Package 'drugsens'

Data format changer

Description

This function gets the count data data.frame, that has a wider format and it returns a longer-formatted data.frame

Usage

change_data_format_to_longer(
  .data,
  pattern_column_markers = "_ratio_of_total_cells",
  unique_name_row_identifier = "filter_image",
  additional_columns = TRUE
)

Arguments

.data	The markers count dataframe that is coming from the processing of the microscopy data
pattern_column_markers	The markers' pattern name to obtain the column with ratios of the markers (it defaults to "_ratio_of_total_cells")
unique_name_row_identifier	String that indicates the unique identifier for each image, defaults as "filter_image"
additional_columns	columns that can be additionally added to the longer formatted data.frame, "Defaults as c("Treatment", "PID", "Image_number", "Tissue", "Concentration", "DOC")"

Details

Reformat the counts data in longer format

Value

A dataframe/tibble.

Examples

# Set up relabeling list
list_of_relabeling <- list(
  "PathCellObject" = "onlyDAPIPositve",
  "cCasp3" = "cCASP3",
  "E-Cadherin: cCASP3" = "E-Cadherin and cCASP3",
  "EpCAM_E-Cadherin" = "E-Cadherin",
  "EpCAM_E-Cadherin and cCASP3" = "E-Cadherin and cCASP3"
)

# Load and process example data
bind_data <- data_binding(
  path_to_the_projects_folder = system.file("extdata/to_merge/", package = "drugsens")
)
counts_dataframe <- make_count_dataframe(bind_data)

# Convert to long format
plotting_ready_dataframe <- change_data_format_to_longer(counts_dataframe)

---

Merge all the dataframes coming out from the QuPath

Description

This function identifies string patterns in the dataset, fills the dataframe with that information, and combines all data into a single file

Usage

data_binding(
  path_to_the_projects_folder,
  files_extension_to_look_for = "csv",
  recursive_search = FALSE,
  forcePath = NULL
)

Arguments

path_to_the_projects_folder	String/Path The path where the files coming out of QuPath are located
files_extension_to_look_for	String The extension of the file outputted from QuPath, (default is "csv")
recursive_search	Boolean, it defined the behavior of the file search, if recursive or not, (default is FALSE)
forcePath	String defining an alternative path to the confic file

Value

A concatenated dataframe from all the files within the indicated path

Examples

## Not run: 
bind_data <- data_binding(path_to_the_projects_folder = system.file("extdata/to_merge/",
                         package = "drugsens"))

## End(Not run)

---

Generate the groovy script used for the analysis

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.

Usage

generate_qupath_script(output_dir = NULL)

Arguments

output_dir

Directory where the script should be saved. If NULL, uses tempdir()

Value

Invisibly returns the path to the generated script file.

Examples

## Not run: 
  # 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)

## End(Not run)

---

Plot some QC plots to define that everything ran correctly

Description

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.

Usage

get_QC_plots(
  .data,
  patient_column_name = "PID",
  colors = c("darkgreen", "red", "orange", "pink"),
  save_plots = FALSE,
  folder_name = NULL,
  x_plot_var = "Treatment_complete",
  isolate_a_specific_patient = NULL
)

Arguments

.data	The preprocessed data (after running make_count_dataframe() and change_data_format_to_longer()) merged data.frame that should be visualized
patient_column_name	The PID's column name in the merged data.frame (defaults to "PID")
colors	A list of colors to supply to personalize the plot, defaults to c("darkgreen", "red", "orange", "pink")
save_plots	A Boolean value indicating if the plots should be saved or not (default is FALSE)
folder_name	A string indicating the name of the folder where to save the plots if save_plots is TRUE
x_plot_var	A string indicating the treatment's full name for the QC plots (default is "Treatment_complete")
isolate_a_specific_patient	A string indicating the patient name to isolate for single plot case (default is NULL)

Value

Invisibly returns NULL, but saves plots to disk if save_plots is TRUE

Examples

# 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
)

---

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.

Usage

get_QC_plots_parsed_merged_data(
  .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
)

Arguments

.data	The preprocessed data frame to analyze
list_of_columns_to_plot	Columns to include in plots. If NULL, all numeric columns are used.
save_plots	Logical, whether to save plots to files. Defaults to FALSE.
saving_plots_folder	Directory for saving plots. If NULL and save_plots=TRUE, uses a subdirectory of tempdir().
save_plots_in_patient_specific_subfolders	Logical, whether to create patient subdirectories. Defaults to TRUE.
fill_color_variable	Variable name for plot color filling
PID_column_name	Column name for patient IDs. Defaults to "PID".
isolate_specific_drug	Drug name to subset data
isolate_specific_patient	Patient ID to subset data
drug_column_name	Column name for drug information. Defaults to "Treatment".
save_list_of_plots	Logical, whether to return list of plot objects. Defaults to TRUE.
p_height	Plot height in inches. Defaults to 10.
p_width	Plot width in inches. Defaults to 10.
verbose	Logical, whether to show progress messages. Defaults to TRUE.

Value

If save_list_of_plots=TRUE, returns a named list of ggplot objects. Otherwise returns invisible(NULL).

Examples

# 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"
)

---

Count the main marker expression

Description

This function counts every single marker present in the "Name" column of the data.frame and return a dataframe of the counts per marker

Usage

make_count_dataframe(
  .data,
  unique_name_row_identifier = "filter_image",
  name_of_the_markers_column = "Name"
)

Arguments

.data	The dataframe that is coming from the processing of the microscopy data
unique_name_row_identifier	The name of the column of the .data where the unique name can be used to counts (it defaults to "filter_image")
name_of_the_markers_column	The name of the column of the .data where the marker names are expressed (ie E-Caderin, DAPI), "Defaults as Name"

Value

A dataframe/tibble.

Examples

# First load example data
pkg_path <- system.file("extdata/to_merge/", package = "drugsens")
bind_data <- data_binding(
  path_to_the_projects_folder = pkg_path,
  files_extension_to_look_for = "csv"
)

# Process the data
counts_dataframe <- make_count_dataframe(bind_data)

# Convert to plotting format
plotting_ready_dataframe <- change_data_format_to_longer(counts_dataframe)



# Example with custom parameters
make_count_dataframe(
  bind_data,
  name_of_the_markers_column = "Name",
  unique_name_row_identifier = "filter_image"
)

---

Generates and use a config txt file

Description

When this function run the first time, it will generated a config.txt file in the user working directory. It will import the data config file into the use environment. This data will be used to change the column names of the imported dataset and change the name of the markers that is often incorrectly exported.

Usage

make_run_config(overwrite_config = FALSE, forcePath = NULL)

Arguments

overwrite_config	Boolean, if TRUE the config_drugsens.txt will be overwritten (default is FALSE)
forcePath	String, Define a custom path for the config file

Value

A dataframe/tibble.

Examples

# Generate config in temporary directory
make_run_config(forcePath = tempdir())

---

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

Usage

string_parsing(.data)

Arguments

.data

dataframe with parsed metadata

Value

A dataframe/tibble.

Examples

# Basic example with sample data
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)


# Example with actual data processing
data.parsed <- string_parsing(input_data)

---