Allow for the filtering of aggregation plots.

R/Contrasts.R

@@ -140,7 +140,9 @@ Contrasts <- R6::R6Class(
       }else{
 
         res <- vector(mode = "list", nrow(self$models))
+        pb <- progress::progress_bar$new(total = length(self$models$linear_model))
         for (i in seq_along(self$models$linear_model)) {
+          pb$tick()
           res[[i]] <- .linfct(self$models$linear_model[[i]], contrast = self$contrasts, avg = avg)
         }
         return(res)

R/LFQDataAggregator.R

@@ -123,31 +123,39 @@ LFQDataAggregator <- R6::R6Class(
       private$.topN(N = N, .func = sum_f)
     },
     #' @description
-    #' creates aggreation plots
+    #' creates aggregation plots
+    #' @param subset create plots for a subset of the data only, e.g. proteins with more then 2 peptides.
     #' @param show.legend default FALSE
     #' @return data.frame
     #'
-    plot = function(show.legend = FALSE){
+    plot = function(subset = NULL, show.legend = FALSE){
       if (is.null(self$lfq_agg)) {
         stop("please aggregate the data first")
       }
+
+      if (!is.null(subset)) {
+        lfqagg <- self$lfq_agg$get_subset(subset)
+      }else {
+        lfqagg <- self$lfq_agg
+      }
       df <- prolfqua::plot_estimate(
         self$lfq$data,
         self$lfq$config,
-        self$lfq_agg$data,
-        self$lfq_agg$config,
+        lfqagg$data,
+        lfqagg$config,
         show.legend = show.legend)
       invisible(df)
     },
     #' @description
     #' writes plots to folder
+    #'
     #' @param qcpath qcpath
     #' @param show.legend legend
     #' @param width figure width
     #' @param height figure height
     #' @return file path
-    write_plots = function(qcpath, show.legend = FALSE, width = 6, height = 6){
-      pl <- self$plot()
+    write_plots = function(qcpath, subset = NULL, show.legend = FALSE, width = 6, height = 6){
+      pl <- self$plot(subset)
       pb <- progress::progress_bar$new(total = nrow(pl))
       filepath <- file.path(qcpath, paste0(self$prefix, "_aggregation_plot.pdf"))
       pdf(filepath , width = width, height = height)

R/tidyMS_R6Model.R

@@ -130,6 +130,10 @@ build_model <- function(data,
                     subject_Id = subject_Id))
 }
 
+
+
+
+
 #' Summarize modelling and error reporting
 #' @param mod model table see \code{\link{build_model}}
 #' @keywords internal

R/tidyMS_R6_Modelling.R

@@ -292,10 +292,11 @@ get_complete_model_fit <- function(modelProteinF){
 #'  subject_Id = x$config$table$hierarchy_keys_depth())
 #' stopifnot(nrow(get_complete_model_fit(mr$modelProtein)) == 6)
 #'
-model_analyse <- function(pepIntensity,
-                          model_strategy,
-                          subject_Id = "protein_Id",
-                          modelName = "Model")
+model_analyse <- function(
+    pepIntensity,
+    model_strategy,
+    subject_Id = "protein_Id",
+    modelName = "Model")
 {
   pepIntensity |>
     dplyr::group_by(!!!syms(subject_Id)) |>

R/tidyMS_aggregation.R

@@ -639,7 +639,11 @@ estimate_intensity <- function(data, config, .func)
 #' stopifnot("ggplot" %in% class(tmpRob$plots[[2]]))
 #'
 #'
-plot_estimate <- function(data, config, data_aggr, config_reduced, show.legend= FALSE ){
+plot_estimate <- function(data,
+                          config,
+                          data_aggr,
+                          config_reduced,
+                          show.legend = FALSE ){
   hierarchy_ID <- "hierarchy_ID"
   xnested <- data |> group_by(!!!syms(config$table$hierarchy_keys_depth())) |> nest()
   xnested <- xnested |> tidyr::unite(hierarchy_ID , !!!syms(config$table$hierarchy_keys_depth()))