Package 'PvSTATEM'

Title: Reading, Quality Control and Preprocessing of MBA (Multiplex Bead Assay) Data
Description: Speeds up the process of loading raw data from MBA (Multiplex Bead Assay) examinations, performs quality control checks, and automatically normalises the data, preparing it for more advanced, downstream tasks. The main objective of the package is to create a simple environment for a user, who does not necessarily have experience with R language. The package is developed within the project of the same name - 'PvSTATEM', which is an international project aiming for malaria elimination.
Authors: Tymoteusz Kwiecinski [aut, cre] , Jakub Grzywaczewski [aut], Mateusz Nizwantowski [aut], Przemyslaw Biecek [ths] , Nuno Sepulveda [ths]
Maintainer: Tymoteusz Kwiecinski <[email protected]>
License: BSD_3_clause + file LICENSE
Version: 0.2.1
Built: 2025-01-27 23:32:46 UTC
Source: CRAN

Help Index

Create a standard curve model for a certain analyte

Description

Create a standard curve model for a certain analyte

Usage

create_standard_curve_model_analyte(
  plate,
  analyte_name,
  data_type = "Median",
  source_mfi_range_from_all_analytes = FALSE,
  detect_high_dose_hook = TRUE,
  ...
)

Arguments

plate

(Plate()) Object of the Plate class
analyte_name

(character(1)) Name of the analyte for which we want to create the model
data_type

(character(1)) Data type of the value we want to use to fit the model - the same datatype as in the plate file. By default, it equals to Median
source_mfi_range_from_all_analytes

(logical(1)) If TRUE, the MFI range is calculated from all analytes; if FALSE, the MFI range is calculated only for the current analyte Defaults to FALSE
detect_high_dose_hook

(logical(1)) If TRUE, the high dose hook effect is detected and handled. For more information, please see the handle_high_dose_hook function documentation.
...

Additional arguments passed to the model

Standard curve samples should not contain na values in mfi values nor in dilutions.

Value

(Model()) Standard Curve model

Generate a Levey-Jennings Report for Multiple Plates.

Description

This function generates a Levey-Jennings report for a list of plates. The report includes layout plot, levey jennings plot, for each analyte and selected dilutions. The report also includes stacked standard curves plot in both monochromatic and color versions for each analyte. The report is generated using the levey_jennings_report_template.Rmd template.

Usage

generate_levey_jennings_report(
  list_of_plates,
  report_title,
  dilutions = c("1/100", "1/400"),
  filename = NULL,
  output_dir = "reports",
  additional_notes = NULL
)

Arguments

list_of_plates

A list of plate objects.
report_title

(character(1)) The title of the report.
dilutions

(character) A character vector specifying the dilutions to be included in the report. Default is c("1/100", "1/400").
filename

(character(1)) The name of the output HTML report file. If not provided or set to NULL, the filename will be based on the first plate name, formatted as ⁠{plate_name}_levey_jennings.html⁠. If the filename does not contain the .html extension, it will be automatically added. Absolute file paths in filename will override output_dir. Existing files at the specified path will be overwritten.
output_dir

(character(1)) The directory where the report will be saved. Defaults to 'reports'. If NULL, the current working directory will be used. Necessary directories will be created if they do not exist.
additional_notes

(character(1)) Additional notes to be included in the report. Markdown formatting is supported. If not provided, the section will be omitted.

Value

A Levey-Jennings report in HTML format.

Examples

output_dir <- tempdir(check = TRUE)

dir_with_luminex_files <- system.file("extdata", "multiplate_lite",
  package = "PvSTATEM", mustWork = TRUE
)
list_of_plates <- process_dir(dir_with_luminex_files,
  return_plates = TRUE, format = "xPONENT", output_dir = output_dir
)
note <- "This is a Levey-Jennings report.\n**Author**: Jane Doe \n**Tester**: John Doe"

generate_levey_jennings_report(
  list_of_plates = list_of_plates,
  report_title = "QC Report",
  dilutions = c("1/100", "1/200"),
  output_dir = tempdir(),
  additional_notes = note
)

Generate a report for a plate.

Description

This function generates a report for a plate. The report contains all the necessary information about the plate, from the general plate parameters, such as examination date, to the breakdown of the analytes' plots. The report is generated using the plate_report_template.Rmd template.

Usage

generate_plate_report(
  plate,
  use_model = TRUE,
  filename = NULL,
  output_dir = "reports",
  counts_lower_threshold = 50,
  counts_higher_threshold = 70,
  additional_notes = NULL
)

Arguments

plate

A plate object.
use_model

(logical(1)) A logical value indicating whether the model should be used in the report.
filename

(character(1)) The name of the output HTML report file. If not provided or equals to NULL, the output filename will be based on the plate name, precisely: ⁠{plate_name}_report.html⁠. By default the plate_name is the filename of the input file that contains the plate data. For more details please refer to Plate.

If the passed filename does not contain .html extension, the default extension .html will be added. Filename can also be a path to a file, e.g. path/to/file.html. In this case, the output_dir and filename will be joined together. However, if the passed filepath is an absolute path and the output_dir parameter is also provided, the output_dir parameter will be ignored. If a file already exists under a specified filepath, the function will overwrite it.
output_dir

(character(1)) The directory where the output CSV file should be saved. Please note that any directory path provided will create all necessary directories (including parent directories) if they do not exist. If it equals to NULL the current working directory will be used. Default is 'reports'.
counts_lower_threshold

(numeric(1)) The lower threshold for the counts plots (works for each analyte). Default is 50.
counts_higher_threshold

(numeric(1)) The higher threshold for the counts plots (works for each analyte). Default is 70.
additional_notes

(character(1)) Additional notes to be included in the report. Contents of this fields are left to the user's discretion. If not provided, the field will not be included in the report.

Value

A report.

Examples

plate_file <- system.file("extdata", "CovidOISExPONTENT_CO_reduced.csv", package = "PvSTATEM")
# a plate file with reduced number of analytes to speed up the computation
layout_file <- system.file("extdata", "CovidOISExPONTENT_CO_layout.xlsx", package = "PvSTATEM")
note <- "This is a test report.\n**Author**: Jane Doe \n**Tester**: John Doe"

plate <- read_luminex_data(plate_file, layout_file, verbose = FALSE)
example_dir <- tempdir(check = TRUE) # a temporary directory
generate_plate_report(plate,
  output_dir = example_dir,
  counts_lower_threshold = 40,
  counts_higher_threshold = 50,
  additional_notes = note
)

Calculate normalised MFI values for a plate

Description

The function calculates the normalised MFI (nMFI) values for each of the analytes in the plate.

The nMFI values are calculated as the ratio of the test samples' MFI values to the standard curve samples with the target dilution.

When nMFI could be used? In general, it is preferred to use Relative Antibody Unit (RAU) values for any analysis. However, it is sometimes impossible to fit a model to the standard curve samples. This may happen if the MFI values of test samples are much higher than the MFI of standard curve samples. Then, the prediction would require significant data extrapolation, which could lead to unreliable results.

In such cases, the nMFI values could be used as a proxy for RAU values if we want, for instance, to account for plate-to-plate variation.

Usage

get_nmfi(
  plate,
  reference_dilution = 1/400,
  data_type = "Median",
  verbose = TRUE
)

Arguments

plate

(Plate()) a plate object for which to calculate the nMFI values
reference_dilution

(⁠numeric(1) or character(1)⁠) the dilution value of the standard curve sample to use as a reference for normalisation. The default is 1/400. It should refer to a dilution of a standard curve sample in the given plate object. This parameter could be either a numeric value or a string. In case it is a character string, it should have format ⁠1/d+⁠, where ⁠d+⁠ is any positive integer.
data_type

(character(1)) type of data for the computation. Median is the default
verbose

(logical(1)) print additional information. The default is TRUE

Value

nmfi (data.frame) a data frame with normalised MFI values for each analyte in the plate and all test samples.

Examples

# read the plate
plate_file <- system.file("extdata", "CovidOISExPONTENT.csv", package = "PvSTATEM")
layout_file <- system.file("extdata", "CovidOISExPONTENT_layout.csv", package = "PvSTATEM")

plate <- read_luminex_data(plate_file, layout_file)

# artificially bump up the MFI values of the test samples (the Median data type is default one)
plate$data[["Median"]][plate$sample_types == "TEST", ] <-
  plate$data[["Median"]][plate$sample_types == "TEST", ] * 10

# calculate the nMFI values
nmfi <- get_nmfi(plate, reference_dilution = 1 / 400)

# we don't do any extrapolation and the values should be comparable across plates
head(nmfi)
# different params
nmfi <- get_nmfi(plate, reference_dilution = "1/50")

Detect and handle the high dose hook effect

Description

Typically, the MFI values associated with standard curve samples should decrease as we dilute the samples. However, sometimes in high dilutions, the MFI presents a non monotonic behavior. In that case, MFI values associated with dilutions above (or equal to) high_dose_threshold should be removed from the analysis.

For more information about this effect please refer to: Namburi, R. P. et. al. (2014) High-dose hook effect.

For the nplr model the recommended number of standard curve samples is at least 4. If the high dose hook effect is detected but the number of samples below the high_dose_threshold is lower than 4, additional warning is printed and the samples are not removed.

The function returns a logical vector that can be used to subset the MFI values.

Usage

handle_high_dose_hook(mfi, dilutions, high_dose_threshold = 1/200)

Arguments

mfi

(numeric())
dilutions

(numeric())
high_dose_threshold

(numeric(1)) MFI values associated with dilutions above this threshold should be checked for the high dose hook effect

Value

sample selector (logical())

Examples

plate_filepath <- system.file(
  "extdata", "CovidOISExPONTENT.csv",
  package = "PvSTATEM", mustWork = TRUE
) # get the filepath of the csv dataset
layout_filepath <- system.file(
  "extdata", "CovidOISExPONTENT_layout.xlsx",
  package = "PvSTATEM", mustWork = TRUE
)
plate <- read_luminex_data(plate_filepath, layout_filepath) # read the data

# here we plot the data with observed high dose hook effect
plot_standard_curve_analyte(plate, "RBD_omicron")

# here we create the model with the high dose hook effect handled
model <- create_standard_curve_model_analyte(plate, "RBD_omicron")

Check validity of given data type

Description

Check if the data type is valid. The data type is valid if it is one of the elements of the VALID_DATA_TYPES vector. The valid data types are:
c(Median, Net MFI, Count, Avg Net MFI, Mean, Peak).

Usage

is_valid_data_type(data_type)

Arguments

data_type

A string representing the data type.

Value

TRUE if the data type is valid, FALSE otherwise.

Check validity of given sample type

Description

Check if the sample type is valid. The sample type is valid if it is one of the elements of the VALID_SAMPLE_TYPES vector. The valid sample types are:

c(ALL, BLANK, TEST, NEGATIVE CONTROL, STANDARD CURVE, POSITIVE CONTROL).

Usage

is_valid_sample_type(sample_type)

Arguments

sample_type

A string representing the sample type.

Value

TRUE if the sample type is valid, FALSE otherwise.

Logistic regression model for the standard curve

Description

The Model class is a wrapper around the nplr model. It allows to predict the RAU (Relative Antibody Unit) values directly from the MFI values of a given sample.

The nplr model is fitted using the formula:

y=B+T−B(1+10b⋅(xmid−x))s,y = B + \frac{T - B}{(1 + 10^{b \cdot (x_{mid} - x)})^s},

where:

  • yy is the predicted value, MFI in our case,

  • xx is the independent variable, dilution in our case,

  • BB is the bottom plateau - the right horizontal asymptote,

  • TT is the top plateau - the left horizontal asymptote,

  • bb is the slope of the curve at the inflection point,

  • xmidx_{mid} is the x-coordinate at the inflection point,

  • ss is the asymmetric coefficient.

This equation is referred to as the Richards' equation. More information about the model can be found in the nplr package documentation.

After the model is fitted to the data, the RAU values can be predicted using the predict method. The RAU value is simply a predicted dilution value (using the standard curve) for a given MFI multiplied by 1,000 000 to have a more readable value. For more information about the differences between dilution, RAU and MFI values, please see the "Normalisation" section in the "Basic PvSTATEM functionalities" vignette.

Public fields

analyte

(character(1))
Name of the analyte for which the model was fitted

dilutions

(numeric())
Dilutions used to fit the model

mfi

(numeric())
MFI values used to fit the model

mfi_min

(numeric(1))
Minimum MFI used for scaling MFI values to the range [0, 1]

mfi_max

(numeric(1))
Maximum MFI used for scaling MFI values to the range [0, 1]

model

(nplr)
Instance of the nplr model fitted to the data

log_dilution

(logical())
Indicator should the dilutions be transformed using the log10 function

log_mfi

(logical())
Indicator should the MFI values be transformed using the log10 function

scale_mfi

(logical())
Indicator should the MFI values be scaled to the range [0, 1]

Active bindings

top_asymptote

(numeric(1))
The top asymptote of the logistic curve

bottom_asymptote

(numeric(1))
The bottom asymptote of the logistic curve

Methods

Public methods

Method new()

Create a new instance of Model R6 class

Usage
Model$new(
  analyte,
  dilutions,
  mfi,
  npars = 5,
  verbose = TRUE,
  log_dilution = TRUE,
  log_mfi = TRUE,
  scale_mfi = TRUE,
  mfi_min = NULL,
  mfi_max = NULL
)
Arguments
analyte

(character(1))
Name of the analyte for which the model was fitted.

dilutions

(numeric())
Dilutions used to fit the model

mfi

MFI (numeric())
values used to fit the model

npars

(numeric(1))
Number of parameters to use in the model

verbose

(logical())
If TRUE prints messages, TRUE by default

log_dilution

(logical())
If TRUE the dilutions are transformed using the log10 function, TRUE by default

log_mfi

(logical())
If TRUE the MFI values are transformed using the log10 function, TRUE by default

scale_mfi

(logical())
If TRUE the MFI values are scaled to the range [0, 1], TRUE by default

mfi_min

(numeric(1))
Enables to set the minimum MFI value used for scaling MFI values to the range [0, 1]. Use values before any transformations (e.g., before the log10 transformation)

mfi_max

(numeric(1))
Enables to set the maximum MFI value used for scaling MFI values to the range [0, 1]. Use values before any transformations (e.g., before the log10 transformation)

Method predict()

Predict RAU values from the MFI values

Usage
Model$predict(mfi, over_max_extrapolation = 0, eps = 1e-06)
Arguments
mfi

(numeric())
MFI values for which we want to predict the RAU values

over_max_extrapolation

(numeric(1))
How much we can extrapolate the values above the maximum RAU value seen in standard curve samples RAUmax\text{RAU}_{max}. Defaults to 0. If the value of the predicted RAU is above RAUmax+over_max_extrapolationRAU_{max} + \text{over\_max\_extrapolation}, the value is censored to the value of that sum.

eps

(numeric(1))
A small value used to avoid numerical issues close to the asymptotes

Returns

(data.frame())
Dataframe with the predicted RAU values for given MFI values The columns are named as follows:

  • RAU - the Relative Antibody Units (RAU) value

  • MFI - the predicted MFI value

Method get_plot_data()

Data that can be used to plot the standard curve.

Usage
Model$get_plot_data()
Returns

(data.frame())
Prediction dataframe for scaled MFI (or logMFI) values in the range [0, 1]. Columns are named as in the predict method

Method print()

Function prints the basic information about the model such as the number of parameters or samples used

Usage
Model$print()

Method clone()

The objects of this class are cloneable with this method.

Usage
Model$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

plate_file <- system.file("extdata", "CovidOISExPONTENT.csv", package = "PvSTATEM")
layout_file <- system.file("extdata", "CovidOISExPONTENT_layout.csv", package = "PvSTATEM")
plate <- read_luminex_data(plate_file, layout_filepath = layout_file)
model <- create_standard_curve_model_analyte(plate, "S2", log_mfi = TRUE)
print(model)

Plate object

Description

A class to represent the luminex plate. It contains information about the samples and analytes that were examined on the plate as well as some additional metadata and batch info

Public fields

plate_name

(character(1))
Name of the plate. Set to the name of the file from which the plate was read.

analyte_names

(character())
Names of the analytes that were examined on the plate.

sample_names

(character())
Names of the samples that were examined on the plate. The order of the samples in this vector is identical with order in the CSV source file.

batch_name

(character(1))
Name of the batch to which the plate belongs.

plate_datetime

(POSIXct())
A date and time when the plate was created by the machine

sample_locations

(character())
Locations of the samples on the plate. This vector is in the same order as the sample_names vector.

sample_types

(character())
Types of the samples that were examined on the plate. The possible values are
c(ALL, BLANK, TEST, NEGATIVE CONTROL, STANDARD CURVE, POSITIVE CONTROL). This vector is in the same order as the sample_names vector.

dilutions

(character())
A list containing names of the samples as keys and string representing dilutions as values. The dilutions are represented as strings. This vector is in the same order as the sample_names vector.

dilution_values

(numeric())
A list containing names of the samples as keys and numeric values representing dilutions as values. It is in the same order as the sample_names vector.

default_data_type

(character(1))
The default data type that will be returned by the get_data method. By default is set to Median.

data

(list())
A list containing dataframes with the data for each sample and analyte. The possible data types - the keys of the list are:
c(Median, Net MFI, Count, Avg Net MFI, Mean, Peak).

In each dataframe, the rows represent samples and the columns represent analytes.

batch_info

(list())
A list containing additional, technical information about the batch.

layout

(character())
A list containing information about the layout of the plate. The layout is read from the separate file and usually provides additional information about the dilutions, sample names, and the sample layout on the actual plate.

blank_adjusted

(logical)
A flag indicating whether the blank values have been adjusted.

Methods

Public methods

Method new()

Method to initialize the Plate object

Usage
Plate$new(
  plate_name,
  sample_names,
  analyte_names,
  batch_name = "",
  plate_datetime = NULL,
  sample_locations = NULL,
  sample_types = NULL,
  dilutions = NULL,
  dilution_values = NULL,
  default_data_type = NULL,
  data = NULL,
  batch_info = NULL,
  layout = NULL
)
Arguments
plate_name

(character(1))
Name of the plate. By default is set to an empty string, during the reading process it is set to the name of the file from which the plate was read.

sample_names

(character())
Names of the samples that were examined on the plate.

analyte_names

(character())
Names of the analytes that were examined on the plate.

batch_name

(character(1))
Name of the batch to which the plate belongs. By default is set to an empty string, during the reading process it is set to the batch field of the plate

plate_datetime

(POSIXct())
Datetime object representing the date and time when the plate was created by the machine.

sample_locations

(character())
Locations of the samples on the plate.

sample_types

(character())
Types of the samples that were examined on the plate. The possible values are
c(ALL, BLANK, TEST, NEGATIVE CONTROL, STANDARD CURVE, POSITIVE CONTROL).

dilutions

(character())
A list containing names of the samples as keys and string representing dilutions as values. The dilutions are represented as strings.

dilution_values

(numeric())
A list containing names of the samples as keys and numeric values representing dilutions as values.

default_data_type

(character(1))
The default data type that will be returned by the get_data method. By default is set to Median.

data

(list())
A list containing dataframes with the data for each sample and analyte. The possible data types - the keys of the list are
c(Median, Net MFI, Count, Avg Net MFI, Mean, Peak). In each dataframe, the rows represent samples and the columns represent analytes.

batch_info

(list())
A list containing additional, technical information about the batch.

layout

(character())
A list containing information about the layout of the plate. The layout is read from the separate file and usually provides additional information about the dilutions, sample names, and the sample layout on the actual plate.

Method print()

Function prints the basic information about the plate such as the number of samples and analytes

Usage
Plate$print(...)
Arguments
...

Additional parameters to be passed to the print function Print the summary of the plate

Method summary()

Function outputs basic information about the plate, such as examination date, batch name, and sample types.

Usage
Plate$summary(..., include_names = FALSE)
Arguments
...

Additional parameters to be passed to the print function Get data for a specific analyte and sample type

include_names

If include_names parameter is TRUE, a part from count of control samples, provides also their names. By default FALSE

Method get_data()

Function returns data for a specific analyte and sample.

Usage
Plate$get_data(
  analyte,
  sample_type = "ALL",
  data_type = self$default_data_type
)
Arguments
analyte

An analyte name or its id of which data we want to extract. If set to 'ALL' returns data for all analytes.

sample_type

is a type of the sample we want to extract data from. The possible values are
c(ALL, BLANK, TEST, NEGATIVE CONTROL, STANDARD CURVE, POSITIVE CONTROL). Default value is ALL.

data_type

The parameter specifying which data type should be returned. This parameter has to take one of values:
c(Median, Net MFI, Count, Avg Net MFI, Mean, Peak). What's more, the data_type has to be present in the plate's data Default value is plate's default_data_type, which is usually Median.

Returns

Dataframe containing information about a given sample type and analyte Get the string representation of dilutions

Method get_dilution()

Function returns the dilution represented as strings for a specific sample type.

Usage
Plate$get_dilution(sample_type)
Arguments
sample_type

type of the samples that we want to obtain the dilution for. The possible values are
c(ALL, BLANK, TEST, NEGATIVE CONTROL, STANDARD CURVE, POSITIVE CONTROL) Default value is ALL.

Returns

A list containing names of the samples as keys and string representing dilutions as values. Get the numeric representation of dilutions

Method get_dilution_values()

Function returns the dilution values for a specific sample type.

Usage
Plate$get_dilution_values(sample_type)
Arguments
sample_type

type of the samples that we want to obtain the dilution values for. The possible values are
c(ALL, BLANK, TEST, NEGATIVE CONTROL, STANDARD CURVE, POSITIVE CONTROL) Default value is ALL.

Returns

A list containing names of the samples as keys and numeric values representing dilutions as values.

Adjust the MFI values by subtracting the background

Method blank_adjustment()

Function adjusts the values of samples (all samples excluding the blanks) by clamping the values to the aggregated value of the BLANK samples for each analyte separately.

The purpose of this operation is to unify the data by clamping values below the background noise. how this method works was inspired by the paper https://doi.org/10.1038/s41598-020-57876-0 which covers the quality control in the MBA.

In short, this operation firstly calculates the aggregate of MFI in the BLANK samples (available methods are: min, max, mean, median) and then replaces all values below this threshold with the threshold value.

Method does not modifies the data of type Count.

This operation is recommended to be performed before any further analysis, but is optional. Skipping it before further analysis is allowed, but will result in a warning.

Usage
Plate$blank_adjustment(threshold = "max", in_place = TRUE)
Arguments
threshold

The method used to calculate the background value for each analyte. Every value below this threshold will be clamped to the threshold value. By default max. Available methods are: min, max, mean, median.

in_place

Whether the method should produce new plate with adjusted values or not, By default TRUE - operates on the current plate.

Method clone()

The objects of this class are cloneable with this method.

Usage
Plate$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

PlateBuilder

Description

This class helps creating the Plate object. It is used to store the data and validate the final fields.

Active bindings

layout_as_vector

Print the layout associated with the plate as a flattened vector of values.

Methods

Public methods

Method new()

Initialize the PlateBuilder object

Usage
PlateBuilder$new(sample_names, analyte_names, batch_name = "", verbose = TRUE)
Arguments
sample_names

  • vector of sample names measured during an examination in the same order as in the data

analyte_names

  • vector of analytes names measured during an examination in the same order as in the data

batch_name

  • name of the batch during which the plate was examined obtained from the plate info. An optional parameter, by default set to "" - an empty string.

verbose

  • logical value indicating whether to print additional information. This parameter is stored as a private attribute of the object and reused in other methods

Method set_sample_locations()

Set the sample types used during the examination

Usage
PlateBuilder$set_sample_locations(sample_locations)
Arguments
sample_locations

vector of sample locations pretty name ie. A1, B2

Method set_dilutions()

Extract and set the dilutions from layout, sample names or use a provided vector of values. The provided vector should be the same length as the number of samples and should contain dilution factors saved as strings

Usage
PlateBuilder$set_dilutions(use_layout_dilutions = TRUE, values = NULL)
Arguments
use_layout_dilutions

logical value indicating whether to use names extracted from layout files to extract dilutions. If set to FALSE the function uses the sample names as a source for dilution

values

a vector of dilutions to overwrite the extraction process

Set and extract sample types from the sample names. Optionally use the layout file to extract the sample types

Method set_sample_types()

Usage
PlateBuilder$set_sample_types(use_layout_types = TRUE, values = NULL)
Arguments
use_layout_types

logical value indicating whether to use names extracted from layout files to extract sample types

values

a vector of sample types to overwrite the extraction process

Method set_sample_names()

Set the sample names used during the examination. If the layout is provided, extract the sample names from the layout file. Otherwise, uses the original sample names from the Luminex file

Usage
PlateBuilder$set_sample_names(use_layout_sample_names = TRUE)
Arguments
use_layout_sample_names

logical value indicating whether to use names extracted from layout files. If set to false, this function only checks if the sample names are provided in the plate

Method set_plate_datetime()

Set the plate datetime for the plate

Usage
PlateBuilder$set_plate_datetime(plate_datetime)
Arguments
plate_datetime

a POSIXct datetime object representing the date and time of the examination

Method set_data()

Set the data used during the examination

Usage
PlateBuilder$set_data(data)
Arguments
data

a named list of data frames containing information about the samples and analytes. The list is named by the type of the data e.g. Median, ⁠Net MFI⁠, etc. The data frames contain information about the samples and analytes The rows are different measures, whereas the columns represent different analytes Example of how data$Median looks like:

Sample Analyte1 Analyte2 Analyte3
Sample1 1.2 2.3 3.4
Sample2 4.5 5.6 6.7
... ... ... ...
Sample96 7.8 8.9 9.0

Method set_default_data_type()

Set the data type used for calculations

Usage
PlateBuilder$set_default_data_type(data_type = "Median")
Arguments
data_type

a character value representing the type of data that is currently used for calculations. By default, it is set to Median

Method set_batch_info()

Set the batch info for the plate

Usage
PlateBuilder$set_batch_info(batch_info)
Arguments
batch_info

a raw list containing metadata about the plate read from the Luminex file

Method set_plate_name()

Set the plate name for the plate. The plate name is extracted from the filepath

Usage
PlateBuilder$set_plate_name(file_path)
Arguments
file_path

a character value representing the path to the file

Method set_layout()

Set the layout matrix for the plate. This function performs basic validation

  • verifies if the plate is a matrix of shape 8x12 with 96 wells

Usage
PlateBuilder$set_layout(layout_matrix)
Arguments
layout_matrix

a matrix containing information about the sample names. dilutions, etc.

Method build()

Create a Plate object from the PlateBuilder object

Usage
PlateBuilder$build(validate = TRUE)

Method clone()

The objects of this class are cloneable with this method.

Usage
PlateBuilder$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Plot counts in a 96-well plate

Description

This function plots counts in a 96-well plate using a colour to represent the count ranges. There is a possibility of plotting exact counts in each well.

If the plot window is resized, it's best to re-run the function to adjust the scaling. Sometimes, when a legend is plotted, the whole layout may be shifted. It's best to stretch the window, and everything will be adjusted automatically.

Usage

plot_counts(
  plate,
  analyte_name,
  plot_counts = TRUE,
  plot_legend = FALSE,
  lower_threshold = 50,
  higher_threshold = 70
)

Arguments

plate

The plate object with the counts data
analyte_name

The name of the analyte
plot_counts

Logical indicating if the counts should be plotted
plot_legend

Logical indicating if the legend should be plotted
lower_threshold

The lower threshold for the counts, it separates green and yellow colours
higher_threshold

The higher threshold for the counts, it separates yellow and red colours

Value

A ggplot object

Examples

plate_filepath <- system.file("extdata", "CovidOISExPONTENT_CO.csv",
  package = "PvSTATEM", mustWork = TRUE
)
layout_filepath <- system.file("extdata", "CovidOISExPONTENT_CO_layout.xlsx",
  package = "PvSTATEM", mustWork = TRUE
)
plate <- read_luminex_data(plate_filepath, layout_filepath)
plot_counts(
  plate = plate, analyte_name = "OC43_NP_NA",
  plot_counts = TRUE, plot_legend = FALSE
)

Plot layout of a 96-well plate

Description

This function plots the layout of a 96-well plate using a colour to represent the sample types.

If the plot window is resized, it's best to re-run the function to adjust the scaling. Sometimes, the whole layout may be shifted when a legend is plotted. It's best to stretch the window, and everything will be adjusted automatically.

Usage

plot_layout(plate, plot_legend = TRUE)

Arguments

plate

The plate object with the layout information
plot_legend

Logical indicating if the legend should be plotted

Value

A ggplot object

Examples

plate_filepath <- system.file("extdata", "CovidOISExPONTENT_CO.csv",
  package = "PvSTATEM", mustWork = TRUE
)
layout_filepath <- system.file("extdata", "CovidOISExPONTENT_CO_layout.xlsx",
  package = "PvSTATEM", mustWork = TRUE
)
plate <- read_luminex_data(plate_filepath, layout_filepath)
plot_layout(plate = plate, plot_legend = TRUE)

Plot Levey-Jennings chart

Description

The function plots a Levey-Jennings chart for the given analyte in the list of plates. The Levey-Jennings chart is a graphical representation of the data that enables the detection of outliers and trends. It is a quality control tool that is widely used in the laboratories across the world.

Usage

plot_levey_jennings(
  list_of_plates,
  analyte_name,
  dilution = "1/400",
  sd_lines = c(1.96),
  data_type = "Median"
)

Arguments

list_of_plates

A list of plate objects for which to plot the Levey-Jennings chart
analyte_name

(character(1)) the analyte for which to plot the Levey-Jennings chart
dilution

(character(1)) the dilution for which to plot the Levey-Jennings chart. The default is "1/400"
sd_lines

(numeric) the vector of coefficients for the standard deviation lines to plot, for example, c(1.96, 2.58) will plot four horizontal lines: mean +/- 1.96sd, mean +/- 2.58sd default is c(1.96) which will plot two lines mean +/- 1.96*sd
data_type

(character(1)) the type of data used plot. The default is "Median"

Value

A ggplot object with the Levey-Jennings chart

Examples

# creating temporary directory for the example
output_dir <- tempdir(check = TRUE)

dir_with_luminex_files <- system.file("extdata", "multiplate_reallife_reduced",
  package = "PvSTATEM", mustWork = TRUE
)
list_of_plates <- process_dir(dir_with_luminex_files,
  return_plates = TRUE, format = "xPONENT", output_dir = output_dir
)
list_of_plates <- rep(list_of_plates, 10) # since we have only 3 plates i will repeat them 10 times

plot_levey_jennings(list_of_plates, "ME", dilution = "1/400", sd_lines = c(0.5, 1, 1.96, 2.58))

Plot MFI value distribution for a given analyte

Description

Plot MFI value distribution for a given analyte

Usage

plot_mfi_for_analyte(
  plate,
  analyte_name,
  data_type = "Median",
  plot_type = "violin",
  scale_y = "log10",
  plot_outliers = FALSE
)

Arguments

plate

A plate object
analyte_name

The analyte to plot
data_type

The type of data to plot. Default is "Median"
plot_type

The type of plot to generate. Default is "violin". Available options are "boxplot" and "violin".
scale_y

What kind of transformation of the scale to apply. By default MFI is presented in a "log10" scale. Available options are described in the documentation of scale_y_continuous undertransform parameter.
plot_outliers

When using "boxplot" type of a plot one can set this parameter to TRUE and display the names of samples for which MFI falls outside the 1.5 IQR interval

Value

A ggplot object

Standard curves

Description

Plot standard curve samples of a plate of a given analyte.

Usage

plot_standard_curve_analyte(
  plate,
  analyte_name,
  data_type = "Median",
  decreasing_rau_order = TRUE,
  log_scale = c("all"),
  plot_line = TRUE,
  plot_blank_mean = TRUE,
  plot_rau_bounds = TRUE,
  plot_legend = TRUE,
  verbose = TRUE
)

Arguments

plate

A plate object
analyte_name

Name of the analyte of which standard curve we want to plot.
data_type

Data type of the value we want to plot - the same datatype as in the plate file. By default equals to ⁠Net MFI⁠
decreasing_rau_order

If TRUE the RAU values are plotted in decreasing order, TRUE by default
log_scale

Which elements on the plot should be displayed in log scale. By default "RAU". If NULL or c() no log scale is used, if "all" or c("RAU", "MFI") all elements are displayed in log scale.
plot_line

If TRUE a line is plotted, TRUE by default
plot_blank_mean

If TRUE the mean of the blank samples is plotted, TRUE by default
plot_rau_bounds

If TRUE the RAU values bounds are plotted, TRUE by default
plot_legend

If TRUE the legend is plotted, TRUE by default
verbose

If TRUE prints messages, TRUE by default

Value

ggplot object with the plot

Examples

path <- system.file("extdata", "CovidOISExPONTENT.csv",
  package = "PvSTATEM", mustWork = TRUE
)
layout_path <- system.file("extdata", "CovidOISExPONTENT_layout.xlsx",
  package = "PvSTATEM", mustWork = TRUE
)
plate <- read_luminex_data(path, layout_filepath = layout_path, verbose = FALSE)
plot_standard_curve_analyte(plate, "Spike_6P", plot_legend = FALSE, data_type = "Median")

Plot standard curve of a certain analyte with fitted model

Description

Function plots the values of standard curve samples and the fitted model.

Usage

plot_standard_curve_analyte_with_model(
  plate,
  model,
  data_type = "Median",
  decreasing_rau_order = TRUE,
  log_scale = c("all"),
  plot_asymptote = TRUE,
  plot_test_predictions = TRUE,
  plot_blank_mean = TRUE,
  plot_rau_bounds = TRUE,
  plot_legend = TRUE,
  verbose = TRUE,
  ...
)

Arguments

plate

Plate object
model

fitted Model object, which predictions we want to plot
data_type

Data type of the value we want to plot - the same datatype as in the plate file. By default equals to Median
decreasing_rau_order

If TRUE the RAU values are plotted in decreasing order, TRUE by default.
log_scale

Which elements on the plot should be displayed in log scale. By default "all". If NULL or c() no log scale is used, if "all" or c("RAU", "MFI") all elements are displayed in log scale.
plot_asymptote

If TRUE the asymptotes are plotted, TRUE by default
plot_test_predictions

If TRUE the predictions for the test samples are plotted, TRUE by default. The predictions are obtained through extrapolation of the model
plot_blank_mean

If TRUE the mean of the blank samples is plotted, TRUE by default
plot_rau_bounds

If TRUE the RAU bounds are plotted, TRUE by default
plot_legend

If TRUE the legend is plotted, TRUE by default
verbose

If TRUE prints messages, TRUE by default
...

Additional arguments passed to the predict function

Value

a ggplot object with the plot

Examples

path <- system.file("extdata", "CovidOISExPONTENT.csv",
  package = "PvSTATEM", mustWork = TRUE
)
layout_path <- system.file("extdata", "CovidOISExPONTENT_layout.xlsx",
  package = "PvSTATEM", mustWork = TRUE
)
plate <- read_luminex_data(path, layout_filepath = layout_path, verbose = FALSE)
model <- create_standard_curve_model_analyte(plate, analyte_name = "Spike_B16172")
plot_standard_curve_analyte_with_model(plate, model, decreasing_rau_order = FALSE)

Standard curve stacked plot for levey-jennings report

Description

Function generates a plot of stacked on top of each other standard curves for a given analyte form a list of plates. The plot is created with the levey-jennings report in mind, but it can be run by itself.

Usage

plot_standard_curve_stacked(
  list_of_plates,
  analyte_name,
  data_type = "Median",
  decreasing_dilution_order = TRUE,
  monochromatic = TRUE,
  legend_type = NULL,
  log_scale = c("all"),
  verbose = TRUE
)

Arguments

list_of_plates

list of Plate objects
analyte_name

Name of the analyte of which standard curves we want to plot.
data_type

Data type of the value we want to plot - the same datatype as in the plate file. By default equals to Median
decreasing_dilution_order

If TRUE the dilution values are plotted in decreasing order, TRUE by default
monochromatic

If TRUE the color of standard curves changes from white (the oldest) to blue (the newest) it helps to observe drift in calibration of the device; otherwise, more varied colours are used, TRUE by default
legend_type

default value is NULL, then legend type is determined based on monochromatic value. If monochromatic is equal to TRUE then legend type is set to date, if it is FALSE then legend type is set to plate_name. User can override this behavior by setting explicitly legend_type to date or plate_name.
log_scale

Which elements on the plot should be displayed in log scale. By default "all". If NULL or c() no log scale is used, if "all" or c("dilutions", "MFI") all elements are displayed in log scale.
verbose

If TRUE prints messages, TRUE by default

Value

ggplot object with the plot

Examples

# creating temporary directory for the example
output_dir <- tempdir(check = TRUE)

dir_with_luminex_files <- system.file("extdata", "multiplate_reallife_reduced",
  package = "PvSTATEM", mustWork = TRUE
)
list_of_plates <- process_dir(dir_with_luminex_files,
  return_plates = TRUE, format = "xPONENT", output_dir = output_dir
)
plot_standard_curve_stacked(list_of_plates, "ME", data_type = "Median", monochromatic = FALSE)

Predict the RAU values from the MFI values

Description

More details can be found here: Model

Usage

## S3 method for class 'Model'
predict(object, mfi, ...)

Arguments

object

(Model()) Object of the Model class
mfi

(numeric()) MFI values for which we want to predict the RAU values Should be in the same scale as the MFI values used to fit the model
...

Additional arguments passed to the method

Value

(data.frame())

Process a dir of files to generate normalised data and reports

Description

The output files will be created alongside their corresponding input files, preserving the directory structure of the input directory unless the flatten_output_dir parameter is set to TRUE.

Usage

process_dir(
  input_dir,
  output_dir = NULL,
  recurse = FALSE,
  flatten_output_dir = FALSE,
  layout_filepath = NULL,
  format = NULL,
  normalisation_types = c("RAU", "nMFI"),
  generate_reports = FALSE,
  merge_outputs = FALSE,
  column_collision_strategy = "intersection",
  return_plates = FALSE,
  dry_run = FALSE,
  verbose = TRUE,
  ...
)

Arguments

input_dir

(character(1)) The directory containing the input files. It may be nested.
output_dir

(character(1)) Optional overwrite directory where the output files should be saved. The default is NULL. By default, the output directory is the same as the input directory.
recurse

(logical(1)) If TRUE, the function will search for files recursively in the input directory. The default is FALSE.
flatten_output_dir

(logical(1)) If TRUE, the output files will be saved in the output directory directly. The default is FALSE.
layout_filepath

(character(1)) The path to the layout file. The default is NULL, and the layout file will have to be determined automatically based on the file name.
format

(character(1)) The format of the Luminex data. The default is NULL, and the format will have to be determined automatically based on the file name. Available options are xPONENT and INTELLIFLEX.
normalisation_types

(character()) A vector of normalisation types to use. The default is c("RAU", "nMFI").
generate_reports

(logical(1)) If TRUE, generate quality control reports for each file. The default is FALSE.
merge_outputs

(logical(1)) If TRUE, merge the outputs of all plates into a single CSV file for each normalisation type. The resulting file will be saved in the output directory with the name ⁠merged_{normalisation_type}_{timestamp}.csv⁠. Example: merged_nMFI_20250115_230735.csv.
column_collision_strategy

(character(1)) A method for handling missing or additional columns when merging outputs. Possible options are union and intersection. The default is intersection.
return_plates

(logical(1)) If TRUE, return a list of processed plates. The default is FALSE.
dry_run

(logical(1)) If TRUE, the function will not process any files but will print the information about the files that would be processed. The default is FALSE.
verbose

(logical(1)) Print additional information. The default is TRUE.
...

Additional arguments to for the process_file function.

Value

If the return_plates parameter is set to TRUE the function returns a list of plates sorted by the plate_datetime (The time of the experiment noted in the csv file) in increasing order (oldest plates first). If the return_plates parameters is set to FALSE the function returns NULL.

Examples

# Select input directory to process
dir <- system.file("extdata", "multiplate_lite", package = "PvSTATEM", mustWork = TRUE)

# Select output directory
output_dir <- tempdir(check = TRUE)

# Process input directory and return plates
plates <- process_dir(dir, return_plates = TRUE, output_dir = output_dir)

Process a file to generate normalised data and reports

Description

Perform process_plate and generate_plate_report for a given plate file. In more detail, this function reads the plate file and calls the process_plate on the processed plate objects across all the normalisation types, including the raw MFI values. If the user has specified the generate_report flag, it will also call the generate_plate_report function generating the quality control report.

Usage

process_file(
  plate_filepath,
  layout_filepath,
  output_dir = "normalised_data",
  format = "xPONENT",
  generate_report = FALSE,
  process_plate = TRUE,
  normalisation_types = c("RAU", "nMFI"),
  verbose = TRUE,
  ...
)

Arguments

plate_filepath

(character(1)) The path to the plate file.
layout_filepath

(character(1)) The path to the layout file.
output_dir

(character(1)) The directory where the output files should be saved. The default is "normalised_data".
format

(character(1)) The format of the Luminex data. The default is "xPONENT". Available options are "xPONENT" and "INTELLIFLEX".
generate_report

(logical(1)) If TRUE, generate a quality control report. The default is FALSE.
process_plate

(logical(1)) If TRUE, process the plate. The default is TRUE. If the value is set to FALSE the function will only read the plate file and return the plate object.
normalisation_types

(character()) A vector of normalisation types to use. The default is c("RAU", "nMFI").
verbose

(logical(1)) Print additional information. The default is TRUE.
...

Additional arguments to for the read_luminex_data function.

Examples

# Select an input csv file for processing and corresponding layout file
plate_file <- system.file("extdata", "CovidOISExPONTENT_CO_reduced.csv", package = "PvSTATEM")
layout_file <- system.file("extdata", "CovidOISExPONTENT_CO_layout.xlsx", package = "PvSTATEM")

example_dir <- tempdir(check = TRUE) # a temporary directory
# create and save dataframe with computed dilutions for all suported noramlization types
process_file(plate_file, layout_file, output_dir = example_dir)

example_dir2 <- tempdir(check = TRUE) # a temporary directory
# process the plate for a specific normalization type
process_file(plate_file, layout_file, output_dir = example_dir2, normalisation_types = c("RAU"))

Process a plate and save output values to a CSV

Description

Depending on the normalisation_type argument, the function will compute the RAU or nMFI values for each analyte in the plate. RAU is the default normalisation type.

The behaviour of the function, in the case of RAU normalisation type, can be summarised as follows:

  1. Adjust blanks if not already done.

  2. Fit a model to each analyte using standard curve samples.

  3. Compute RAU values for each analyte using the corresponding model.

  4. Aggregate computed RAU values into a single data frame.

  5. Save the computed RAU values to a CSV file.

More info about the RAU normalisation can be found in create_standard_curve_model_analyte function documentation create_standard_curve_model_analyte or in the Model reference Model.

In case the normalisation type is nMFI, the function will:

  1. Adjust blanks if not already done.

  2. Compute nMFI values for each analyte using the target dilution.

  3. Aggregate computed nMFI values into a single data frame.

  4. Save the computed nMFI values to a CSV file.

More info about the nMFI normalisation can be found in get_nmfi function documentation get_nmfi.

Usage

process_plate(
  plate,
  filename = NULL,
  output_dir = "normalised_data",
  write_output = TRUE,
  normalisation_type = "RAU",
  data_type = "Median",
  include_raw_mfi = TRUE,
  adjust_blanks = FALSE,
  verbose = TRUE,
  reference_dilution = 1/400,
  ...
)

Arguments

plate

(Plate()) a plate object
filename

(character(1)) The name of the output CSV file with normalised MFI values. If not provided or equals to NULL, the output filename will be based on the normalisation type and the plate name, precisely: ⁠{plate_name}_{normalisation_type}.csv⁠. By default the plate_name is the filename of the input file that contains the plate data. For more details please refer to Plate.

If the passed filename does not contain .csv extension, the default extension .csv will be added. Filename can also be a path to a file, e.g. path/to/file.csv. In this case, the output_dir and filename will be joined together. However, if the passed filepath is an absolute path and the output_dir parameter is also provided, the output_dir parameter will be ignored. If a file already exists under a specified filepath, the function will overwrite it.
output_dir

(character(1)) The directory where the output CSV file should be saved. Please note that any directory path provided will create all necessary directories (including parent directories) if they do not exist. If it equals to NULL the current working directory will be used. Default is 'normalised_data'.
write_output

(logical(1)) whether or not to write the output to a file specified by filename parameter. The default is TRUE.
normalisation_type

(character(1)) type of normalisation to use. Available options are:
c(RAU, nMFI).
data_type

(character(1)) type of data to use for the computation. Median is the default
include_raw_mfi

(logical(1)) include raw MFI values in the output. The default is TRUE. In case this option is TRUE, the output dataframe contains two columns for each analyte: one for the normalised values and one for the raw MFI values. The normalised columns are named as AnalyteName and AnalyteName_raw, respectively.
adjust_blanks

(logical(1)) adjust blanks before computing RAU values. The default is FALSE
verbose

(logical(1)) print additional information. The default is TRUE
reference_dilution

(numeric(1)) target dilution to use as reference for the nMFI normalisation. Ignored in case of RAU normalisation. Default is 1/400. It should refer to a dilution of a standard curve sample in the given plate object. This parameter could be either a numeric value or a string. In case it is a character string, it should have the format ⁠1/d+⁠, where ⁠d+⁠ is any positive integer.
...

Additional arguments to be passed to the fit model function (create_standard_curve_model_analyte)

Value

a data frame with normalised values

Examples

plate_file <- system.file("extdata", "CovidOISExPONTENT_CO_reduced.csv", package = "PvSTATEM")
# a plate file with reduced number of analytes to speed up the computation
layout_file <- system.file("extdata", "CovidOISExPONTENT_CO_layout.xlsx", package = "PvSTATEM")

plate <- read_luminex_data(plate_file, layout_file, verbose = FALSE)

example_dir <- tempdir(check = TRUE) # a temporary directory
# create and save dataframe with computed dilutions
process_plate(plate, output_dir = example_dir)

# process plate without adjusting blanks and save the output to a file with a custom name
process_plate(plate,
  filename = "plate_without_blanks_adjusted.csv",
  output_dir = example_dir, adjust_blanks = FALSE
)


# nMFI normalisation
process_plate(plate,
  output_dir = example_dir,
  normalisation_type = "nMFI", reference_dilution = 1 / 400
)

Read the Intelliflex format data

Description

Read the Intelliflex format data

Usage

read_intelliflex_format(path, verbose = TRUE)

Arguments

path

Path to the INTELLIFLEX file
verbose

Print additional information. Default is TRUE

Read layout data from a file

Description

Read layout data from a file

Usage

read_layout_data(layout_file_path, ...)

Arguments

layout_file_path

Path to the layout file
...

Additional arguments to pass to the underlying read function

Value

A matrix with the layout data. The row names are supposed to be letters A,B,C, etc. The column names are supposed to be numbers 1,2,3, etc.

Read Luminex Data

Description

Reads a file containing Luminex data and returns a Plate object. If provided, can also read a layout file, which usually contains information about the sample names, sample types or its dilutions.

The function is capable of reading data in two different formats:

  • xPONENT

  • INTELLIFLEX which are produced by two different Luminex machines.

Usage

read_luminex_data(
  plate_filepath,
  layout_filepath = NULL,
  format = "xPONENT",
  plate_file_separator = ",",
  plate_file_encoding = "UTF-8",
  use_layout_sample_names = TRUE,
  use_layout_types = TRUE,
  use_layout_dilutions = TRUE,
  default_data_type = "Median",
  sample_types = NULL,
  dilutions = NULL,
  verbose = TRUE
)

Arguments

plate_filepath

Path to the Luminex plate file
layout_filepath

Path to the Luminex layout file
format

The format of the Luminex data. Select from: xPONENT, INTELLIFLEX
plate_file_separator

The separator used in the plate file
plate_file_encoding

The encoding used in the plate file
use_layout_sample_names

Whether to use names from the layout file in extracting sample names.
use_layout_types

Whether to use names from the layout file in extracting sample types. Works only when layout file is provided
use_layout_dilutions

Whether to use dilutions from the layout file in extracting dilutions. Works only when layout file is provided
default_data_type

The default data type to use if none is specified
sample_types

a vector of sample types to use instead of the extracted ones
dilutions

a vector of dilutions to use instead of the extracted ones
verbose

Whether to print additional information and warnings. TRUE by default

Value

Plate file containing the Luminex data

Examples

plate_file <- system.file("extdata", "CovidOISExPONTENT.csv", package = "PvSTATEM")
layout_file <- system.file("extdata", "CovidOISExPONTENT_layout.csv", package = "PvSTATEM")
plate <- read_luminex_data(plate_file, layout_file)

plate_file <- system.file("extdata", "CovidOISExPONTENT_CO.csv", package = "PvSTATEM")
layout_file <- system.file("extdata", "CovidOISExPONTENT_CO_layout.xlsx", package = "PvSTATEM")
# To suppress warnings and additional information use verbose = FALSE
plate <- read_luminex_data(plate_file, layout_file, verbose = FALSE)

Read the xPONENT format data

Description

Read the xPONENT format data

Usage

read_xponent_format(
  path,
  exact_parse = FALSE,
  encoding = "utf-8",
  separator = ",",
  verbose = TRUE
)

Arguments

path

Path to the xPONENT file
exact_parse

Whether to parse the file exactly or not Exact parsing means that the batch, calibration and assay metadata will be parsed as well
encoding

Encoding of the file
separator

Separator for the CSV values
verbose

Whether to print the progress. Default is TRUE

Translate sample names to sample types

Description

Function translates sample names to sample types based on the sample name from Luminex file and the sample name from the layout file, which may not be provided. The function uses regular expressions to match the sample names to the sample types.

It parses the names as follows:

If sample_names or sample_names_from_layout equals to BLANK, BACKGROUND or B, then SampleType equals to BLANK

If sample_names or sample_names_from_layout equals to ⁠STANDARD CURVE⁠, SC, S, contains substring ⁠1/\d+⁠ and has prefix ⁠ ⁠, S_, S , S or CP3, then SampleType equals to ⁠STANDARD CURVE⁠

If sample_names or sample_names_from_layout equals to ⁠NEGATIVE CONTROL⁠, N, or contains substring NEG, then SampleType equals to ⁠NEGATIVE CONTROL⁠

If sample_names or sample_names_from_layout starts with P followed by whitespace, POS followed by whitespace, some sample name followed by substring ⁠1/\d+⁠ SampleType equals to ⁠POSITIVE CONTROL⁠

Otherwise, the returned SampleType is TEST

Usage

translate_sample_names_to_sample_types(
  sample_names,
  sample_names_from_layout = NULL
)

Arguments

sample_names

(character())
Vector of sample names from Luminex file
sample_names_from_layout

(character())
Vector of sample names from Layout file values in this vector may be different than sample_names and may contain additional information about the sample type like dilution. This vector when set has to have at least the length of sample_names.

Value

A vector of valid sample_type strings of length equal to the length of sample_names

Examples

translate_sample_names_to_sample_types(c("B", "BLANK", "NEG", "TEST1"))
translate_sample_names_to_sample_types(c("S", "CP3"))