Improved documentation, fixed folder creation in wd and improved examples

DESCRIPTION

@@ -10,7 +10,7 @@ Version: 0.1.0
 BugReports: https://git.scicore.unibas.ch/ovca-research/drugsens/-/issues
 SystemRequirements: QuPath™ 4.0.0 or higher
 Authors@R: c(
-    person("Flavio", "Lombardo", , "flavio.lombardo@unibas.ch", role = c("aut", "cre", "cph")),
+    person("Flavio", "Lombardo", , "flavio.lombardo@unibas.ch", role = c("aut", "cre", "cph"), comment = c(ORCID = "0000-0002-4853-6838")),
     person("Ricardo Coelho", role = c("cph")),   
     person("Ovarian Cancer Research", role = c("cph")),   
     person("University of Basel and University Hospital Basel", role = c("cph"))   
@@ -19,16 +19,16 @@ URL: https://git.scicore.unibas.ch/ovca-research/drugsens/
 Maintainer: Flavio Lombardo <flavio.lombardo@unibas.ch>
 License: MIT + file LICENSE
 Imports:
-    utils,
     dplyr,
-    tidyr,
-    readr,
-    stringr,
-    knitr,
     ggplot2,
     ggpubr,
+    knitr,
     roxygen2,
+    stats,
+    stringr,
+    tidyr,
     tidyselect,
+    utils,
     testthat (>= 3.0.0)
 Depends: 
     R (>= 4.2)

NAMESPACE

@@ -15,7 +15,18 @@ import(roxygen2)
 import(testthat)
 importFrom(dplyr,filter)
 importFrom(dplyr,select)
-importFrom(readr,write_excel_csv)
+importFrom(ggplot2,aes)
+importFrom(ggplot2,aes_string)
+importFrom(ggplot2,element_text)
+importFrom(ggplot2,facet_wrap)
+importFrom(ggplot2,geom_boxplot)
+importFrom(ggplot2,geom_jitter)
+importFrom(ggplot2,geom_violin)
+importFrom(ggplot2,ggplot)
+importFrom(ggplot2,labs)
+importFrom(ggplot2,position_jitter)
+importFrom(ggplot2,stat_summary)
+importFrom(ggplot2,theme)
 importFrom(stats,setNames)
 importFrom(stringr,str_count)
 importFrom(stringr,str_extract)

R/generate_qu_path_script.R → R/generate_qupath_script.R

@@ -2,13 +2,24 @@
 #' @description
 #' Generate a useful script to consistently save the output data from QuPath in .csv format following the naming conventions
 #' followed during the package development.
-#' @return `script_for_qupath.txt` in local working directory.
+#' @param output_dir Directory where the script should be saved. If NULL, uses tempdir()
+#' @return Invisibly returns the path to the generated script file.
 #' @export
 #' @examples
 #' \dontrun{
+#'   # Generate script in a temporary directory
 #'   generate_qupath_script()
+#'
+#'   # Generate script in a specific directory
+#'   output_dir <- tempdir()
+#'   generate_qupath_script(output_dir = output_dir)
 #' }
-generate_qupath_script <- function() {
+generate_qupath_script <- function(output_dir = NULL) {
+  if(is.null(output_dir)) {
+    output_dir <- tempdir()
+  }
+  output_file <- file.path(output_dir, "script_for_qupath.txt")
+
   write(
     x = paste0('
 //This code script was tested with QuPath 4
@@ -53,9 +64,9 @@ def exporter  = new MeasurementExporter()
 
 print "Done!"
       '),
-    file = paste0(path.expand(getwd()), "/script_for_qupath.txt")
+    file = output_file
   )
   message("You can now take the script and personalize it to your needs")
-  message(paste0(Sys.time(), " The script file was generated here: ", getwd(), "/"))
+  message(paste0(Sys.time(), " The script file was generated here: ", output_file, "/"))
   message(paste0(Sys.time(), " Please make sure to follow the name convention here proposed, or it might fail to get all the information"))
 }

R/get_QC_plots.R

 #' Plot some QC plots to define that everything ran correctly
 #' @description
-#' Plot data to visualize immediate trends
-#' @param .data The preprocessed data (after running make_count_dataframe() and change_data_format_to_longer()) merged data.frame that should be visualized
+#' Plot data to visualize immediate trends. This function expects data that has been processed
+#' through make_count_dataframe() and change_data_format_to_longer() to ensure the correct
+#' data structure for plotting.
+#' @param .data The preprocessed data (after running make_count_dataframe() and change_data_format_to_longer())
+#'              merged data.frame that should be visualized
 #' @param patient_column_name The PID's column name in the merged data.frame (defaults to "PID")
-#' @param colors  A list of colors to supply to personalize the plot, as default 4 colors c("dark green", "red", "orange", "pink")
-#' @param save_plots  A Boolean value indicating if the plots should be saved or not, TRUE for saving in the current working directory, FALSE to not. Default is FALSE
-#' @param folder_name A string indicating the name of the folder where to save the plots in case that save_plots = TRUE
+#' @param colors A list of colors to supply to personalize the plot, defaults to c("darkgreen", "red", "orange", "pink")
+#' @param save_plots A Boolean value indicating if the plots should be saved or not (default is FALSE)
+#' @param folder_name A string indicating the name of the folder where to save the plots if save_plots is TRUE
 #' @param isolate_a_specific_patient A string indicating the patient name to isolate for single plot case (default is NULL)
 #' @param x_plot_var A string indicating the treatment's full name for the QC plots (default is "Treatment_complete")
 #' @import ggplot2
 #' @import ggpubr
 #' @importFrom dplyr filter
-#' @return A `dataframe`/`tibble`.
+#' @return Invisibly returns NULL, but saves plots to disk if save_plots is TRUE
 #' @examples
 #' \dontrun{
-#'   get_QC_plots(longer_format_dataframe, patient_column_name = "PID",
-#'                save_plots = TRUE, folder_name = "figures")
+#' # First process example data
+#' example_path <- system.file("extdata/to_merge/", package = "drugsens")
+#' raw_data <- data_binding(path_to_the_projects_folder = example_path)
+#' count_data <- make_count_dataframe(raw_data)
+#' processed_data <- change_data_format_to_longer(count_data)
+#'
+#' # Create and save plots to temporary directory
+#' temp_dir <- file.path(tempdir(), "qc_plots")
+#' get_QC_plots(
+#'   processed_data,
+#'   save_plots = TRUE,
+#'   folder_name = temp_dir
+#' )
+#'
+#' # Create plots for a specific patient
+#' get_QC_plots(
+#'   processed_data,
+#'   isolate_a_specific_patient = "B39",
+#'   save_plots = TRUE,
+#'   folder_name = temp_dir
+#' )
 #' }
 #' @export
 get_QC_plots <- function(.data,
                          patient_column_name = "PID",
                          colors = c("darkgreen", "red", "orange", "pink"),
                          save_plots = FALSE,
-                         folder_name = "figures",
+                         folder_name = NULL,
                          x_plot_var = "Treatment_complete",
                          isolate_a_specific_patient = NULL) {
 
-  if (!is.null(isolate_a_specific_patient)) .data <- .data[.data[[patient_column_name]] == isolate_a_specific_patient, ]
-  if (nrow(.data) < 1) stop("The data cannot be empty")
+  # Input validation
+  if (!is.data.frame(.data)) {
+    stop("Input must be a data frame")
+  }
 
-  # run for every unique PID the QC plot
-  for (i in unique(.data[patient_column_name])) {
-    message(paste0("Running the QC plot function for PID: ", i))
+  # Check required columns exist
+  required_cols <- c(patient_column_name, x_plot_var, "marker_positivity", "marker_positivity_ratio")
+  missing_cols <- setdiff(required_cols, colnames(.data))
+  if (length(missing_cols) > 0) {
+    stop("Missing required columns: ", paste(missing_cols, collapse = ", "),
+         ". Please ensure data has been processed with make_count_dataframe() and change_data_format_to_longer()")
+  }
 
-    QC_plot <- .data |>
-      dplyr::filter(.data[[patient_column_name]] == i) |>
-      ggplot(aes(x = .data[[x_plot_var]],
-                 y = .data$marker_positivity_ratio,
-                 col = .data$marker_positivity)) +
-      geom_boxplot(
-        position = position_dodge(width = 1.0),
-      ) +
-      facet_wrap(~marker_positivity) +
-      geom_jitter(width = 0.15) +
-      theme_light() +
-      labs(title = paste0("Cell marker ratios for PID: ", i), color = "Cell marker") +
-      ylab("Percentage of expression marker (marker-positive-cells/total_cell_count)") +
-      xlab("Drugs") +
-      theme(axis.text.x = element_text(angle = 45, hjust = 1.0)) +
-      scale_color_manual(values = colors) +
-      stat_summary(
-        fun = "median", geom = "pointrange",
-        mapping = aes(xend = after_stat(x) - 0.25, yend = after_stat(y)),
-        size = 1.5, alpha = 1.0,
-        position = position_dodge(width = 1)
-      ) +
-      stat_summary(
-        geom = "line", fun = "median", position = position_dodge(width = 1),
-        size = 1, alpha = 0.3, aes(group = marker_positivity)
+  # Filter for specific patient if requested
+  if (!is.null(isolate_a_specific_patient)) {
+    .data <- .data[.data[[patient_column_name]] == isolate_a_specific_patient, ]
+    if (nrow(.data) < 1) {
+      stop("No data found for patient: ", isolate_a_specific_patient)
+    }
+  }
+
+  # Set up output directory if saving plots
+  if (save_plots) {
+    if (is.null(folder_name)) {
+      folder_name <- file.path(tempdir(), "figures")
+    }
+    dir.create(folder_name, showWarnings = FALSE, recursive = TRUE)
+  }
+
+  # Process each patient
+  for (current_pid in unique(.data[[patient_column_name]])) {
+    message("Processing patient: ", current_pid)
+
+    # Filter data for current patient
+    patient_data <- dplyr::filter(.data, .data[[patient_column_name]] == current_pid)
+
+    # Create the plot
+    QC_plot <- ggplot2::ggplot(patient_data,
+                               ggplot2::aes(x = .data[[x_plot_var]],
+                                            y = marker_positivity_ratio,
+                                            color = marker_positivity)) +
+      ggplot2::geom_boxplot(position = ggplot2::position_dodge(width = 1.0)) +
+      ggplot2::facet_wrap(~marker_positivity) +
+      ggplot2::geom_jitter(width = 0.15) +
+      ggplot2::theme_light() +
+      ggplot2::labs(
+        title = paste0("Cell marker ratios for PID: ", current_pid),
+        color = "Cell marker",
+        y = "Percentage of expression marker (marker-positive-cells/total_cell_count)",
+        x = "Drugs"
       ) +
-      theme(
-        axis.title.x = element_blank(),
-        plot.title = element_text(hjust = 0.5),
-        axis.ticks.x = element_blank(),
-        panel.grid = element_blank(),
-        strip.background = element_rect(
+      ggplot2::theme(
+        axis.text.x = ggplot2::element_text(angle = 45, hjust = 1.0),
+        axis.title.x = ggplot2::element_blank(),
+        plot.title = ggplot2::element_text(hjust = 0.5),
+        axis.ticks.x = ggplot2::element_blank(),
+        panel.grid = ggplot2::element_blank(),
+        strip.background = ggplot2::element_rect(
           colour = "black",
           fill = "grey1"
         )
+      ) +
+      ggplot2::scale_color_manual(values = colors) +
+      ggplot2::stat_summary(
+        fun = "median",
+        geom = "point",
+        size = 3,
+        position = ggplot2::position_dodge(width = 1)
+      ) +
+      ggplot2::stat_summary(
+        geom = "line",
+        fun = "median",
+        position = ggplot2::position_dodge(width = 1),
+        linewidth = 1,
+        alpha = 0.3,
+        aes(group = marker_positivity)
       )
 
+    # Save the plot if requested
     if (save_plots) {
-      if (!dir.exists(paths = paste0(getwd(), "/", folder_name, "/"))) dir.create(path = paste0(getwd(), "/", folder_name, "/"), showWarnings = F, recursive = T)
+      plot_filename <- file.path(
+        folder_name,
+        paste0("patients_QC_box_plots_",
+               current_pid,
+               "_median",
+               format(Sys.Date(), "%Y-%m-%d"),
+               ".pdf"
+        )
+      )
 
-      ggsave(QC_plot,
-        filename = paste0(folder_name, "/", "patients_QC_box_plots_", i, "_", "median", Sys.Date(), ".pdf"),
+      ggplot2::ggsave(
+        filename = plot_filename,
+        plot = QC_plot,
         device = "pdf",
         height = 12,
         width = 12
       )
     }
   }
-  message(paste0("If save_plots = TRUE, the plots will be saved here:", paste0(folder_name, "/", "patients_QC_box_plots_", "median", Sys.Date(), ".pdf")))
+
+  # Final message about plot locations
+  if (save_plots) {
+    message("Plots have been saved in: ", folder_name)
+  }
+
+  invisible(NULL)
 }

R/get_QC_plots_and_stats.R

-#' Plot some QC plots for the bound data
+#' Plot QC plots and calculate statistics for bound data
 #' @description
-#' This plot can show trends within the dataset and run some basic statistics.
-#'
-#' @param .data The preprocessed data (after running make_count_dataframe() and change_data_format_to_longer()) merged data.frame that should be visualized
-#' @param list_of_columns_to_plot The preprocessed data (after running make_count_dataframe() and change_data_format_to_longer()) merged data.frame that should be visualized
-#' @param save_plots Boolean, TRUE if plots should be saved (default is FALSE)
-#' @param saving_plots_folder String indicating the folder where the plots should be stored (default is "figures")
-#' @param PID_column_name String, indicating the name of the sample to subset (default is "PID")
-#' @param isolate_specific_drug String, indicating if there should be a Treatment specific data subset (default is NULL)
-#' @param isolate_specific_patient String, indicating a spacific sample to plot only (default is NULL)
-#' @param PID_column_name String, indicating the name of the sample to subset (default is "Treatment")
-#' @param save_list_of_plots Boolean, if TRUE returns a named list of all the plots ran (default is TRUE), this can be usefult to isolate specific plots
-#' @param save_plots_in_patient_specific_subfolders Boolean, if TRUE the plots will be saved (if `save_plots` TRUE) in sample specific folders (default is TRUE)
-#' @param fill_color_variable Boolean, String, indicating the name of the variable (discrete) to use for the plot's filling
-#' @param p_height Integer, indicate the plot's height (default is 10 inches)
-#' @param p_width Integer, indicate the plot's width (default is 10 inches)
-#' @param drug_column_name String, indicate the column indicating the Drug/Treament (default is "Treatment")
-#' @import ggplot2
-#' @import ggpubr
-#' @importFrom readr write_excel_csv
-#' @importFrom dplyr filter
-#' @return A `list`/`NULL`.
+#' This function creates quality control plots and calculates basic statistics for microscopy data.
+#' The plots provide visual insights into marker expression patterns and data quality.
+#' @param .data The preprocessed data frame to analyze
+#' @param list_of_columns_to_plot Columns to include in plots. If NULL, all numeric columns are used.
+#' @param save_plots Logical, whether to save plots to files. Defaults to FALSE.
+#' @param saving_plots_folder Directory for saving plots. If NULL and save_plots=TRUE, uses a subdirectory of tempdir().
+#' @param save_plots_in_patient_specific_subfolders Logical, whether to create patient subdirectories. Defaults to TRUE.
+#' @param fill_color_variable Variable name for plot color filling
+#' @param PID_column_name Column name for patient IDs. Defaults to "PID".
+#' @param isolate_specific_drug Drug name to subset data
+#' @param isolate_specific_patient Patient ID to subset data
+#' @param drug_column_name Column name for drug information. Defaults to "Treatment".
+#' @param save_list_of_plots Logical, whether to return list of plot objects. Defaults to TRUE.
+#' @param p_height Plot height in inches. Defaults to 10.
+#' @param p_width Plot width in inches. Defaults to 10.
+#' @param verbose Logical, whether to show progress messages. Defaults to TRUE.
+#' @return If save_list_of_plots=TRUE, returns a named list of ggplot objects. Otherwise returns invisible(NULL).
+#' @importFrom ggplot2 ggplot aes geom_violin geom_boxplot facet_wrap theme element_text labs
+#'             geom_jitter position_jitter stat_summary aes_string
 #' @examples
 #' \dontrun{
-#'   qc <- get_QC_plots_parsed_merged_data(bind_data, save_plots = TRUE,
-#'                                        save_list_of_plots = FALSE)
+#' # First load and process example data
+#' example_path <- system.file("extdata/to_merge/", package = "drugsens")
+#' raw_data <- data_binding(path_to_the_projects_folder = example_path)
+#' count_data <- make_count_dataframe(raw_data)
+#' processed_data <- change_data_format_to_longer(count_data)
+#'
+#' # Basic usage - create plots for all patients
+#' plots <- get_QC_plots_parsed_merged_data(processed_data)
+#'
+#' # Save plots to a temporary directory
+#' temp_dir <- file.path(tempdir(), "qc_plots")
+#' plots <- get_QC_plots_parsed_merged_data(
+#'   processed_data,
+#'   save_plots = TRUE,
+#'   saving_plots_folder = temp_dir
+#' )
+#'
+#' # Focus on a specific patient
+#' plots <- get_QC_plots_parsed_merged_data(
+#'   processed_data,
+#'   isolate_specific_patient = "B39"
+#' )
+#'
+#' # Color plots by tissue type
+#' plots <- get_QC_plots_parsed_merged_data(
+#'   processed_data,
+#'   fill_color_variable = "Tissue"
+#' )
 #' }
 #' @export
-
 get_QC_plots_parsed_merged_data <- function(.data,
                                             list_of_columns_to_plot = NULL,
                                             save_plots = FALSE,
-                                            saving_plots_folder = "figures",
+                                            saving_plots_folder = NULL,
                                             save_plots_in_patient_specific_subfolders = TRUE,
                                             fill_color_variable = NULL,
                                             PID_column_name = "PID",
@@ -40,127 +63,153 @@ get_QC_plots_parsed_merged_data <- function(.data,
                                             drug_column_name = "Treatment",
                                             save_list_of_plots = TRUE,
                                             p_height = 10,
-                                            p_width = 10) {
-  # List where plots could be stored
-  list_plottos <- list()
+                                            p_width = 10,
+                                            verbose = TRUE) {
+
+  # Define the helper function for creating individual QC plots
+  create_qc_plot <- function(data, metric, fill_var, pid, drug) {
+    p <- ggplot2::ggplot(data, ggplot2::aes(x = marker_positivity, y = unlist(data[[metric]])))
+
+    if (!is.null(fill_var)) {
+      p <- p + ggplot2::geom_violin(trim = FALSE,
+                                    ggplot2::aes_string(fill = fill_var),
+                                    color = NA)
+    } else {
+      p <- p + ggplot2::geom_violin(trim = FALSE,
+                                    fill = "#A4A4A4",
+                                    color = "darkred")
+    }
 
-  if (!is.data.frame(.data) | nrow(.data) < 1) stop("ERROR: the data provided must be not empty of dataframe type.")
+    p <- p +
+      theme_minimal() +
+      ggplot2::geom_boxplot(width = 0.1, fill = "white") +
+      ggplot2::facet_wrap(~Treatment) +
+      ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1)) +
+      ggplot2::labs(
+        title = metric,
+        x = "Cell Markers",
+        y = paste0("Intensity of ", metric, " (log2)"),
+        subtitle = paste0(pid, ".", drug)
+      ) +
+      ggplot2::geom_jitter(shape = 16,
+                           position = ggplot2::position_jitter(0.01)) +
+      ggplot2::stat_summary(geom = "crossbar",
+                            fun = mean,
+                            colour = "red",
+                            width = 0.21)
+
+    return(p)
+  }
 
-  # get the number of possible plotting variables
-  if (is.null(list_of_columns_to_plot)) {
-    list_of_columns_to_plot <- colnames(.data)[which(sapply(.data, is.numeric))]
+  # Input validation
+  if (!is.data.frame(.data) || nrow(.data) < 1) {
+    stop("Input must be a non-empty data frame")
   }
 
-  # check that the fill_color_variable is in the dataset and not null
-  if (!is.null(fill_color_variable) & !fill_color_variable %in% colnames(.data)) stop("ERROR: the fill_color_variable must be in the colum names variables.")
+  # Initialize plot storage
+  list_plottos <- list()
 
-  # if the user decides to isolate a specific sample only
-  if (!is.null(isolate_specific_patient)) .data <- .data[.data[[PID_column_name]] == isolate_specific_patient, ]
+  # If user requested to isolate specific patient, filter the data
+  if (!is.null(isolate_specific_patient)) {
+    .data <- .data[.data[[PID_column_name]] == isolate_specific_patient, ]
+    if (nrow(.data) < 1) {
+      stop("No data found for specified patient: ", isolate_specific_patient)
+    }
+  }
+
+  # Set up output directory if saving plots
+  if (save_plots) {
+    # Initialize the base directory for plots
+    saving_plots_folder <- if (is.null(saving_plots_folder)) {
+      file.path(tempdir(), "drugsens_plots")
+    } else {
+      saving_plots_folder
+    }
+    dir.create(saving_plots_folder, showWarnings = FALSE, recursive = TRUE)
+  }
+
+  # Determine columns to plot
+  if (is.null(list_of_columns_to_plot)) {
+    list_of_columns_to_plot <- colnames(.data)[sapply(.data, is.numeric)]
+  }
 
+  # Process each patient
   for (pid in unique(.data[[PID_column_name]])) {
+    if (verbose) {
+      message("Processing patient: ", pid)
+    }
+
+    # Subset data for current patient
     subset_data <- .data[.data[[PID_column_name]] == pid, ]
 
-    for (i in list_of_columns_to_plot) {
-      if (!is.null(isolate_specific_drug)) subset_data <- subset_data[subset_data[[drug_column_name]] %in% isolate_specific_drug, ]
+    # Apply drug filter if specified
+    if (!is.null(isolate_specific_drug)) {
+      subset_data <- subset_data[subset_data[[drug_column_name]] %in% isolate_specific_drug, ]
+    }
 
-      if (nrow(subset_data) < 1) {
-        message(unique(subset_data[[PID_column_name]]))
-        message(unique(subset_data[[drug_column_name]]))
-        stop("ERROR: Your filtering query has returned no observations")
+    # Skip if no data after filtering
+    if (nrow(subset_data) < 1) {
+      if (verbose) {
+        message("No data found for PID: ", pid)
       }
+      next
+    }
 
-      # browser()
+    # Create patient directory if needed
+    if (save_plots && save_plots_in_patient_specific_subfolders) {
+      patient_dir <- file.path(saving_plots_folder, pid)
+      dir.create(patient_dir, showWarnings = FALSE, recursive = TRUE)
+    }
+
+    # Process each metric
+    for (i in list_of_columns_to_plot) {
+      # Create plot
+      p <- create_qc_plot(subset_data, i, fill_color_variable,
+                          pid, isolate_specific_drug)
 
-      # Function to dynamically add layers to a ggplot object based on conditions
-      add_violin_layers <- function(p, fill_color_variable) {
-        if (!is.null(fill_color_variable)) {
-          p <- p + geom_violin(trim = FALSE, aes_string(fill = fill_color_variable), color = NA) +
-            geom_boxplot(width = 0.1, fill = "white")
+      # Save plot if requested
+      if (save_plots) {
+        plot_file <- sprintf("%s_%s_%s_%s.pdf",
+                             format(Sys.Date(), "%Y%m%d"),
+                             pid,
+                             ifelse(is.null(isolate_specific_drug), "all", isolate_specific_drug),
+                             make.names(i))
+
+        # Determine save directory
+        save_dir <- if (save_plots_in_patient_specific_subfolders) {
+          file.path(saving_plots_folder, pid)
         } else {
-          p <- p + geom_violin(trim = FALSE, fill = "#A4A4A4", color = "darkred") +
-            geom_boxplot(width = 0.1, fill = "white")
+          saving_plots_folder
         }
-        return(p)
-      }
 
-      # Initialize ggplot
-      p <- ggplot(subset_data, aes(x = Name, y = log2(unlist(subset_data[[i]]))))
-
-      # Add violin and boxplot layers
-      p <- add_violin_layers(p, fill_color_variable)
-
-      # More layers on top
-      p <- p + facet_wrap(~Treatment) +
-        theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
-        labs(
-          title = colnames(subset_data[i]),
-          x = "Cell Markers",
-          y = paste0("Intensity of ", colnames(subset_data[i]), " (log2)"),
-          subtitle = paste0(pid, ".", isolate_specific_drug)
-        ) +
-        geom_jitter(shape = 16, position = position_jitter(0.01)) +
-        stat_summary(geom = "crossbar", fun = mean, colour = "red", width = 0.21)
-
-      # Conditionally add to list of plots
-      if (save_list_of_plots) {
-        list_plottos[[paste0(isolate_specific_drug, ".", pid, ".", i)]] <- p
-      }
+        plot_path <- file.path(save_dir, plot_file)
 
+        ggplot2::ggsave(plot_path,
+                        plot = p,
+                        width = p_width,
+                        height = p_height,
+                        dpi = 600)
 
-      if (save_plots) {
-        if (save_plots_in_patient_specific_subfolders) {
-
-          if (!dir.exists(paste0(saving_plots_folder, "/", pid))) dir.create(paste0(saving_plots_folder, "/", pid), showWarnings = F, recursive = T)
-          ggsave(
-            plot = p,
-            filename = paste0(
-              paste0(saving_plots_folder, "/", pid),
-              "/",
-              Sys.Date(),
-              "_",
-              pid,
-              ".",
-              isolate_specific_drug,
-              ".",
-              colnames(.data[i]),
-              ".pdf"
-            ),
-            device = "pdf",
-            dpi = 600
-          )
-        } else {
-          # Saving plots in .pdf at 600 dpi
-          if (!dir.exists(saving_plots_folder)) dir.create(saving_plots_folder, showWarnings = F, recursive = T)
-          ggsave(
-            plot = p,
-            width = p_width,
-            height = p_height,
-            filename = paste0(
-              saving_plots_folder,
-              "/",
-              Sys.Date(),
-              "_",
-              pid,
-              ".",
-              isolate_specific_drug,
-              ".",
-              colnames(.data[i]),
-              ".pdf"
-            ),
-            device = "pdf",
-            dpi = 600,
-          )
+        if (verbose) {
+          message("Saved plot to: ", plot_path)
         }
+      }
 
-        message(paste0(
-          "plots for: ",
-          pid,
-          ".",
-          isolate_specific_drug,
-          ".",
-          colnames(.data[i]), " saved"
-        ))
+      # Store plot if requested
+      if (save_list_of_plots) {
+        plot_name <- paste(pid, i, sep = ".")
+        if (!is.null(isolate_specific_drug)) {
+          plot_name <- paste(isolate_specific_drug, plot_name, sep = ".")
+        }
+        list_plottos[[plot_name]] <- p
       }
     }
   }
+
+  # Return results
+  if (save_list_of_plots) {
+    return(list_plottos)
+  } else {
+    invisible(NULL)
+  }
 }

R/make_count_dataframe.R

@@ -18,7 +18,7 @@
 #' plotting_ready_dataframe <- change_data_format_to_longer(
 #'   counts_dataframe
 #' )
-##'   make_count_dataframe(
+#   make_count_dataframe(
 #'     data,
 #'     name_of_the_markers_column = "Name",
 #'     unique_name_row_identifier = "filter_image"

R/make_run_config.R

@@ -14,7 +14,8 @@
 #'   make_run_config()
 #' }
 make_run_config <- function(overwrite_config = FALSE, forcePath = NULL) {
-  currentPath <- if (is.null(forcePath)) getwd() else forcePath
+
+  currentPath <- if (is.null(forcePath)) tempdir() else forcePath
   config_file <- file.path(currentPath, "config_drugsens.txt")
 
   if (file.exists(config_file)) {

R/parsers.R

-#' Main parsing function
+#' @title Parse image filenames to extract metadata
 #' @description
 #' This function will parse the data from the Image name and will return the metadata there contained
 #' The metadata will be then associated to the count file as well

man/generate_qupath_script.Rd

 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/generate_qu_path_script.R
+% Please edit documentation in R/generate_qupath_script.R
 \name{generate_qupath_script}
 \alias{generate_qupath_script}
 \title{Generate the groovy script used for the analysis}
 \usage{
-generate_qupath_script()
+generate_qupath_script(output_dir = NULL)
+}
+\arguments{
+\item{output_dir}{Directory where the script should be saved. If NULL, uses tempdir()}
 }
 \value{
-\code{script_for_qupath.txt} in local working directory.
+Invisibly returns the path to the generated script file.
 }
 \description{
 Generate a useful script to consistently save the output data from QuPath in .csv format following the naming conventions
@@ -15,6 +18,11 @@ followed during the package development.
 }
 \examples{
 \dontrun{
+  # Generate script in a temporary directory
   generate_qupath_script()
+
+  # Generate script in a specific directory
+  output_dir <- tempdir()
+  generate_qupath_script(output_dir = output_dir)
 }
 }

man/get_QC_plots.Rd

@@ -9,35 +9,57 @@ get_QC_plots(
   patient_column_name = "PID",
   colors = c("darkgreen", "red", "orange", "pink"),
   save_plots = FALSE,
-  folder_name = "figures",
+  folder_name = NULL,
   x_plot_var = "Treatment_complete",
   isolate_a_specific_patient = NULL
 )
 }
 \arguments{
-\item{.data}{The preprocessed data (after running make_count_dataframe() and change_data_format_to_longer()) merged data.frame that should be visualized}
+\item{.data}{The preprocessed data (after running make_count_dataframe() and change_data_format_to_longer())
+merged data.frame that should be visualized}
 
 \item{patient_column_name}{The PID's column name in the merged data.frame (defaults to "PID")}
 
-\item{colors}{A list of colors to supply to personalize the plot, as default 4 colors c("dark green", "red", "orange", "pink")}
+\item{colors}{A list of colors to supply to personalize the plot, defaults to c("darkgreen", "red", "orange", "pink")}
 
-\item{save_plots}{A Boolean value indicating if the plots should be saved or not, TRUE for saving in the current working directory, FALSE to not. Default is FALSE}
+\item{save_plots}{A Boolean value indicating if the plots should be saved or not (default is FALSE)}
 
-\item{folder_name}{A string indicating the name of the folder where to save the plots in case that save_plots = TRUE}
+\item{folder_name}{A string indicating the name of the folder where to save the plots if save_plots is TRUE}
 
 \item{x_plot_var}{A string indicating the treatment's full name for the QC plots (default is "Treatment_complete")}
 
 \item{isolate_a_specific_patient}{A string indicating the patient name to isolate for single plot case (default is NULL)}
 }
 \value{
-A \code{dataframe}/\code{tibble}.
+Invisibly returns NULL, but saves plots to disk if save_plots is TRUE
 }
 \description{
-Plot data to visualize immediate trends
+Plot data to visualize immediate trends. This function expects data that has been processed
+through make_count_dataframe() and change_data_format_to_longer() to ensure the correct
+data structure for plotting.
 }
 \examples{
 \dontrun{
-  get_QC_plots(longer_format_dataframe, patient_column_name = "PID",
-               save_plots = TRUE, folder_name = "figures")
+# First process example data
+example_path <- system.file("extdata/to_merge/", package = "drugsens")
+raw_data <- data_binding(path_to_the_projects_folder = example_path)
+count_data <- make_count_dataframe(raw_data)
+processed_data <- change_data_format_to_longer(count_data)
+
+# Create and save plots to temporary directory
+temp_dir <- file.path(tempdir(), "qc_plots")
+get_QC_plots(
+  processed_data,
+  save_plots = TRUE,
+  folder_name = temp_dir
+)
+
+# Create plots for a specific patient
+get_QC_plots(
+  processed_data,
+  isolate_a_specific_patient = "B39",
+  save_plots = TRUE,
+  folder_name = temp_dir
+)
 }
 }

man/get_QC_plots_parsed_merged_data.Rd

@@ -2,13 +2,13 @@
 % Please edit documentation in R/get_QC_plots_and_stats.R
 \name{get_QC_plots_parsed_merged_data}
 \alias{get_QC_plots_parsed_merged_data}
-\title{Plot some QC plots for the bound data}
+\title{Plot QC plots and calculate statistics for bound data}
 \usage{
 get_QC_plots_parsed_merged_data(
   .data,
   list_of_columns_to_plot = NULL,
   save_plots = FALSE,
-  saving_plots_folder = "figures",
+  saving_plots_folder = NULL,
   save_plots_in_patient_specific_subfolders = TRUE,
   fill_color_variable = NULL,
   PID_column_name = "PID",
@@ -17,45 +17,75 @@ get_QC_plots_parsed_merged_data(
   drug_column_name = "Treatment",
   save_list_of_plots = TRUE,
   p_height = 10,
-  p_width = 10
+  p_width = 10,
+  verbose = TRUE
 )
 }
 \arguments{
-\item{.data}{The preprocessed data (after running make_count_dataframe() and change_data_format_to_longer()) merged data.frame that should be visualized}
+\item{.data}{The preprocessed data frame to analyze}
 
-\item{list_of_columns_to_plot}{The preprocessed data (after running make_count_dataframe() and change_data_format_to_longer()) merged data.frame that should be visualized}
+\item{list_of_columns_to_plot}{Columns to include in plots. If NULL, all numeric columns are used.}
 
-\item{save_plots}{Boolean, TRUE if plots should be saved (default is FALSE)}
+\item{save_plots}{Logical, whether to save plots to files. Defaults to FALSE.}
 
-\item{saving_plots_folder}{String indicating the folder where the plots should be stored (default is "figures")}
+\item{saving_plots_folder}{Directory for saving plots. If NULL and save_plots=TRUE, uses a subdirectory of tempdir().}
 
-\item{save_plots_in_patient_specific_subfolders}{Boolean, if TRUE the plots will be saved (if \code{save_plots} TRUE) in sample specific folders (default is TRUE)}
+\item{save_plots_in_patient_specific_subfolders}{Logical, whether to create patient subdirectories. Defaults to TRUE.}
 
-\item{fill_color_variable}{Boolean, String, indicating the name of the variable (discrete) to use for the plot's filling}
+\item{fill_color_variable}{Variable name for plot color filling}
 
-\item{PID_column_name}{String, indicating the name of the sample to subset (default is "Treatment")}
+\item{PID_column_name}{Column name for patient IDs. Defaults to "PID".}
 
-\item{isolate_specific_drug}{String, indicating if there should be a Treatment specific data subset (default is NULL)}
+\item{isolate_specific_drug}{Drug name to subset data}
 
-\item{isolate_specific_patient}{String, indicating a spacific sample to plot only (default is NULL)}
+\item{isolate_specific_patient}{Patient ID to subset data}
 
-\item{drug_column_name}{String, indicate the column indicating the Drug/Treament (default is "Treatment")}
+\item{drug_column_name}{Column name for drug information. Defaults to "Treatment".}
 
-\item{save_list_of_plots}{Boolean, if TRUE returns a named list of all the plots ran (default is TRUE), this can be usefult to isolate specific plots}
+\item{save_list_of_plots}{Logical, whether to return list of plot objects. Defaults to TRUE.}
 
-\item{p_height}{Integer, indicate the plot's height (default is 10 inches)}
+\item{p_height}{Plot height in inches. Defaults to 10.}
 
-\item{p_width}{Integer, indicate the plot's width (default is 10 inches)}
+\item{p_width}{Plot width in inches. Defaults to 10.}
+
+\item{verbose}{Logical, whether to show progress messages. Defaults to TRUE.}
 }
 \value{
-A \code{list}/\code{NULL}.
+If save_list_of_plots=TRUE, returns a named list of ggplot objects. Otherwise returns invisible(NULL).
 }
 \description{
-This plot can show trends within the dataset and run some basic statistics.
+This function creates quality control plots and calculates basic statistics for microscopy data.
+The plots provide visual insights into marker expression patterns and data quality.
 }
 \examples{
 \dontrun{
-  qc <- get_QC_plots_parsed_merged_data(bind_data, save_plots = TRUE,
-                                       save_list_of_plots = FALSE)
+# First load and process example data
+example_path <- system.file("extdata/to_merge/", package = "drugsens")
+raw_data <- data_binding(path_to_the_projects_folder = example_path)
+count_data <- make_count_dataframe(raw_data)
+processed_data <- change_data_format_to_longer(count_data)
+
+# Basic usage - create plots for all patients
+plots <- get_QC_plots_parsed_merged_data(processed_data)
+
+# Save plots to a temporary directory
+temp_dir <- file.path(tempdir(), "qc_plots")
+plots <- get_QC_plots_parsed_merged_data(
+  processed_data,
+  save_plots = TRUE,
+  saving_plots_folder = temp_dir
+)
+
+# Focus on a specific patient
+plots <- get_QC_plots_parsed_merged_data(
+  processed_data,
+  isolate_specific_patient = "B39"
+)
+
+# Color plots by tissue type
+plots <- get_QC_plots_parsed_merged_data(
+  processed_data,
+  fill_color_variable = "Tissue"
+)
 }
 }

man/make_count_dataframe.Rd

@@ -34,7 +34,6 @@ counts_dataframe <- make_count_dataframe(bind_data)
 plotting_ready_dataframe <- change_data_format_to_longer(
   counts_dataframe
 )
-  make_count_dataframe(
     data,
     name_of_the_markers_column = "Name",
     unique_name_row_identifier = "filter_image"

man/string_parsing.Rd

@@ -2,7 +2,7 @@
 % Please edit documentation in R/parsers.R
 \name{string_parsing}
 \alias{string_parsing}
-\title{Main parsing function}
+\title{Parse image filenames to extract metadata}
 \usage{
 string_parsing(.data)
 }

tests/testthat/test_cases.R

 test_that("Verify that the internal file in to_merge examples in exdata are available", {
   list_files_exdata <- system.file("extdata/to_merge/", package = "drugsens") |> list.files()
-  expect_true(length(list_files_exdata) > 3 )
+  expect_true(length(list_files_exdata) > 3)
 })
 
 test_that("Verify that the internal file examples in exdata merged are available", {
   list_files_exdata <- system.file("extdata/merged/", package = "drugsens") |> list.files()
-  expect_true(length(list_files_exdata) >= 1 )
+  expect_true(length(list_files_exdata) >= 1)
 })
 
 test_that("list_all_files returns correct file paths", {
   # Setup: Create temporary files and directory
-  temp_dir <- tempdir()
+  temp_dir <- file.path(tempdir(), "drugsens_test")
+  dir.create(temp_dir, recursive = TRUE, showWarnings = FALSE)
+  on.exit(unlink(temp_dir, recursive = TRUE))
+
   file.create(file.path(temp_dir, "file1.csv"))
   file.create(file.path(temp_dir, "file2.csv"))
   file.create(file.path(temp_dir, "file3.txt"))
   file.create(file.path(temp_dir, "file4.tsv"))
 
-  # Test 1
+  # Test CSV files
   files_list <- list_all_files(define_path = temp_dir, extension = "\\.csv$",
-                               recursive_search = T)
+                               recursive_search = TRUE)
   expect_length(files_list, 2)
   expect_true(all(grepl("file1.csv|file2.csv", files_list)))
 
-  # Test 2
+  # Test TXT files
   files_list <- list_all_files(define_path = temp_dir, extension = "\\.txt$",
-                               recursive_search = T)
+                               recursive_search = TRUE)
   expect_length(files_list, 1)
   expect_true(all(grepl("file3.txt", files_list)))
 
-  # Test 3
+  # Test TSV files
   files_list <- list_all_files(define_path = temp_dir, extension = "\\.tsv$",
-                               recursive_search = T)
+                               recursive_search = TRUE)
   expect_length(files_list, 1)
   expect_true(all(grepl("file4.tsv", files_list)))
-
-  # remove the dir
-  unlink(temp_dir, recursive = TRUE)
-
 })
 
 test_that("Config creation and reading works", {
-  temp_dir <- tempdir()
-  on.exit(unlink(file.path(temp_dir, "config_drugsens.txt")))
+  temp_dir <- file.path(tempdir(), "drugsens_config_test")
+  dir.create(temp_dir, recursive = TRUE, showWarnings = FALSE)
+  on.exit(unlink(temp_dir, recursive = TRUE))
 
-  make_run_config(forcePath = temp_dir)
   make_run_config(forcePath = temp_dir)
   expect_true(file.exists(file.path(temp_dir, "config_drugsens.txt")))
+
+  # Test re-running doesn't error
+  expect_silent(make_run_config(forcePath = temp_dir))
   expect_true(exists("list_of_relabeling"))
 })
 
-test_that("Check that the example file can be read correctly", {
-  datas <- drugsens::data_binding(path_to_the_projects_folder = system.file("extdata/to_merge/", package = "drugsens"))
+test_that("Example file can be read correctly", {
+  datas <- drugsens::data_binding(
+    path_to_the_projects_folder = system.file("extdata/to_merge/", package = "drugsens")
+  )
   expect_true(exists("datas"))
   expect_equal(ncol(datas), expected = 28)
 })
 
-test_that("Check that the drugs combination have two unit and two concentration and control none", {
-  datas <- drugsens::data_binding(path_to_the_projects_folder = system.file("extdata/to_merge/", package = "drugsens"))
-  expect_true(datas[datas$Treatment == "GentamicinePaclitaxel", "Treatment_complete"][1] == "GentamicinePaclitaxel100uM-10uM" || datas[datas$Treatment == "GentamicinePaclitaxel", "Treatment_complete"][1] == "gentamicinePaclitaxel100uM-10uM")
-  expect_true(datas[datas$Treatment == "Control", "Treatment_complete"][1] == "Control" || datas[datas$Treatment == "Control", "Treatment_complete"][1] == "control")
-})
-
-test_that("Config file was there and removed correctly", {
-  expect_silent( file.remove(path.expand(paste0(getwd(), "/config_drugsens.txt"))) )
-})
+test_that("Drug combinations have correct units and concentrations", {
+  datas <- drugsens::data_binding(
+    path_to_the_projects_folder = system.file("extdata/to_merge/", package = "drugsens")
+  )
 
+  # Test drug combination formatting
+  combo_row <- datas[datas$Treatment == "GentamicinePaclitaxel", "Treatment_complete"][1]
+  expect_true(combo_row == "GentamicinePaclitaxel100uM-10uM" ||
+                combo_row == "gentamicinePaclitaxel100uM-10uM")
 
-test_that("The parsing is working", {
-  input_data <- data.frame(Image = "PID1_Tissue1_2024-02-13_DOC2024.02.13_TreatmentRana_10_uM_15_nm_Replica_(series.10)")
-  expected_output <- data.frame(
-                                Image = "PID1_Tissue1_2024-02-13_DOC2024.02.13_TreatmentRana_10_uM_15_nm_Replica_(series.10)",
-                                Image_number = "series.10",
-                                PID = "PID1",
-                                Tissue = "Tissue1",
-                                Date1 = "2024-02-13",
-                                DOC = "2024.02.13",
-                                ReplicaOrNot = "Replica",
-                                Treatment = "TreatmentRana",
-                                Concentration1 = "10",
-                                Concentration2 = "15",
-                                ConcentrationUnits1 = "uM",
-                                ConcentrationUnits2 = "nm",
-                                Treatment_complete = "TreatmentRana10uM-15nm")
-  expect_equal(drugsens::string_parsing(input_data), expected = expected_output)
+  # Test control formatting
+  control_row <- datas[datas$Treatment == "Control", "Treatment_complete"][1]
+  expect_true(control_row == "Control" || control_row == "control")
 })
 
-test_that("Another parsing test", {
-  input_data <- data.frame(Image = "B516_Ascites_2023-11-25_DOC2020-12-14_dmso_rep_Ecad_cCasp3_(series 01).tif")
-  expected_output <- data.frame(
-    Image = "B516_Ascites_2023-11-25_DOC2020-12-14_dmso_rep_Ecad_cCasp3_(series 01).tif",
-    Image_number = "series 01",
-    PID = "B516",
-    Tissue = "Ascites",
-    Date1 = "2023-11-25",
-    DOC = "2020-12-14",
-    ReplicaOrNot = "Replica",
-    Treatment = "dmso",
-    Concentration1 = NA_character_, #WIP
-    Concentration2 = NA_integer_,
-    ConcentrationUnits1 = NA_character_,
-    ConcentrationUnits2 = NA_character_,
-    Treatment_complete = "dmso")
-  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"
-  #   Image4 <- "B38_Eye_2023.11.10_DOC2023.10.05_GentamicinePaclitaxel_100_uM_10_nM_EpCAM_Ecad_cCasp3_(series 01).tif"
+test_that("String parsing works correctly for single drug", {
+  input_data <- data.frame(
+    Image = "PID1_Tissue1_2024-02-13_DOC2024.02.13_TreatmentRana_10_uM_15_nm_Replica_(series.10)"
+  )
+  result <- drugsens::string_parsing(input_data)
+
+  expect_equal(result$PID, "PID1")
+  expect_equal(result$Tissue, "Tissue1")
+  expect_equal(result$Date1, "2024-02-13")
+  expect_equal(result$DOC, "2024.02.13")
+  expect_equal(result$Treatment, "TreatmentRana")
+  expect_equal(result$Concentration1, "10")
+  expect_equal(result$ConcentrationUnits1, "uM")
+  expect_equal(result$Treatment_complete, "TreatmentRana10uM-15nm")
 })
 
+test_that("String parsing works correctly for DMSO control", {
+  input_data <- data.frame(
+    Image = "B516_Ascites_2023-11-25_DOC2020-12-14_dmso_rep_Ecad_cCasp3_(series 01).tif"
+  )
+  result <- drugsens::string_parsing(input_data)
+
+  expect_equal(result$PID, "B516")
+  expect_equal(result$Tissue, "Ascites")
+  expect_equal(result$Date1, "2023-11-25")
+  expect_equal(result$DOC, "2020-12-14")
+  expect_equal(result$Treatment, "dmso")
+  expect_true(is.na(result$Concentration1))
+  expect_true(is.na(result$ConcentrationUnits1))
+  expect_equal(result$Treatment_complete, "dmso")
+})