fix(vignette): Turn off nM to M conversion in vignette

NAMESPACE

@@ -1,20 +1,22 @@
 # Generated by roxygen2: do not edit by hand
 
 export(MSstatsPrepareDoseResponseFit)
+export(calculatePeptideWeights)
 export(calculateTurnoverRatios)
 export(convertGroupToNumericDose)
 export(doseResponseFit)
 export(futureExperimentSimulation)
 export(plot_tpr_power_curve)
 export(predictIC50)
 export(predictIC50Parallel)
-export(selectTopNPeptides)
 export(run_tpr_simulation)
 export(visualizeResponseProtein)
 import(dplyr)
 importFrom(BiocParallel,bplapply)
 importFrom(BiocParallel,bpparam)
 importFrom(data.table,rbindlist)
+importFrom(dplyr,across)
+importFrom(dplyr,all_of)
 importFrom(dplyr,arrange)
 importFrom(dplyr,distinct)
 importFrom(dplyr,filter)
@@ -55,6 +57,7 @@ importFrom(parallel,stopCluster)
 importFrom(plotly,ggplotly)
 importFrom(plotly,layout)
 importFrom(stats,approx)
+importFrom(stats,cor)
 importFrom(stats,p.adjust)
 importFrom(stats,pf)
 importFrom(stats,quantile)

R/Visualize_Isotonic_Fit.R

@@ -65,6 +65,7 @@
 #' print(plot2)
 #'
 #' # Example 3: Color points by replicate
+#' \dontrun{
 #' plot3 <- visualizeResponseProtein(
 #'   data = prepared_data,
 #'   protein_name = "PROTEIN_A",
@@ -74,6 +75,7 @@
 #'   color_by = "replicate"
 #' )
 #' print(plot3)
+#' }
 #'
 #' @export
 visualizeResponseProtein = function(data,

R/protein_turnover_ratio_helper.R

@@ -21,6 +21,7 @@
 #' @return Data frame with columns: Protein, BaseSequence, TimeVal, Run, Heavy, Light, Total, H_frac, L_frac
 #'
 #' @examples
+#' \dontrun{
 #' # Basic usage - all proteins, all peptides
 #' ratios <- calculateTurnoverRatios(
 #'   feature_data = quant_data$FeatureLevelData
@@ -59,6 +60,7 @@
 #'
 #' # Filter for specific protein after calculation
 #' protein_ratios <- ratios %>% filter(Protein == "A0A2K5TXF6")
+#' }
 #'
 #' @export
 #' @importFrom dplyr filter mutate group_by summarise arrange slice_head pull ungroup select rename
@@ -163,7 +165,7 @@ calculateTurnoverRatios <- function(
 #' @return Numeric vector of hours
 #'
 #' @examples
-#' parse_timepoint(c("0hr", "1hr", "12hrs", "168hrs"))
+#' MSstatsResponse:::parse_timepoint(c("0hr", "1hr", "12hrs", "168hrs"))
 #' # Returns: 0 1 12 168
 #'
 #' @keywords internal
@@ -205,6 +207,7 @@ parse_timepoint <- function(time_strings) {
 #'   - weight: Combined quality weight (product of all components)
 #'
 #' @examples
+#' \dontrun{
 #' # Calculate ratios first
 #' ratios <- calculateTurnoverRatios(feature_data)
 #'
@@ -226,6 +229,7 @@ parse_timepoint <- function(time_strings) {
 #'
 #' # Use stricter validity threshold
 #' ratios_weighted_strict <- calculatePeptideWeights(ratios, validity_threshold = 1.0)
+#' }
 #'
 #' @export
 #' @importFrom dplyr group_by mutate ungroup across all_of

tests/testthat/test-IC50_prediction.R

@@ -191,11 +191,12 @@ test_that(".calcSingleIC50 handles single protein-drug pair", {
     bootstrap = FALSE,
     prot = "TestProtein",
     drug_type = "TestDrug",
-    target_response = 0.5
+    target_response = 0.5,
+    precalculated_ratios = FALSE
   )
 
   expect_s3_class(result, "data.frame")
-  expect_named(result, c("protein", "drug", "IC50", "IC50_lower_bound", "IC50_upper_bound"))
+  expect_named(result, c("protein", "drug", "IC50", "IC50_lower_bound", "IC50_upper_bound", "direction"))
   expect_equal(result$protein, "TestProtein")
   expect_equal(result$drug, "TestDrug")
 })
@@ -217,7 +218,8 @@ test_that(".calcSingleIC50 handles bootstrap option", {
     bootstrap = FALSE,
     prot = "P1",
     drug_type = "D1",
-    target_response = 0.5
+    target_response = 0.5,
+    precalculated_ratios = FALSE
   )
 
   expect_true(is.na(result_no_boot$IC50_lower_bound))
@@ -234,7 +236,8 @@ test_that(".calcSingleIC50 handles bootstrap option", {
     bootstrap = TRUE,
     prot = "P1",
     drug_type = "D1",
-    target_response = 0.5
+    target_response = 0.5,
+    precalculated_ratios = FALSE
   )
 
   expect_false(is.na(result_boot$IC50_lower_bound))
@@ -257,7 +260,8 @@ test_that(".calcSingleIC50 handles ratio vs log scale", {
     bootstrap = FALSE,
     prot = "P1",
     drug_type = "D1",
-    target_response = 0.5
+    target_response = 0.5,
+    precalculated_ratios = FALSE
   )
 
   result_log <- .calcSingleIC50(
@@ -270,7 +274,8 @@ test_that(".calcSingleIC50 handles ratio vs log scale", {
     bootstrap = FALSE,
     prot = "P1",
     drug_type = "D1",
-    target_response = 0.5
+    target_response = 0.5,
+    precalculated_ratios = FALSE
   )
 
   # Results should be different
@@ -295,7 +300,8 @@ test_that(".calcSingleIC50 handles failed fits", {
     bootstrap = FALSE,
     prot = "P1",
     drug_type = "D1",
-    target_response = 0.5
+    target_response = 0.5,
+    precalculated_ratios = FALSE
   )
   })
 

tests/testthat/test-PredictIC50.R

@@ -34,7 +34,7 @@ test_that("predictIC50 returns correct structure", {
   })
 
   expect_s3_class(results, "data.frame")
-  expect_named(results, c("protein", "drug", "IC50", "IC50_lower_bound", "IC50_upper_bound"))
+  expect_named(results, c("protein", "drug", "IC50", "IC50_lower_bound", "IC50_upper_bound", "direction"))
   expect_equal(nrow(results), 3)  # 3 proteins
 })
 

vignettes/MSstatsResponse.Rmd

@@ -162,7 +162,7 @@ dia_prepared <- MSstatsPrepareDoseResponseFit(
   drug_column = "drug",
   protein_column = "Protein",
   log_abundance_column = "LogIntensities",
-  transform_nM_to_M = TRUE  
+  transform_nM_to_M = FALSE  
 )
 
 # Examine prepared data structure