#' Plot QC plots and calculate statistics for bound data
#' @description
#' 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{
#' # 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 = NULL,
save_plots_in_patient_specific_subfolders = TRUE,
fill_color_variable = NULL,
PID_column_name = "PID",
isolate_specific_drug = NULL,
isolate_specific_patient = NULL,
drug_column_name = "Treatment",
save_list_of_plots = TRUE,
p_height = 10,
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")
}
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)
}
# Input validation
if (!is.data.frame(.data) || nrow(.data) < 1) {
stop("Input must be a non-empty data frame")
}
# Initialize plot storage
list_plottos <- list()
# 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, ]
# Apply drug filter if specified
if (!is.null(isolate_specific_drug)) {
subset_data <- subset_data[subset_data[[drug_column_name]] %in% isolate_specific_drug, ]
}
# Skip if no data after filtering
if (nrow(subset_data) < 1) {
if (verbose) {
message("No data found for PID: ", pid)
}
next
}
# 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)
# 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 {
saving_plots_folder
}
plot_path <- file.path(save_dir, plot_file)
ggplot2::ggsave(plot_path,
plot = p,
width = p_width,
height = p_height,
dpi = 600)
if (verbose) {
message("Saved plot to: ", plot_path)
}
}
# 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)
}
}