Feedback received and applied

DESCRIPTION

 Package: drugsens
 Title: Automated Analysis of 'QuPath' Output Data and Metadata Extraction
-Description: A comprehensive toolkit for analyzing microscopy data output from
-    'QuPath' software. Provides functionality for automated data processing,
-    metadata extraction, and statistical analysis of imaging results.
-    Designed to complement the STAR Protocol Protocol for quantifying drug 
-    sensitivity in 3D patient-derived ovarian cancer models 
-    <doi:10.1016/j.xpro.2024.103274>.
+Description: A comprehensive toolkit for analyzing microscopy data output from 'QuPath' software. 
+    Provides functionality for automated data processing, metadata extraction, and statistical 
+    analysis of imaging results. The methodology implemented in this package is based on 
+    Labrosse et al. (2024) <doi:10.1016/j.xpro.2024.103274> "Protocol for quantifying drug 
+    sensitivity in 3D patient-derived ovarian cancer models", which describes the complete 
+    workflow for drug sensitivity analysis in patient-derived cancer models.
 Version: 0.1.0
 BugReports: https://git.scicore.unibas.ch/ovca-research/drugsens/-/issues
 SystemRequirements: QuPath™ 4.0.0 or higher

NAMESPACE

@@ -7,6 +7,7 @@ export(get_QC_plots)
 export(get_QC_plots_parsed_merged_data)
 export(make_count_dataframe)
 export(make_run_config)
+export(string_parsing)
 import(ggplot2)
 import(ggpubr)
 import(knitr)

R/get_QC_plots_and_stats.R

@@ -24,7 +24,7 @@
 #' @examples
 #' \dontrun{
 #'   qc <- get_QC_plots_parsed_merged_data(bind_data, save_plots = TRUE,
-#'                                        save_list_of_plots = TRUE)
+#'                                        save_list_of_plots = FALSE)
 #' }
 #' @export
 
@@ -64,8 +64,8 @@ get_QC_plots_parsed_merged_data <- function(.data,
       if (!is.null(isolate_specific_drug)) subset_data <- subset_data[subset_data[[drug_column_name]] %in% isolate_specific_drug, ]
 
       if (nrow(subset_data) < 1) {
-        print(unique(subset_data[[PID_column_name]]))
-        print(unique(subset_data[[drug_column_name]]))
+        message(unique(subset_data[[PID_column_name]]))
+        message(unique(subset_data[[drug_column_name]]))
         stop("ERROR: Your filtering query has returned no observations")
       }
 

R/parsers.R

@@ -8,11 +8,12 @@
 #' @importFrom stringr str_count
 #' @return A `dataframe`/`tibble`.
 #' @param .data dataframe with parsed metadata
+#' @export
 #' @examples
 #' input_data <- data.frame(
 #'   Image = "B516_Ascites_2023-11-25_DOC2020-12-14_dmso_rep_Ecad_cCasp3_(series 01).tif"
 #' )
-#' test <- drugsens:::string_parsing(input_data)
+#' test <- drugsens::string_parsing(input_data)
 #' \dontrun{
 #'   data.parsed <- string_parsing(.data)
 #' }

man/get_QC_plots_parsed_merged_data.Rd

@@ -56,6 +56,6 @@ This plot can show trends within the dataset and run some basic statistics.
 \examples{
 \dontrun{
   qc <- get_QC_plots_parsed_merged_data(bind_data, save_plots = TRUE,
-                                       save_list_of_plots = TRUE)
+                                       save_list_of_plots = FALSE)
 }
 }

man/string_parsing.Rd

@@ -20,7 +20,7 @@ The metadata will be then associated to the count file as well
 input_data <- data.frame(
   Image = "B516_Ascites_2023-11-25_DOC2020-12-14_dmso_rep_Ecad_cCasp3_(series 01).tif"
 )
-test <- drugsens:::string_parsing(input_data)
+test <- drugsens::string_parsing(input_data)
 \dontrun{
   data.parsed <- string_parsing(.data)
 }

tests/testthat/test_cases.R

@@ -82,7 +82,7 @@ test_that("The parsing is working", {
                                 ConcentrationUnits1 = "uM",
                                 ConcentrationUnits2 = "nm",
                                 Treatment_complete = "TreatmentRana10uM-15nm")
-  expect_equal(drugsens:::string_parsing(input_data), expected = expected_output)
+  expect_equal(drugsens::string_parsing(input_data), expected = expected_output)
 })
 
 test_that("Another parsing test", {
@@ -101,7 +101,7 @@ test_that("Another parsing test", {
     ConcentrationUnits1 = NA_character_,
     ConcentrationUnits2 = NA_character_,
     Treatment_complete = "dmso")
-  expect_equal(drugsens:::string_parsing(input_data), expected = expected_output)
+  expect_equal(drugsens::string_parsing(input_data), expected = expected_output)
   #   Image1 <- "B516_Ascites_2023-11-25_DOC2020-12-14_CarboplatinPaclitaxel_100_uM_10_nM_Ecad_cCasp3_(series 01).tif"
   #   Image2 <- "A8759_Spleen_2020.11.10_DOC2001.10.05_compoundX34542_1000_uM_EpCAM_Ecad_cCasp3_(series 01).tif"
   #   Image3 <- "A8759_Spleen_2020.11.10_DOC2001.10.05_compoundX34542_1000_uM_EpCAM_Ecad_cCasp3_(series 01).tif"