################################################################################ # 4PL and Linear bioassay evaluation # Author: Franz Innerbichler # 1.4.2026 ################################################################################ library(shiny) library(shinydashboard) library(shinyjs) library(shinyAce) library(shinycssloaders) library(shinyBS) library(purrr) library(gslnls) library(tidyverse) library(ggplot2) library(ggExtra) library(gtable) library(reshape2) library(openxlsx) library(DT) library(ggpubr) library(grid) library(gridExtra) library(drc) library(twopartm) library(car) library(dplyr) library(scales) source("../R/Global.R") #### ui ---- ui <- dashboardPage( dashboardHeader(title = "Plateflow"), dashboardSidebar( sidebarMenu( img(src = "logov2.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("INSPECT your data", tabName = "Dataupload", icon = icon("magnet", lib = "glyphicon")), menuItem("EXPLORE 4PL and linear reg", 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("OPTIMIZE with 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$u("InnerAnalytics")), paste(rep(" ", 9), collapse = ""), "Developer:", paste(rep(" ", 9), collapse = ""), "Host on:", paste(rep(" ", 9), collapse = ""), "Version:" )), align = "left", style = "position:fixed; bottom:0;width=100%; background: #FFC337BB; font-family: Times New Roman; font-size:100%; padding: 5px; color:#4545BA; box-sizing: border-box; z-index: 1000;" ) ), dashboardBody( fluidPage( tabItems( tabItem(tabName = "home", htmlOutput("homePage")), tabItem(tabName = "Dataupload", uiOutput("Dataupload")), tabItem(tabName = "fourPL", uiOutput("fourPL")), # tabItem(tabName = "XLdiagn", uiOutput("XLdiagn")), # tabItem(tabName = "pla", uiOutput("pla")), tabItem(tabName = "wizard", uiOutput("wizard")) # , # tabItem(tabName = "documentation", uiOutput("docu")) ) ) ), skin = "blue" ) #### server ---- server <- function(input, output, session) { #### reactive values ---- Dat <- reactiveValues() REP <- reactiveValues() REPlin <- reactiveValues() # this sets the environment of all functions that need to have access to the # reactiveValues objects to the environment of the server function. # this way, the reactiveValues are specific to the server namespace environment(plot_f) <- environment() environment(pot4plFUNC) <- environment() environment(tests_FUNC) <- environment() #### renderUIs Home ---- output$homePage <- renderUI({ navbarPage( "Home", tabPanel( "Introduction", tags$style(HTML("pre { color: black; background-color: #7FAEFF55; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif;font-size: 12px;} ")), h4("Introduction to the plateflow software"), # tags$mark("linear regression"), br(), column( 6, "INSPECT your plate reader data: This is the right place if you want to visualize your data in the context of a 4 PL fit or a linear regression fit. ", "Bring your data in a readable format and start inspecting.", br(), "Example of EXCEL/csv/numbers file:", br(), tags$img(src = "ExampleXL.png", class = "adv_logo", width = "100%"), "It needs to contain 1 column with the dilution concentrations (first or last column) and at least 2 columns of reference and test sample readouts, respectively.", "The reference readout columns have to be before the test sample readout columns. The column names for reference and test are free to set, but different for all columns.", "The column name of the dilution concentrations can be as follows: concentration, dose, log_concentration, log_dose (first letter can be capital)", "It is assumed, that the concentrations are in anti-log or in natural log mode.", ), column(6, ) ), tabPanel( "Documentation", h4("Introduction "), h4("Information on dilution setting"), "(for details see:", a(href = "ADONIS.pdf", "Whitepaper", download = NA, target = "_blank"), ")", tags$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$Dataupload <- renderUI({ navbarPage( title = "", tabPanel( title = "Real data", tabsetPanel( tabPanel( "Data input", column( 3, # img(src="Screenshot.png", width=200), box( title = "Upload", status = "warning", solidHeader = TRUE, width = 12, "Please upload your EXCEL file here", fileInput("iFile", "", accept = ".xlsx") ), uiOutput(outputId = "sheetName"), "For data format in the EXCEL file see Data template", "If no data are uploaded, the settings to the right are used for calculations.", tags$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"), "(potency CI test is set per default)", 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 Lack-of-Fit" = "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(2, style = "background: #7FAEFF88", 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) ), column(2, style = "background: #7FAEFF88", 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), 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) ) ), 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"), box( title = "Useful information", status = "info", solidHeader = TRUE, width = 12, "", collapsible = TRUE, tableOutput("VarDiagn") ) ), column( 8, plotOutput("XLplot"), htmlOutput("No4PLFitText"), DTOutput("pottab4plXL"), box( title = "Residuals and QQ-plot", status = "info", solidHeader = TRUE, width = 12, "", collapsible = TRUE, plotOutput("diagnplot") ), box( title = "Assay Suitability Tests", status = "info", solidHeader = TRUE, width = 12, "", collapsible = TRUE, DTOutput("EQtests") ), DTOutput("pottab4plTransXL"), box( title = "ANOVA", status = "info", solidHeader = TRUE, width = 12, "", collapsible = TRUE, 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 = TRUE, 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"), useShinyjs(), actionButton("btn", "Download 4PL PDF report", icon = icon("download")), downloadButton("downloadXLReport", style = "visibility: hidden;"), #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 = TRUE, 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$style("#PureErrW4plMeta{color: red; font-size: 16px; font_style: italic;}")), plotOutput("plot4plMeta", width = "80%"), DTOutput("pottab4pl"), "Footnote: test performed on relative CIs.", DTOutput("EQtests4pl"), # SSTs h5("*...The estimate for F-test on regression and on Lack-of-Fit is the p-value"), h5("F-test on regression passes if F-value > F-crit and thus p < 0.05"), h5("F-test on Lack-of-Fit passes if F-value < F-crit and thus p > 0.05"), h5("Test results outcome: 0 ... test passed (for EQ tests: CI within limits); 1 ... test failed (for EQ tests CI not within limits); -1 ... calculations unbound/denominator too close to 0"), # plotOutput("CIplot, height=50%") ), column( 8, "4 PL ANOVA unrestricted", box( title = "ANOVA unrestricted", status = "warning", solidHeader = TRUE, width = 12, "", DT::dataTableOutput("ANOVA") ), h5("Please note that for the CIs of rel. potency the RSS of the restricted model is used"), h5("RSS ... 'Residual error' in the SumSquares column"), h5("MSE ... 'Residual error' in the MeanSquaress column"), h5("SSE ... 'Pure error' in the SumSquares column"), h5("RMSE ... Square root of the 'Residual Error' in the MeanSquares column"), verbatimTextOutput("RMSE") ) ) ) ), tabPanel( "ln-transformed y", htmlOutput("PureErrWLogMeta"), tags$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$style("#PureErrW3{color: red; font-size: 16px; font_style: italic;}")), column( 12, plotOutput("plotLinMeta"), "Delta method is used for potency CIs", DTOutput("pottabMeta") ), column( 5, h3("Tests for linear PLA:"), box( title = "Suitability tests", status = "primary", solidHeader = TRUE, collapsible = TRUE, width = 12, DTOutput("TESTSlinMeta") ), h5("The estimate is the p-value of the test"), h5("F-tests on regression, significance of slopes, and preparation need to have a p-value <0.05 to pass"), h5("All other tests pass if p-value > 0.05"), box( title = "Unrestricted linear model (SSSI):", status = "primary", solidHeader = TRUE, collapsible = TRUE, width = 12, tableOutput("SummaryModABuMeta") ), h4("Restricted linear model (CSSI):"), box( title = "Restricted linear model (CSSI):", status = "primary", solidHeader = TRUE, collapsible = TRUE, width = 12, tableOutput("SummaryModABMeta") ) ), column( 6, h3("ANOVA for parallel line assay"), box( title = "ANOVA for simultated data", status = "primary", solidHeader = TRUE, collapsible = TRUE, width = 12, DTOutput("ANOVAlinMeta") ), " CI for difference of slopes:", tableOutput("SlopeDiffCIMeta"), ) ), tabPanel( "Report", h4("Settings for report"), downloadButton("downloadXLReport", label = "Download PDF report", class = "butt"), tags$style(type = "text/css", "#downloadXLReport {background-color: orange; color: black;font-family: COurier New}"), ) ) ) # ) ) ) }) output$sessioninfo <- renderPrint(sessionInfo()) output$pla <- renderUI({ navbarPage( title = "pla", tabPanel("Analysis and Plots", ) ) }) #### UI wizard ---- output$wizard <- renderUI({ navbarPage( title = "Dilution setting", tabPanel( "Plots", sidebarLayout( sidebarPanel( width = 3, fluidRow( box( title = "Upload multiple worksheets", status = "warning", solidHeader = TRUE, width = 12, "Please upload your EXCEL file here", fileInput("MiFile", "", accept = ".xlsx") ), 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) ) ), mainPanel( tabsetPanel( id = "tabs", tabPanel("4pl", column(12, # h3("Confidence intervals"), # tableOutput("CIs"), # "The confidence interval table is interactive for changes in: variability slider (%SD), potency of test-slider, # and 'Adjust the dilutions'-slider", # tableOutput("optimalDils"), plotOutput("sigPlotREF"), plotOutput("sigPlotTEST"), #selectInput(inputId = "scenario", label = "Select an 'optimal' scenario:", choices = c("scenario 2", "scenario 3", "scenario 6", "steep slope")) )), tabPanel("Dilution slider", h4("Adjust the dilutions"), plotOutput("plotfordilutions"), h5("Dilution factors"), box( title = "Adjusted dilutions", status = "primary", solidHeader = TRUE, width = 12, collapsible = TRUE, 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", ), tabPanel("Histograms", h4("Histograms of parameters"), plotOutput("histCIs"), column(6, plotOutput("histEC50REF"), plotOutput("histLasREF"), plotOutput("histUasREF") ), column(6, plotOutput("histEC50TEST"), plotOutput("histLasTEST"), plotOutput("histUasTEST") ) ), tabPanel( "Report", h4("Settings for report")) ) ) # main panel ) # sidebar Layout ) # tabpanel ) # navbarPage }) 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)) { 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 }) #### input optim XL file ---- observe({ if (!is.null(input$MiFile)) { MinFile <- input$MiFile ext <- tools::file_ext(MinFile$name) file.rename(MinFile$datapath, paste(MinFile$datapath, ".xlsx", sep = "")) t.filelocation <- gsub("\\\\", "/", paste(MinFile$datapath, ext, sep = ".")) sheets <- openxlsx::getSheetNames(t.filelocation) dat <- lapply(sheets, openxlsx::read.xlsx, xlsxFile = t.filelocation) names(dat) <- sheets Dat$Mws <- dat names(Dat$Mws) <- sheets Dat$Msheets <- sheets Dat$MFileName <- input$MiFile[["name"]] } }) #### process XLSX file ---- observe({ if (!is.null(input$iFile)) { if (!is.null(input$sheet)) { if (input$sheet != "please choose") { Dat$RepIdentifier <- input$RepIdentifier Dat$Author <- input$Author Dat$NoP <- input$NoP Dat$Assay <- input$Assay Dat$FITsFlag <- FALSE # browser() XLdat <- Dat$wb[input$sheet][[1]] if (is.null(XLdat)) XLdat <- Dat$wb[Dat$sheets[1]][[1]] cn <- colnames(XLdat) logI <- grep("log|ln", cn) logDoseI <- grep("log_dose", cn) if (length(logI) > 0 & length(logDoseI) == 0) { XLdat$log_dose <- XLdat[, logI] XLdat2 <- XLdat[, -logI] CORro <- cor(XLdat$log_dose, XLdat[, 3]) } else if (length(logI) == 0 & length(logDoseI) == 0) { Ind <- grep(".ilution|.ose|.onc", cn) XLdat$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$PureErrW2 <- renderText(warning_text2()) warning_textPureErr <- reactive({ ifelse(PureErrFlag, "Pure Error is selected", "") }) output$PureErrWParEst <- renderText(warning_textPureErr()) 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$XLplotSing <- pSing pSing }) warning_textNo4PLFit <- reactive({ ifelse(Dat$FITsFlag, "No meaningful 4PL fit was possible. This may havea several reasons: \nA control sample was tested/\n the EC50 is not catched with the dilutions/\n the assay/reader had a problem", "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." ) }) output$No4PLFitText <- renderText(warning_textNo4PLFit()) output$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 }) output$coeffs_r <- renderTable({ NULL }) output$bends_r2 <- renderTable({ NULL }) output$coeffs_unr <- renderTable({ NULL }) output$logcoeffs_r <- renderTable({ NULL }) output$bends_unr2 <- renderTable({ NULL }) output$logcoeffs_unr <- 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$coeffs_UN <- coeffsMU names(coeffsMU) <- c("lowAsym REF", "slope REF", "upperAsym REF", "EC50 REF", "lowAsym TEST", "slope TEST", "upperAsym TEST", "r") XbendMUlREF <- coeffsMU[4] - 1.5434 / abs(coeffsMU[2]) XbendMUuREF <- coeffsMU[4] + 1.5434 / abs(coeffsMU[2]) XbendMUlTEST <- coeffsMU[4] - coeffsMU[8] - 1.5434 / abs(coeffsMU[6]) XbendMUuTEST <- coeffsMU[4] + coeffsMU[8] + 1.5434 / abs(coeffsMU[6]) XbendMRlREF <- coeffsMR[4] - 1.5434 / abs(coeffsMR[2]) XbendMRuREF <- coeffsMR[4] + 1.5434 / abs(coeffsMR[2]) XbendMRlTEST <- coeffsMR[4] - coeffsMR[5] - 1.5434 / abs(coeffsMR[2]) XbendMRuTEST <- coeffsMR[4] - coeffsMR[5] + 1.5434 / abs(coeffsMR[2]) XasymlREF <- coeffsMR[4] - 3 / abs(coeffsMR[2]) XasymuREF <- coeffsMR[4] + 3 / abs(coeffsMR[2]) XasymlTEST <- coeffsMR[4] - coeffsMR[5] - 3 / abs(coeffsMR[2]) XasymuTEST <- coeffsMR[4] - coeffsMR[5] + 3 / abs(coeffsMR[2]) BPsMR_MU <- data.frame( points = c( "lower bendpoint REF", "upper bendpoint REF", "lower bendpoint TEST", "upper bendpoint TEST", "lower asymp. point REF", "upper asymp. point REF", "lower asymp. point TEST", "upper asymp. point TEST", "bendREF_lower_unrestr", "bendREF_upper_unrestr", "bendTESTE_lower_unrestr", "bendTEST_upper_unrestr" ), estimates = c( round(XbendMRlREF, 3), round(XbendMRuREF, 3), round(XbendMRlTEST, 3), round(XbendMRuTEST, 3), round(XasymlREF, 3), round(XasymuREF, 3), round(XasymlTEST, 3), round(XasymuTEST, 3), round(XbendMRlREF, 3), round(XbendMRuREF, 3), round(XbendMRlTEST, 3), round(XbendMRuTEST, 3) ) ) Dat$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$sigma^2 # VCOVpure <- V_V*meanPureErr SEsPure <- sqrt(diag(V_V) * meanPureErr) pot_est <- data.frame( estimate = exp(coeffsMR[5]), lowerCI = exp(coeffsMR[5] - qt(0.975, DFsPure) * SEsPure[5]), upperCI = exp(coeffsMR[5] + qt(0.975, DFsPure) * SEsPure[5]) ) # VCOVu <- vcov(mu) V_Vu <- Smu$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]) ) } 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 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$ANOVAXLS <- ANOVAtab2 FITsTrans <- Fitting_FUNC(XLdat2, TransFlag = TRUE) # SUlog <- FITsTrans[[2]] SRlog <- FITsTrans[[1]] RMSE_unrlog_diagn <- SUlog$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$DiagnTable <- DiagnTable logpotest <- FITsTrans[[3]] # exp(confintd(mrlog, "r", method = "asymptotic")) # compParm(logpot, "c") logpotUest <- FITsTrans[[4]] # exp(confintd(mulog, "r", method = "asymptotic")) # compParm(logpotu, "c") # Berechnung der Konfidenzintervalle (CI) # logpotCI <- c(exp(Smrlog[5,1] - qt(0.975, nrow(all_1)-5) * Smrlog[5,2]), exp(Smrlog[5,1]), exp(Smrlog[5,1] + qt(0.975, nrow(all_1)-5) * Smrlog[5,2])) colnames(logpotest) <- c("estimate", "lowerCI", "upperCI") colnames(logpotUest) <- c("estimate", "lowerCI", "upperCI") cnXL <- colnames(XLdat2) Filesample <- data.frame(Test = c("FILE NAME:", "SAMPLES"), Test2 = c(Dat$FileName, paste(cnXL[1], " vs ", cnXL[4]))) colnames(Filesample) <- c("", "") output$Filesampl <- renderTable( { Filesample }, rownames = FALSE ) UnRPLAausw <- data.frame( Information = c( "model", "lower asymptote Ref", "Hill's slope Ref", "upper asymptote Ref", "EC50 Ref", "lower asymptote Test", "Hill's slope Test", "upper asymptote Test", "EC50 Difference", "relative potency", "lower CI", "upper CI" ), Results = unlist(c("UNRESTRICTED", round(coeffsMU, 3), round(potU_est * 100, 3))) ) # von psl_nls # "log relative potency", "log lower CI", "log upper CI", round(logpotest, 3), round(compParm(potu, "c", display = FALSE), 3) output$coeffs_unr <- renderTable({ UnRPLAausw }) 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 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 = TRUE ) 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$LogUnrPLAausw <- LogUnrPLAAusw if (exists("Ind")) { Dat$dilution <- XLdat[, Ind] } else { Dat$dilution <- exp(XLdat[, logI]) } ##### Plot XL 4PL ---- output$XLplot <- renderPlot({ XLplot4pl <- plot_f(XLdat2, TransFlag = FALSE) 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({ 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({ 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))) 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 = FALSE) }) #### Plot 4pl Meta Transformed ---- output$plot4plTransMeta <- renderPlot({ PureErrFlag <- input$PureErrMeta warning_text3 <- reactive({ ifelse(PureErrFlag, "Pure error selected", "") }) output$PureErrWLogMeta <- renderText(warning_text3()) sigmoid <- sigmoid() det_sig <- NULL plot_f(sim2(), TransFlag = TRUE) }) #### Testergebnisse für 4PL Meta ---- observe({ if (is.null(sim2())) { return(NULL) } if (is.null(input$PureErrMeta)) { return(NULL) } # observeEvent(input$StartCalc,{ PureErrFlag <- input$PureErrMeta warning_text3 <- reactive({ ifelse(PureErrFlag, "Pure error selected", "") }) 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 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 = FALSE, 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")) } output$EQtests4pl <- renderDT({ dat }) }) # observe #### Testergebnisse 4PL für XLSX ---- observe({ if (is.null(Dat$EXCEL)) { return(NULL) } if (is.null(input$PureErr)) { return(NULL) } if (Dat$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 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$simdat <- DT::renderDataTable({ tab <- sim2() if (is.character(tab)) stop(tab) tab2 <- round(tab, 5) colnames(tab2) <- c( paste("T", seq(1, (ncol(tab2) - 1) / 2)), paste("R", seq(1, (ncol(tab2) - 1) / 2)), "log_conc" ) dat <- datatable(tab2, options = list( paging = TRUE, pageLength = 20, dom = "t" )) }) ##### Concentrationtab Meta ---- output$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$Conctab <- Conctab dat <- datatable(Conctab, options = list( paging = TRUE, pageLength = 12, dom = "t" )) |> formatStyle(0, target = "row", backgroundColor = styleEqual( c("avs", "sds", "cv", "avs_test", "sds_test", "cv_test"), c("lightgrey", "lightgreen", "pink", "lightgrey", "lightgreen", "pink") ) ) |> formatRound(columns = colnames(Conctab), digits = 3) }) output$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$Conctab <- Conctab dat <- datatable(Conctab, options = list( paging = TRUE, pageLength = 12, dom = "t" )) |> formatStyle(0, target = "row", backgroundColor = styleEqual( c("avs", "sds", "cv", "avs_test", "sds_test", "cv_test"), c("lightgrey", "lightgreen", "pink", "lightgrey", "lightgreen", "pink") ) ) |> formatRound(columns = colnames(Conctab), digits = 3) }) #### process XL linearly, Plot output ---- output$plotLin <- renderPlot({ if (is.null(Dat$EXCEL)) { return(NULL) } tab <- Dat$EXCEL # tab <- sim2() if (is.character(tab)) stop(tab) 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$circles <- circle if (Dat$FITsFlag) sigmoid <- NULL else sigmoid <- Dat$coeffsMUnr pLin <- PlotLinPLA_FUNC(circle, sigmoid = sigmoid, all_l2, pl_df, indS, indT) REPlin$pLin <- pLin pLin }) #### process metadata, Plot output ---- output$plotLinMeta <- renderPlot({ tab <- sim2() if (is.null(tab)) { return(NULL) } if (is.character(tab)) stop(tab) if (!is.na(Dils()[4])) noDilSer <- Dils()[4] else noDilSer <- (ncol(tab) - 1) / 2 Conc <- CONC() log_conc <- log(Conc) Conctab <- perConcTab(tab, noDilSer) if (!is.na(Dils()[3])) noDil <- Dils()[3] else noDil <- length(Conc) slopeSt <- slopeTe <- matrix(NA, nrow = noDil - 2, ncol = 2) for (i in 1:(noDil - 2)) { avs <- Conctab[noDilSer + 1, ] threes <- data.frame(lnC = log(Conc[i:(i + 2)]), resp = avs[i:(i + 2)]) lm3St <- lm(resp ~ lnC, data = threes) slopeSt[i, ] <- lm3St$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() 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$PureErrW <- renderText(warning_text()) LIN <- ANOVAlintests(tab, circlesMeta, Limite, PureErrFlag = PureErrFlag) df <- LIN[[1]] su_modU <- LIN[[2]] su_mod2 <- LIN[[4]] output$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 = TRUE, dom = "t", rownames = FALSE ) ) |> 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), # Limits from linear PLA page as.numeric(input$lEACratioAdiff), as.numeric(input$uEACratioAdiff) ) noDil <- nrow(tab) noDilSer <- Dat$noDilSeriesXL Conctab <- perConcTab(tab, noDilSeries = noDilSer) slopeSt <- slopeTe <- matrix(NA, nrow = noDil - 2, ncol = 2) for (i in 1:(noDil - 2)) { avs <- Conctab[noDilSer + 1, ] threes <- data.frame(lnC = log(Conc[i:(i + 2)]), resp = avs[i:(i + 2)]) lm3St <- lm(resp ~ lnC, data = threes) slopeSt[i, ] <- lm3St$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$PureErrW3 <- renderText(warning_text()) LIN <- ANOVAlintests(tab, circle, Limite, PureErrFlag = PureErrFlag) df <- LIN[[1]] su_modU <- LIN[[2]] su_mod2 <- LIN[[4]] output$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$ANOVA <- df[, 4:length(df)] dat <- datatable(df2[, 1:3], options = list( paging = TRUE, dom = "t", rownames = FALSE ) ) |> formatStyle("test_results", target = "row", backgroundColor = styleEqual( c(-1, 0, 1), c("pink", "lightgreen", "lightgrey") )) output$TESTSlin <- DT::renderDataTable({ dat }) }) #### output 4PL ANOVA tests Meta ---- output$ANOVA <- DT::renderDataTable({ sigmoid <- sigmoid() tab <- ANOVA4plUnresfunc(sim2()) # ,sigmoid dat <- datatable(tab, options = list( dom = "t", rownames = FALSE ) ) |> formatStyle("p_value", target = "row", backgroundColor = styleEqual( c("p_value"), c("lightgrey") ) ) }) #### output 4PL ANOVA tests XL ---- # not needed # output$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 = FALSE ) ) |> 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 = FALSE ) ) |> 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 = FALSE ) ) |> 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$upperPot) ) circles <- Dat$circlesMeta PureErrFlag <- input$PureErrMeta pottab <- LinPotTab(circles, Lim, PureErrFlag = PureErrFlag) dat <- datatable(pottab, options = list( dom = "t", rownames = FALSE ) ) |> formatStyle("test_result", target = "row", backgroundColor = styleEqual(c(0, 1), c("#B5C74055", "#F9545488")) ) }) #### 4pl potency table Meta ---- observe({ if (is.null(sim2()) | is.null(Dils())) { return(NULL) } ro_new <- sim2() Dils_ <- Dils() if (!is.na(Dils()[4])) noDilSer <- Dils()[4] else noDilSer <- 3 PureErrFl <- input$PureErrMeta pottab4 <- pot4plFUNC(ro_new = ro_new, PureErrFlag = PureErrFl) 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 = TRUE, dom = "t", rownames = FALSE ) ) |> formatStyle("test_result", target = "row", backgroundColor = styleEqual( c(0, 1), c("lightgreen", "pink") )) }) output$pottab4plTrans <- DT::renderDataTable({ dat <- datatable(pottab4_[3:4, ], options = list( digits = 3, paging = TRUE, dom = "t", rownames = FALSE ) ) |> formatStyle("test_result", target = "row", backgroundColor = styleEqual( c(0, 1), c("lightgreen", "pink") )) }) }) #### 4pl potency table XL ---- observe({ if (is.null(Dat$EXCEL)) { return(NULL) } if (Dat$FITsFlag) { return(NULL) } ro_new <- Dat$EXCEL noDilSer <- Dat$noDilSeriesXL PureErrFl <- input$PureErr pottab4 <- pot4plFUNC(ro_new = ro_new, PureErrFlag = PureErrFl) 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 <- "passed" } else { test_potCI <- "failed" } if (as.numeric(pottab4_$relative_lowerCL[2]) > Lim[[9]] & as.numeric(pottab4_$relative_upperCL[2]) < Lim[[10]]) { test_potUCI <- "" } else { test_potUCI <- "" } if (as.numeric(pottab4_$relative_lowerCL[3]) > Lim[[9]] & as.numeric(pottab4_$relative_upperCL[3]) < Lim[[10]]) { test_potCI_t <- "" } else { test_potCI_t <- "" } if (as.numeric(pottab4_$relative_lowerCL[4]) > Lim[[9]] & as.numeric(pottab4_$relative_upperCL[4]) < Lim[[10]]) { test_potUCI_t <- "" } else { test_potUCI_t <- "" } 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$pottab4plXL <- pottab4_[1:2, ] # browser() output$pottab4plXL <- DT::renderDataTable({ dat <- datatable(pottab4_[1:2, ], rownames = FALSE, options = list( digits = 3, paging = TRUE, dom = "t" ) ) |> formatStyle("test_result", target = "row", backgroundColor = styleEqual( c("passed", "failed"), c("#B5C74055", "#F9545455") )) }) output$pottab4plTransXL <- DT::renderDataTable({ dat <- datatable(pottab4_[3:4, -ncol(pottab4_)], rownames = FALSE, options = list( digits = 3, paging = TRUE, dom = "t" ) ) }) }) #### Dilutions Simulator ---- observe({ if (!is.null(Dat$Mws)) { AllXL <- Dat$Mws AllSheets <- Dat$Msheets URMcoefsL <- RMcoefsL <- potEstL <- list() for (N_WS in 1:length(AllXL)) { datWS <- as.data.frame(AllXL[[N_WS]]) cn <- colnames(datWS) logI <- grep("log|ln", cn) logDoseI <- grep("log_dose", cn) if (length(logI) > 0 & length(logDoseI) == 0) { datWS$log_dose <- datWS[, logI] datWS2 <- datWS[, -logI] CORro <- cor(datWS$log_dose, datWS[, 3]) } else if (length(logI) == 0 & length(logDoseI) == 0) { Ind <- grep(".ilution|.ose|.onc", cn) datWS$log_dose <- log(datWS[, Ind]) CORro <- cor(datWS[, Ind], datWS[, 3]) datWS2 <- datWS[, -Ind] } else if (length(logI) > 0 & length(logDoseI) > 0) { datWS2 <- datWS CORro <- cor(datWS[, logI], datWS[, 3]) } Dat$datWS2 <- datWS2 FITs <- Fitting_FUNC(datWS2, TransFlag = F) pot_est <- FITs[[3]] potEstL[[N_WS]] <- pot_est potU_est <- FITs[[4]] # unrestricted SU_mu <- FITs[[2]] URMcoefs1 <- SU_mu$coefficients URMcoefs <- t(matrix(unlist(URMcoefs1[,1]))) URMcoefs_ <- cbind(AllSheets[[N_WS]], URMcoefs) URMcoefsL[[N_WS]] <- URMcoefs_ SU_mr <- FITs[[1]] RMcoefs1 <- SU_mr$coefficients RMcoefs <- t(matrix(unlist(RMcoefs1[,1]))) RMcoefs_ <- cbind(AllSheets[[N_WS]], RMcoefs) RMcoefsL[[N_WS]] <- RMcoefs_ X <- seq(min(datWS2$log_dose), max(datWS2$log_dose), 0.1) sigRef <- URMcoefs[1,1] + (URMcoefs[1,3]-URMcoefs[1,1])/(1+exp(URMcoefs[1,2]*(URMcoefs[1,4]-X))) sigTest1 <- URMcoefs[1,5] + (URMcoefs[1,7]-URMcoefs[1,5])/(1+exp(URMcoefs[1,6]*(URMcoefs[1,4] - URMcoefs[1,8]-X))) #browser() dfPlotsigRef <- data.frame(X=X, sigRef = sigRef, Sheet = AllSheets[[N_WS]]) dfPlotsigTest <- data.frame(X=X, sigTest = sigTest1, Sheet = AllSheets[[N_WS]]) if (!exists("SIGrefDF")) SIGrefDF <- dfPlotsigRef else SIGrefDF <- rbind(SIGrefDF, dfPlotsigRef) if (!exists("SIGtestDF")) SIGtestDF <- dfPlotsigTest else SIGtestDF <- rbind(SIGtestDF,dfPlotsigTest) } #for N_WS #browser() URMcoefsDF <- t(matrix(unlist(URMcoefsL),nrow=9)) EC50TEST <- as.numeric(URMcoefsDF[,5]) - as.numeric(URMcoefsDF[,9]) # EC50TEST <- EC50TEST[!EC50TEST %in% boxplot.stats(EC50TEST)$out] EC50REF <- as.numeric(URMcoefsDF[,5]) # EC50REF <- EC50REF[!EC50REF %in% boxplot.stats(EC50REF)$out] UasREF <- as.numeric(URMcoefsDF[,4]) # UasREF <- UasREF[!UasREF %in% boxplot.stats(UasREF)$out] LasREF <- as.numeric(URMcoefsDF[,2]) # LasREF <- LasREF[!LasREF %in% boxplot.stats(LasREF)$out] UasTEST <- as.numeric(URMcoefsDF[,4]) LasTEST <- as.numeric(URMcoefsDF[,2]) RMcoefsDF <- t(matrix(unlist(RMcoefsL),nrow=6)) Dat$URMcoefsDF <- URMcoefsDF Dat$RestrM <- RMcoefsDF CalcPotDF <- t(matrix(unlist(potEstL),nrow=3)) Dat$CalcPot <- CalcPotDF # #### sigmoid plots ---- Slope <- as.numeric(URMcoefsDF[1,3]) if (Slope > 0) { x_UA <- max(X); x_LA <- min(X) } else { x_UA <- min(X); x_LA <- max(X) } #browser() BoxDF <- data.frame(EC50REF = EC50REF, EC50TEST = EC50TEST, LasREF = LasREF, UasREF = UasREF) p1 <- ggplot(SIGrefDF, aes(x=X, y=sigRef, col=as.factor(Sheet))) + geom_line() + annotate("text", label="x", x=x_UA, y=UasREF, alpha=0.2) + annotate("text", label="o", x=x_LA, y=LasREF, alpha=0.2) + geom_vline(xintercept = EC50REF, alpha = 0.2) + scale_x_continuous(expand = c(0, 0)) + scale_y_continuous(expand = c(0, 0)) + expand_limits(y = c(min(SIGrefDF$sigRef) - 0.1 * diff(range(SIGrefDF$sigRef)), max(SIGrefDF$sigRef) + 0.1 * diff(range(SIGrefDF$sigRef)))) + expand_limits(x = c(min(SIGrefDF$X) - 0.1 * diff(range(SIGrefDF$X)), max(SIGrefDF$X) + 0.1 * diff(range(SIGrefDF$X)))) + xlab("dilutions") + #ggtitle("Plot of all calculated reference fits (unrestricted model, in gray vertical lines: EC50)") + theme_bw() + theme(axis.text = element_text(face = "bold", size = 15), plot.title = element_text(size = 15, face = "bold"), plot.margin = unit(c(0.2, 0.2, 0.5, 0.5), "lines")) # Horizontal marginal boxplot - to appear at the top of the chart pBox_hor <- ggplot( BoxDF, aes(x = factor(1), y = EC50REF)) + geom_boxplot(outlier.colour = NA) + geom_jitter(position = position_jitter(width = 0.05)) + scale_y_continuous(expand = c(0, 0)) + expand_limits(y = c(min(SIGrefDF$X) - 0.1 * diff(range(SIGrefDF$X)), max(SIGrefDF$X) + 0.1 * diff(range(SIGrefDF$X)))) + coord_flip() + theme_bw() + theme(axis.text = element_blank(), axis.title = element_blank(), axis.ticks = element_blank(), plot.margin = unit(c(1, 0.2, -0.5, 0.5), "lines")) # Vertical marginal boxplot - to appear at the right of the chart pBox_ver <- ggplot(BoxDF, aes(x = factor(1), y = UasREF)) + geom_boxplot(outlier.colour = NA) + geom_jitter(position = position_jitter(width = 0.05)) + scale_y_continuous(expand = c(0, 0)) + expand_limits(y = c(min(SIGrefDF$sigRef) - 0.1 * diff(range(SIGrefDF$sigRef)), max(SIGrefDF$sigRef) + 0.1 * diff(range(SIGrefDF$sigRef)))) + theme_bw() + theme(axis.text = element_blank(), axis.title = element_blank(), axis.ticks = element_blank(), plot.margin = unit(c(0.2, 1, 0.5, -0.5), "lines")) #browser() gt1 <- ggplot_gtable(ggplot_build(p1)) gt2 <- ggplot_gtable(ggplot_build(pBox_hor)) gt3 <- ggplot_gtable(ggplot_build(pBox_ver)) # Get maximum widths and heights maxWidth <- unit.pmax(gt1$widths[2:3], gt2$widths[2:3]) maxHeight <- unit.pmax(gt1$heights[4:5], gt3$heights[4:5]) # Set the maximums in the gtables for gt1, gt2 and gt3 gt1$widths[2:3] <- as.list(maxWidth) gt2$widths[2:3] <- as.list(maxWidth) gt1$heights[4:5] <- as.list(maxHeight) gt3$heights[4:5] <- as.list(maxHeight) # Create a new gtable gt <- gtable(widths = unit(c(7, 1), "null"), height = unit(c(1, 7), "null")) # Instert gt1, gt2 and gt3 into the new gtable gt <- gtable_add_grob(gt, gt1, 2, 1) gt <- gtable_add_grob(gt, gt2, 1, 1) gt <- gtable_add_grob(gt, gt3, 2, 2) # grid.rect(x = 0.5, y = 0.5, height = 0.995, width = 0.995, default.units = "npc", # gp = gpar(col = "black", fill = NA, lwd = 1)) # And render the plot grid.newpage() #browser() output$sigPlotREF <- renderPlot({ grid.draw(gt) }) Dat$sigPlotREF <- p1 # p2 <- ggplot(SIGtestDF, aes(x=X, y=sigTest, col=as.factor(Sheet))) + geom_line() + annotate("text", label="x", x=x_UA, y=UasTEST, alpha=0.2) + annotate("text", label="o", x=x_LA, y=LasTEST, alpha=0.2) + geom_vline(xintercept = EC50TEST, alpha = 0.2) + xlab("dilutions") + ggtitle("Calculated test sample fits (unrestricted model, in gray vertical lines: EC50)") + theme_bw() + theme(axis.text = element_text(face = "bold", size = 15), plot.title = element_text(size = 15, face = "bold")) output$sigPlotTEST <- renderPlot({ p2 }) Dat$sigPlotTEST <- p2 #### histograms right panel ---- #browser() all_lPot <- data.frame(Cat_potency= c(rep("rel poteny",nrow(CalcPotDF)), rep("lower CI",nrow(CalcPotDF)),rep("upper CI",nrow(CalcPotDF))), Potency_and_CI = c(CalcPotDF[,1], CalcPotDF[,2],CalcPotDF[,3])) all_lPot[,2][all_lPot[,2] > 5] <- NA all_lPot[,2][all_lPot[,2] < 0.1] <- NA P_histCI <- ggplot(all_lPot, aes(x=Potency_and_CI, fill=Cat_potency)) + geom_histogram(color="#e9ecef", alpha=0.6, position = "identity") + scale_fill_manual(values=c("darkgreen","darkblue","salmon2","tomato3")) + ggtitle("Histogram of relative potencies, standard RMSEs") + scale_x_continuous( breaks=seq(trunc(min(all_lPot$Potency_and_CI, na.rm=T)*10)/10, max(all_lPot$Potency_and_CI, na.rm=T)*1.1, by=0.4), ) + theme_bw() + theme(axis.text = element_text(face="bold", size=15), axis.text.x = element_text(angle=90), plot.title= element_text(size=15, face="bold")) output$histCIs <- renderPlot({ P_histCI }) output$histEC50REF <- renderPlot({ hist(EC50REF, col="steelblue", border="white", main = 'Histogram of EC50REF') }) output$histLasREF <- renderPlot({ hist(LasREF, col="violet", border="white",main = 'Histogram of lower asymptotes REF') }) output$histUasREF <- renderPlot({ hist(UasREF, col="darkturquoise", border="white",main = 'Histogram of upper asymptotes REF') }) output$histEC50TEST <- renderPlot({ hist(EC50TEST, col="steelblue", border="white", main = 'Histogram of EC50TEST') }) output$histLasTEST <- renderPlot({ hist(LasTEST, col="violet", border="white",main = 'Histogram of lower asymptotes TEST') }) output$histUasTEST <- renderPlot({ hist(UasTEST, col="darkturquoise", border="white",main = 'Histogram of upper asymptotes TEST') }) Dat$histEC50REF <- hist(EC50REF, col="steelblue", border="white", main = 'Histogram of EC50REF') Dat$histLasREF <- hist(LasREF, col="violet", border="white", main = 'Histogram of EC50REF') Dat$histUasREF <- hist(UasREF, col="darkturquoise", border="white", main = 'Histogram of EC50REF') tab <- AllXL[[1]] dils <- tab$log_dose min_y <- min(tab[, 1:2]) max_y <- max(tab[, 1:2]) 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(EC50TEST)) { av <- mean(EC50TEST, na.rm = TRUE) } 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() # 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$coeffs_UN)) { # SAMPLE50 <- sigmoid[1] + (sigmoid[3] - sigmoid[1]) / (1 + exp(sigmoid[5] * ((sigmoid[7] + 0.693147) - dils2))) # SAMPLE200 <- sigmoid[1] + (sigmoid[3] - sigmoid[1]) / (1 + exp(sigmoid[5] * ((sigmoid[7] - 0.693147) - dils2))) # Xbend50l <- sigmoid[7] + 0.693147 - 1.5434 / sigmoid[5] # Xbend200l <- sigmoid[7] - 0.693147 - 1.5434 / sigmoid[5] # Xbend50u <- sigmoid[7] + 0.693147 + 1.5434 / sigmoid[5] # Xbend200u <- sigmoid[7] - 0.693147 + 1.5434 / sigmoid[5] # Xbend50 <- max(Xbend50l, Xbend50u) # Xbend200 <- min(Xbend200l, Xbend200u) # dummy <- plot_f(tab) # } else { # SAMPLE50 <- det_sig[3] + (det_sig[5] - det_sig[3]) / (1 + exp(det_sig[1] * (det_sig[7] + 0.693147 - dils2))) # SAMPLE200 <- det_sig[3] + (det_sig[5] - det_sig[3]) / (1 + exp(det_sig[1] * (det_sig[7] - 0.693147 - dils2))) # Xbend50l <- det_sig[7] + 0.693147 - 1.5434 / det_sig[1] # Xbend200l <- det_sig[7] - 0.693147 - 1.5434 / det_sig[1] # Xbend50u <- det_sig[7] + 0.693147 + 1.5434 / det_sig[1] # Xbend200u <- det_sig[7] - 0.693147 + 1.5434 / det_sig[1] # Xbend50 <- max(Xbend50l, Xbend50u) # Xbend200 <- min(Xbend200l, Xbend200u) # dummy <- plot_f(tab) # } # # # pl_df <- cbind(dils2, SAMPLE50, SAMPLE200) # output$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(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$scenario == "scenario 6") { # annotate("text", # x = optdils2, y = rep(min_y + (max_y - min_y) * 0.25, length(optdils2)), # label = as.character(round(optdils2, 3)), color = "seagreen" # ) # } # } + # annotate("text", # x = optdils[1], y = (max_y + min_y) * 0.5, # label = paste("in green: optimal \n dilutions acc. to Whitepaper\n", input$scenario), color = "seagreen", # size = 14 / .pt, fontface = "bold" # ) output$plotfordilutions <- renderPlot({ print(p_dil) }) } # if (!is.null(p2)) } # if !is.null Dat$Mws }) #### 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) 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$heterosked) { # heterosc noise ro_jit <- matrix(unlist(map(av, function(x) x + rnorm(1, 0, x * sd_fac_ / 100))), nrow = noDil, ncol = noDilSer * 2) } else { # homosc noise ro_jit <- matrix(unlist(map(av, function(x) x + rnorm(1, 0, sd_fac_ * yAxfac / 100))), nrow = noDil, ncol = noDilSer * 2) } ro_jit <- abs(ro_jit) ro_new <- cbind(ro_jit, log_dose) all_l <- melt(data.frame(ro_new), id.vars = "log_dose", variable.name = "replname", value.name = "readout") all_l$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 = FALSE) 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 = FALSE) 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 = FALSE) 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 resHistuAs <- as.data.frame(resHist[, 8:10]) resHistuAs_l <- melt(data.frame(resHistuAs), variable.name = "ratio_CIs", value.name = "readout") 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) 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---- observeEvent(input$btn, { if(!Dat$FITsFlag) { runjs("$('#downloadXLReport')[0].click();") } else { showModal(modalDialog( title = "No 4PL model to Download", "Please select other data before download.", easyClose = TRUE, footer = NULL )) } }) output$downloadXLReport <- downloadHandler( filename = paste0("Report_4PLEvaluation", Dat$RepIdentifier, ".pdf"), content = function(file) { tpdr <- tempdir() tempReport <- file.path(tpdr, "Doc_BioassayReport.Rmd") file.copy("Doc_BioassayReport.Rmd", tempReport, overwrite = T) tempReportc <- file.path(tpdr, "logov2.png") file.copy("logov2.png", tempReportc, overwrite = T) rmarkdown::render(tempReport, output_file = file, params = list( FileName = Dat$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 = TRUE) tempReportc <- file.path(tpdr, "logov2.png") file.copy("logov2.png", tempReportc, overwrite = TRUE) rmarkdown::render(tempReport, output_file = file, params = list( FileName = Dat$FileName, author = Dat$Author, NoP = Dat$NoP, Assay = Dat$Assay, REP = REP, REPlin = REPlin, coeffsLin = Dat$coeffs_UN ), envir = new.env(parent = globalenv()) ) } ) } shinyApp(ui, server)