tab <- Calc_DilRes(as=ast,at=ate,ds=dst,dt=dte,cs=cst,ct=cte,r=r_,bt=bte,bs=bst, sd_fac=input$sdfac,log_conc=Dat$newDils, # auslenkU=outlierU, # auslenkM=outlierM, # auslenkL=outlierL, heteroNoise = FALSE, noDilSeries = noDilSer, noDils = noDil) Limite <- list(as.numeric(input$lEACdiffla), as.numeric(input$uEACdiffla), as.numeric(input$lEACratiola), as.numeric(input$uEACratiola), as.numeric(input$lEACratioSlope), as.numeric(input$uEACratioSlope), as.numeric(input$lEACratioua), as.numeric(input$uEACratioua), as.numeric(input$lowerPot), as.numeric(input$upperPot), as.numeric(input$lEACratioAdiff), as.numeric(input$uEACratioAdiff)) CItable <- tests_FUNC(tab,Limite,PureErrFlag=PureErrFlag) CItable_ <- CItable[-c(1,2,6,8,9),-c(2,4,5)] potAll <- pot4plFUNC(tab, input$PureErr) restrPot <- potAll[1,1:4] restrPot[2:4] <- round(as.numeric(restrPot[2:4]),5) potAll_ <- rbind(CItable_, restrPot) potAll_$CIwidth <- as.numeric(potAll_[,4])-as.numeric(potAll_[,3]) potAll_[,1] <- c("ratio of lower asymptotes","ratio of slopes","ratio of upper asymptotes", "ratio of asympt. differences","restricted potency") output$bps <- renderTable({ DF <- data.frame(sample=names(Dat$bendpoints),BPs=Dat$bendpoints) DF }) return(potAll_) }) }) #### simulations ---- observe({ observeEvent(input$goSim,{ sd_fac_ <- as.numeric(input$sdfac) r_ <- log(as.numeric(input$potencydiff)/100) Conc <- Dat$MetaConc as = sigmoid()[1]; bs = sigmoid()[5];cs = sigmoid()[7];ds = sigmoid()[3];at = sigmoid()[2]; bt = sigmoid()[6];r = sigmoid()[8]; ct = cs-r_; dt = sigmoid()[4] if (!is.null(Dat$MetaConc)) { Conc <- Dat$MetaConc } else { Conc <- CONC() } log_dose <- log(Conc) yAxfac <- (ds-as) if (!is.na(input$NoDilSer)) { noDilSer <- input$NoDilSer } else if (!is.null(Dat$NoDilSeriesXL)) noDilSer <- Dat$noDilSeriesXL else noDilSer <- 3 if (!is.na(input$NoDil)) noDil <- input$NoDil else noDil <- length(Conc) isRef <- rep(c(1,0),1,each=noDilSer*noDil) isSample <- rep(c(0,1),1,each=noDilSer*noDil) N <- as.numeric(input$simN) av <- as*isRef + at*isSample + (ds*isRef + dt*isSample - as*isRef - at*isSample)/ (1+isRef*exp(bs*(cs - log_dose)) + isSample*exp(bt*(ct-log_dose))) resHist <- matrix(NA,nrow=N, ncol=13) residualsList <- list() start.time2 <- Sys.time() withProgress(message = 'Making plot', value=0, { for (i in 1:N) { if (input$heterosked) { # heterosc noise ro_jit <- matrix(unlist(map(av, function(x) x+rnorm(1,0,x*sd_fac_/100))), nrow=noDil, ncol=noDilSer*2) } else { # homosc noise ro_jit <- matrix(unlist(map(av, function(x) x+rnorm(1,0,sd_fac_*yAxfac/100))), nrow=noDil, ncol=noDilSer*2) } # browser() ro_jit <- abs(ro_jit) ro_new <- cbind(ro_jit, log_dose) all_l <- melt(data.frame(ro_new), id.vars="log_dose", variable.name = "replname", value.name = "readout") all_l$isRef <- isRef all_l$isSample <- isSample all_l$Conc <- exp(all_l$log_dose) pot <- drm(readout ~ Conc, isSample, data=all_l, fct=LL.4(names=c("b","d","a","c")), pmodels=data.frame(1,1,1,isSample)) potAll <- EDcomp(pot, percVec=c(50,50), interval="delta", display=FALSE) potAll2 <- potAll[1:3] RSS <- sum(pot$predres[,2]^2) dfreed <- nrow(all_l)-5 MSE <- RSS/dfreed potU <- drm(readout ~ Conc, isSample, data=all_l, fct=LL.4(names=c("b","d","a","c")), pmodels=data.frame(isSample, isSample,isSample,isSample)) DF_U <- nrow(all_l)-8 uAsratio <- compParm(potU, "a",display=F) uCIuAs <- uAsratio[1]+qt(0.975,DF_U)*uAsratio[2] lCIuAs <- uAsratio[1]-qt(0.975,DF_U)*uAsratio[2] lAsratio <- compParm(potU, "d",display=F) uCIlAs <- lAsratio[1]+qt(0.975,DF_U)*lAsratio[2] lCIlAs <- lAsratio[1]-qt(0.975,DF_U)*lAsratio[2] Sloperatio <- compParm(potU, "b",display=F) uCISlo <- Sloperatio[1]+qt(0.975,DF_U)*Sloperatio[2] lCISlo <- Sloperatio[1]-qt(0.975,DF_U)*Sloperatio[2] su <- summary(potU) v <- vcov(potU)[c(5,6),c(5,6)] Vd <- vcov(potU)[c(3,4),c(3,4)] Va_d <- v+Vd A_DTEST <- su$coefficients[6,1]-su$coefficients[4,1] A_DREF <- su$coefficients[5,1]-su$coefficients[3,1] if (abs(at/(sqrt(Va_d[2,2]/3))) > qt(0.95,2)) { try(Fie_ad <- round(FiellerRatio(A_DREF,A_DTEST, Va_d),5)) } if (!exists("Fie_ad")) Fie_ad <- NA resHist[i,] <- c(potAll2, sqrt(MSE),Sloperatio[1],lCISlo, uCISlo, uAsratio[1], lCIuAs, uCIuAs, Fie_ad[1],Fie_ad[2],Fie_ad[3]) colnames(resHist) <- c("pot4pl","lCI4pl","uCI4pl","RMSE","estSlope_ratio", "lCISlope_ratio","uCISlope_ratio","estuAs_ratio", "lCIuAs_ratio","uCIuAs_ratio","estAsyDiff_ratio", "lCIAsyDiff_ratio", "uCIAsyDiff_ratio") incProgress(1/N, detail=paste("Doing simulations",i)) } # withProgress }) end.time2 <- Sys.time() Dat$resHist <- resHist }) }) #### simulation Histograms output ---- output$plotHistuAs <- renderPlot({ if (!is.null(Dat$resHist)) { resHist <- Dat$resHist #browser() resHistuAs <- as.data.frame(resHist[,8:10]) resHistuAs_l <- melt(data.frame(resHistuAs), variable.name="ratio_CIs", value.name = "readout") #browser() lowquant_uAs <- quantile(resHistuAs[,2], probs=as.numeric(input$lowQuant)/100) upquant_uAs <- quantile(resHistuAs[,3], probs=as.numeric(input$uppQuant)/100) p_uAs <- ggplot(resHistuAs_l) + geom_histogram(aes(readout, fill=ratio_CIs),alpha=0.5,position="identity") + labs(title = paste("upper asymptote ratio EACs:", round(lowquant_uAs,3), " to ", round(upquant_uAs,3))) + geom_vline(xintercept = c(lowquant_uAs, upquant_uAs), color="black", linetype="dashed", linewidth=1) + geom_vline(xintercept = c(input$lEACratioua , input$uEACratioua), color="red", linetype="dashed", linewidth=1) + theme_bw() # asympt diff ratio resHistAsDiff <- as.data.frame(resHist[,11:13]) resHistAsDiff_l <- melt(data.frame(resHistAsDiff), variable.name="ratio_CIs", value.name = "readout") lowquant_AsDiff <- quantile(resHistAsDiff[,2], probs=as.numeric(input$lowQuant)/100) upquant_AsDiff <- quantile(resHistAsDiff[,3], probs=as.numeric(input$uppQuant)/100) p_AsDiff <- ggplot(resHistAsDiff_l, aes(readout, fill=ratio_CIs)) + geom_histogram(alpha=0.5,position="identity") + labs(title = paste("asymptote diff. ratio EACs:", round(lowquant_AsDiff,3), " to ", round(upquant_AsDiff,3))) + geom_vline(xintercept = c(lowquant_AsDiff, upquant_AsDiff), color="black", linetype="dashed", linewidth=1) + geom_vline(xintercept = c(input$lEACratioAdiff , input$uEACratioAdiff), color="red", linetype="dashed", linewidth=1) + theme_bw() # Slope ratio resHistSlo <- as.data.frame(resHist[,5:7]) resHistSlo_l <- melt(data.frame(resHistSlo), variable.name="ratio_CIs", value.name = "readout") lowquant_Slo <- quantile(resHistSlo[,2], probs=as.numeric(input$lowQuant)/100) upquant_Slo <- quantile(resHistSlo[,3], probs=as.numeric(input$uppQuant)/100) p_Slo <- ggplot(resHistSlo_l, aes(readout, fill=ratio_CIs)) + geom_histogram(alpha=0.5,position="identity") + labs(title = paste("Slope ratio EACs:", round(lowquant_Slo,3), " to ", round(upquant_Slo,3))) + geom_vline(xintercept = c(lowquant_Slo, upquant_Slo), color="black", linetype="dashed", linewidth=1) + geom_vline(xintercept = c(input$lEACratioSlope , input$uEACratioSlope), color="red", linetype="dashed", linewidth=1) + theme_bw() # poency ratio resHistPot <- as.data.frame(resHist[,1:3]) resHistPot_l <- melt(data.frame(resHistPot), variable.name="ratio_CIs", value.name = "readout") lowquant_Pot <- quantile(resHistPot[,2], probs=as.numeric(input$lowQuant)/100) upquant_Pot <- quantile(resHistPot[,3], probs=as.numeric(input$uppQuant)/100) #browser() p_Pot <- ggplot(resHistPot_l, aes(readout, fill=ratio_CIs)) + geom_histogram(alpha=0.5,position="identity") + labs(title = paste("Poency ratio EACs:", round(lowquant_Pot,3), " to ", round(upquant_Pot,3))) + geom_vline(xintercept = c(lowquant_Pot, upquant_Pot), color="black", linetype="dashed", linewidth=1) + geom_vline(xintercept = c(input$lowerPot/100, input$upperPot/100), color="red", linetype="dashed", linewidth=1) + theme_bw() grid.arrange(p_Slo, p_AsDiff, p_uAs, p_Pot, nrow=1) } }) #### download XL 4PL report---- output$downloadXLReport <- downloadHandler( filename= paste0("Report_4PLEvaluation", Dat$RepIdentifier,".pdf"), content = function(file) { tpdr <- tempdir() tempReport <- file.path(tpdr,"Doc_BioassayReport.Rmd") file.copy("Doc_BioassayReport.Rmd", tempReport, overwrite = T) tempReportc <- file.path(tpdr,"logo.png") file.copy("logo.png", tempReportc, overwrite = T) rmarkdown::render(tempReport, output_file = file, params = list(FileName = Dat$FileName, author = Dat$Author, NoP = Dat$NoP, Assay = Dat$Assay, REP = REP, coeffs = Dat$coeffs_UN), envir = new.env(parent = globalenv())) } ) #### download XL Lin report---- output$downloadXLReportLin <- downloadHandler( filename= paste0("Report_linPLA",Dat$nameRep ,".pdf"), content = function(file) { tpdr <- tempdir() tempReport <- file.path(tpdr,"Doc_BioassayLinReport.Rmd") file.copy("Doc_BioassayLinReport.Rmd", tempReport, overwrite = T) tempReportc <- file.path(tpdr,"logo.png") file.copy("logo.png", tempReportc, overwrite = T) rmarkdown::render(tempReport, output_file = file, params = list(FileName = Dat$FileName, author = Dat$Author, REP = REP, REPlin = REPlin, coeffsLin = Dat$coeffs_UN), envir = new.env(parent = globalenv())) } ) } shinyApp(ui, server)