- clean up lasso experiment remnants

- sync package loading with req.txt file
CeMOS-Mannheim · May 22, 2024 · a6ed1dc · a6ed1dc
1 parent 2eecbce
commit a6ed1dc
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Showing 5 changed files with 3 additions and 134 deletions.
diff --git a/components/mainpanel.R b/components/mainpanel.R
@@ -1,7 +1,6 @@
 source("components/mainTab.R")
 source("components/qcTab.R")
 source("components/pcaTab.R")
-source("components/lassoTab.R")
 source("components/hcTab.R")
 
 appMainPanel <- function(defaults) {

diff --git a/components/qcTab.R b/components/qcTab.R
@@ -18,7 +18,6 @@ qcTab <- function() {
                                             "Selected-mz",
                                             "PC-x",
                                             "PC-y",
-                                            "LASSO-error",
                                             "Outlier-mz",
                                             "Outlier-all"),
                                 multiple = FALSE,

diff --git a/components/server.R b/components/server.R
@@ -12,8 +12,6 @@ server <- function(input, output, session) {
   appData <<- emptyAppDataObject()
   vol <- getVolumes()
 
-  #defaults <- defaultsSettingsHandler(userSavedSettings = "settings.csv")
-
   # check if "dir" is set in defaults
   if (!is.null(defaults$dir)) {
     appData$selected_dir <- defaults$dir
@@ -434,97 +432,10 @@ server <- function(input, output, session) {
     }
   })
 
-  #### LASSO tab #####
-  # inital dummy text output
-  output$mixture <- renderText({
-    paste("Mixture = 1")
-  })
-  output$glmTruePred <- renderPlotly({
-    # dummy plot
-    p <- dummyPlot("Fit model\nto display plot")
-
-    return(ggplotly(p))
-  })
-
-  output$glmVi <- renderPlotly({
-    # dummy plot
-    p <- dummyPlot("Fit model\nto display plot")
-    return(ggplotly(p))
-  })
-
-  observeEvent(input$doGLM, {
-    if (appData$info_state == "processed") {
-
-      message("starting model fit...\n")
-      show_spinner()
-      appData$model <- fitGLM(appData$res,
-                              sigmoid = input$sigmoidModel,
-                              elasticNet = input$elasticNet,
-                              corFilter = input$corFilter)
-      updateSliderInput(inputId = "penalty",
-                        value = log10(appData$model$penalty))
-      if (input$elasticNet) {
-        output$mixture <- renderText({
-          paste("Mixture =", round(appData$model$mixture, 2))
-        })
-      } else {
-        output$mixture <- renderText({
-          paste("Mixture = 1")
-        })
-      }
-
-      message("model fitted.\n")
-      hide_spinner()
-
-      output$glmTruePred <- renderPlotly({
-        p <- glmRegPlot(model = appData$model, penalty = input$penalty)
-
-        return(ggplotly(p))
-      })
-
-      observeEvent(input$penalty, {
-        output$glmVi <- renderPlotly({
-
-          p <- viPlot(getVi(appData$model, input$penalty))
-
-          return(ggplotly(p))
-        })
-      })
-    }
-  })
-
-  observeEvent(input$resetPenalty, {
-    if (!is.null(appData$model)) {
-      updateSliderInput(inputId = "penalty",
-                        value = log10(appData$model$penalty))
-    } else {
-      message("appData$model was NULL\n")
-    }
-  })
-
-  observeEvent(input$lasso2peaksTable, {
-    if (!is.null(appData$model)) {
-
-      vi <- getVi(appData$model, penalty = input$penalty) %>%
-        perpareVi()
-
-      appData$stats <- appData$stats_original %>%
-        left_join(vi, by = join_by(mzIdx))
-
-      message("Updated peak table with lasso data.\n")
-    }
-  })
-
   #### HClust tab #####
   observeEvent(input$doHC, {
     if (appData$show_plot) {
       show_spinner()
-      # appData$hc <- doHClust(appData$res,
-      #                        cut = input$num_cluster,
-      #                        dist = input$hcDist,
-      #                        clustMethod = input$hcMethod,
-      #                        useFittedCurves = input$hcUseFitted)
-      # p <- plotDendro(appData$hc$dend)
 
       appData$hc <- clusterCurves(appData$res, nClusters = 15)
       hide_spinner()
@@ -541,15 +452,10 @@ server <- function(input, output, session) {
     }
   })
 
-
-
-
   output$clustCurvesPlot <- renderPlotly({
     if (appData$show_plot & !is.null(appData$hc)) {
       show_spinner()
-      # p <- plotClusterCurves(dend = appData$hc$dend,
-      #                        tintmat = appData$hc$tintmat,
-      #                        useFittedCurves = input$hcUseFitted)
+
       p <- plotTraj(appData$hc, k = input$num_cluster) +
         labs(y = "rel. Intensity [arb. u.]",
              x = "Log10 Concentration",
@@ -563,8 +469,6 @@ server <- function(input, output, session) {
   output$optNumClust <- renderPlotly({
     if (appData$show_plot & !is.null(appData$hc)) {
       show_spinner()
-      # p <- optimalNumClustersPlot(appData$hc$opt,
-      #                             sel_k = input$num_cluster)
       p <- plotClusterMetrics(appData$hc) +
       hide_spinner()
       return(ggplotly(p))
@@ -574,11 +478,6 @@ server <- function(input, output, session) {
 
   observeEvent(input$hc2peaksTable, {
     if (appData$show_plot & !is.null(appData$hc)) {
-      # clusters <- extractClusters(appData$hc$dend) %>%
-      #   mutate(mzIdx = match.closest(x = mz,
-      #                                table = getAllMz(appData$res),
-      #                                tolerance = 0.1)) %>%
-      #   select(-mz)
 
       clusters <- extractLaClusters(appData$hc, k = input$num_cluster)
       appData$stats <- appData$stats_original %>%

diff --git a/functions/checkInstalledPackages.R b/functions/checkInstalledPackages.R
@@ -1,16 +1,11 @@
-checkInstalledPackages <- function() {
+checkInstalledPackages <- function(req_file = "req.txt") {
   #### package check on start up ####
   # If a package is installed, it will be loaded. If any
   # are not, the missing package(s) will be installed
   # from CRAN and then loaded.
 
   ## First specify the packages of interest
-  packages = c("tidyverse", "shiny", "shinyFiles",
-               "MALDIquant", "MALDIquantForeign", "DT", "plotly",
-               "shinycssloaders", "shinyhelper", "knitr", "shinybusy",
-               "shinythemes", "shinyWidgets", "devtools",
-               "proxy", "sparsepca", "platetools", "zoo",
-               "fs", "cluster", "shinyjs", "latrend", "dtwclust")
+  packages = read.csv(req_file)
 
   ## Now load or install&load all
   package.check <- lapply(

diff --git a/functions/plotFunctions.R b/functions/plotFunctions.R
@@ -141,7 +141,6 @@ plateMapPlot <- function(appData,
                                   "Selected-mz",
                                   "PC-x",
                                   "PC-y",
-                                  "LASSO-error",
                                   "Outlier-mz",
                                   "Outlier-all"),
                          PCs,
@@ -263,28 +262,6 @@ plateMapPlot <- function(appData,
                spot = spots)
            }
          },
-         "LASSO-error" = {
-           if(!is.null(appData$model)) {
-             df <- appData$model$model %>%
-               finalize_model(tibble(penalty = 10^penalty)) %>%
-               fit(data = appData$model$prepData, formula = conc ~.) %>%
-               predict(appData$model$prepData) %>%
-               mutate(truth = pull(appData$model$prepData, conc)) %>%
-               mutate(val = abs(truth - .pred),
-                      spot = spots)
-
-             if(log10) {
-               lab <- "log10(Abs. error)"
-             } else {
-               lab <- "Abs. error"
-             }
-           } else {
-             lab <- "No LASSO data"
-             df <- tibble(
-               val = rep(NA_integer_, length(spots)),
-               spot = spots)
-           }
-         },
          "Outlier-mz" = {
            lab <- paste0("Chauvenet\nCriterion\n",
                          "m/z=", round(getMzFromMzIdx(res, mzIdx = mz_idx), digits = 2))