Fix turnover

DESCRIPTION

@@ -28,7 +28,6 @@ License: Artistic-2.0
 Encoding: UTF-8
 Depends: R (>= 4.5.0)
 LazyData: false
-RoxygenNote: 7.3.3
 Imports:
     BiocParallel,
     ggplot2,
@@ -54,3 +53,4 @@ Suggests:
 VignetteBuilder: knitr
 Roxygen: list(markdown = TRUE)
 biocViews: Proteomics, MassSpectrometry, StatisticalMethod, Software, Regression
+Config/roxygen2/version: 8.0.0

NAMESPACE

@@ -1,8 +1,11 @@
 # Generated by roxygen2: do not edit by hand
 
 export(MSstatsPrepareDoseResponseFit)
+export(calculateConfidence)
 export(calculatePeptideWeights)
+export(calculateQCScore)
 export(calculateTurnoverRatios)
+export(classifyTurnoverProteins)
 export(convertGroupToNumericDose)
 export(doseResponseFit)
 export(futureExperimentSimulation)
@@ -17,12 +20,18 @@ importFrom(BiocParallel,bpparam)
 importFrom(data.table,rbindlist)
 importFrom(dplyr,across)
 importFrom(dplyr,all_of)
+importFrom(dplyr,any_of)
 importFrom(dplyr,arrange)
+importFrom(dplyr,case_when)
+importFrom(dplyr,coalesce)
+importFrom(dplyr,dense_rank)
 importFrom(dplyr,distinct)
 importFrom(dplyr,filter)
 importFrom(dplyr,group_by)
 importFrom(dplyr,if_else)
+importFrom(dplyr,left_join)
 importFrom(dplyr,mutate)
+importFrom(dplyr,n_distinct)
 importFrom(dplyr,pull)
 importFrom(dplyr,rename)
 importFrom(dplyr,select)
@@ -59,7 +68,9 @@ importFrom(plotly,ggplotly)
 importFrom(plotly,layout)
 importFrom(stats,approx)
 importFrom(stats,cor)
+importFrom(stats,median)
 importFrom(stats,p.adjust)
+importFrom(stats,pbinom)
 importFrom(stats,pf)
 importFrom(stats,quantile)
 importFrom(stats,rlnorm)

R/ExperimentalDesignSimulation.R

@@ -494,8 +494,7 @@ simulateChemoProteinLevelNonParametric = function(N_proteins = 3000,
 #' @param concentration_count Number of concentrations in simulation
 #'
 #' @return A list containing the plot and plot data
-#' @importFrom ggplot2 ggplot aes geom_bar geom_text labs scale_fill_manual
-#'   scale_y_continuous theme_classic theme element_text
+#' @importFrom ggplot2 ggplot aes geom_bar geom_text labs scale_fill_manual scale_y_continuous theme_classic theme element_text
 #' @import dplyr
 plotHitRateMSstatsResponse = function(results, rep_count, concentration_count) {
 

R/TPR_Power_Curve.R

@@ -217,8 +217,7 @@ run_tpr_simulation <- function(rep_range, concentrations, dose_range,
 #' @param show_legend Logical. Whether to display the legend.
 #'
 #' @return A ggplot object.
-#' @importFrom ggplot2 ggplot aes geom_line geom_point scale_x_continuous
-#'   scale_y_continuous scale_color_manual labs theme_bw theme element_text
+#' @importFrom ggplot2 ggplot aes geom_line geom_point scale_x_continuous scale_y_continuous scale_color_manual labs theme_bw theme element_text
 #' @noRd
 .make_tpr_panel <- function(data, k_grid, show_legend = FALSE) {
   rep_levels <- sort(unique(data$N_rep))
@@ -288,8 +287,7 @@ run_tpr_simulation <- function(rep_range, concentrations, dose_range,
 #' plot_tpr_power_curve(results)
 #' }
 #'
-#' @importFrom ggplot2 ggplot aes geom_line geom_point scale_x_continuous
-#'   scale_y_continuous scale_color_manual labs theme_bw theme element_text
+#' @importFrom ggplot2 ggplot aes geom_line geom_point scale_x_continuous scale_y_continuous scale_color_manual labs theme_bw theme element_text
 #' @importFrom plotly ggplotly layout
 #' @export
 plot_tpr_power_curve <- function(simulation_results, static = FALSE) {

R/protein_turnover_ratio_helper.R

@@ -187,6 +187,30 @@ parse_timepoint <- function(time_strings) {
   return(hours)
 }
 
+#' Kendall monotonicity score, robust to sparse or all-missing data
+#'
+#' Computes `max(0, Kendall's tau)` between time and response, treating a group
+#' with fewer than two finite (time, response) pairs as non-monotonic (score 0)
+#' instead of letting `cor(use = "complete.obs")` error on zero complete pairs.
+#' A zero-variance group (>= 2 points but constant) yields `NA` from `cor()`,
+#' which is also mapped to 0.
+#'
+#' @param time Numeric vector of timepoints.
+#' @param response Numeric vector of responses (same length as `time`).
+#'
+#' @return A single numeric monotonicity score in \[0, 1\].
+#'
+#' @keywords internal
+#' @importFrom stats cor
+kendall_monotonicity <- function(time, response) {
+  ok <- is.finite(time) & is.finite(response)
+  if (sum(ok) < 2) {
+    return(0)
+  }
+  score <- suppressWarnings(cor(time[ok], response[ok], method = "kendall"))
+  if (is.na(score)) 0 else max(0, score)
+}
+
 
 #' Calculate quality-based weights for peptide measurements
 #'
@@ -297,11 +321,8 @@ calculatePeptideWeights <- function(
     ) %>%
     group_by(across(all_of(c(protein_col, peptide_col)))) %>%
     mutate(
-      monotonicity_score = pmax(0,
-                                cor(.data[[time_col]], .data[[response_col]],
-                                    method = "kendall", use = "complete.obs")
-      ),
-      monotonicity_score = if_else(is.na(monotonicity_score), 0, monotonicity_score)
+      monotonicity_score = kendall_monotonicity(.data[[time_col]],
+                                                .data[[response_col]])
     ) %>%
     ungroup() %>%
     mutate(