| Type: | Package |
| Title: | Pharmacokinetic Bioanalysis Experiments Design and Exploration |
| Version: | 0.4.0 |
| Maintainer: | Omar Elashkar <omar.i.elashkar@gmail.com> |
| Description: | Automate pharmacokinetic/pharmacodynamic bioanalytical procedures based on best practices and regulatory recommendations. The package impose regulatory constrains and sanity checking for common bioanalytical procedures. Additionally, 'PKbioanalysis' provides a relational infrastructure for plate management and injection sequence. |
| License: | AGPL (≥ 3) |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.3 |
| Depends: | R (≥ 4.3.0) |
| Imports: | dplyr, tidyselect, stringr (≥ 1.5.1), ggplot2 (≥ 3.5.1), ggforce (≥ 0.4.1), tidyr, glue (≥ 1.6.2), checkmate, shiny, DBI, duckdb (≥ 1.0.0), bslib, bsicons, cli, DiagrammeR, DT, shinyWidgets, shinyjs, units, stats, shinyalert, htmltools, rlang, grDevices, utils, yaml, rhandsontable, methods, reticulate, RTMB, janitor, tibble, xml2, RaMS, data.tree, nloptr, forcats, ggiraph, gt, plotly, pracma, reactable, gtools, ellmer, htmlwidgets, jsonlite, nlme, pmxTools, scales, shinychat, uuid, sortable |
| Suggests: | rxode2, knitr, patchwork, rmarkdown, testthat (≥ 3.0.0) |
| SystemRequirements: | python (>= 3.10) |
| URL: | https://omarashkar.github.io/PKbioanalysis/ |
| BugReports: | https://github.com/OmarAshkar/PKbioanalysis/issues |
| Config/testthat/edition: | 3 |
| Collate: | 'PKbioanalysis-package.R' 'calc.R' 'chrom_anomaly_plots.R' 'chrom_app.R' 'chrom_parsers.R' 'chrom_utils.R' 'generics.R' 'class.R' 'chromatogram.R' 'config.R' 'dil_map.R' 'estim_residuals.R' 'genAI.R' 'gen_studydesign.R' 'generate_test_set.R' 'injec_list.R' 'linearitycheck.R' 'method_file.R' 'peak_integerate.R' 'plate.R' 'plate_expr.R' 'plates_class.R' 'process_chroms.R' 'quant_app.R' 'quant_misc.R' 'quant_object.R' 'quant_parsers.R' 'samples_profiles.R' 'study_app.R' 'suitability_utils.R' 'utils.R' 'writers.R' 'zzz.R' |
| NeedsCompilation: | no |
| Packaged: | 2026-01-14 20:02:19 UTC; omar.elashkar |
| Author: | Omar Elashkar 
[aut, cre] |
| Repository: | CRAN |
| Date/Publication: | 2026-01-14 20:40:07 UTC |
PKbioanalysis: Pharmacokinetic Bioanalysis Experiments Design and Exploration
Description
Automate pharmacokinetic/pharmacodynamic bioanalytical procedures based on best practices and regulatory recommendations. The package impose regulatory constrains and sanity checking for common bioanalytical procedures. Additionally, 'PKbioanalysis' provides a relational infrastructure for plate management and injection sequence.
Author(s)
Maintainer: Omar Elashkar omar.i.elashkar@gmail.com (ORCID)
See Also
Useful links:
Report bugs at https://github.com/OmarAshkar/PKbioanalysis/issues
Utility to extract named xml child to dataframe and rename
Description
Utility to extract named xml child to dataframe and rename
Usage
.child_to_df(xmltree, tag)
Arguments
xmltree |
xml tree |
tag |
xml tag to extract |
Filter peaks to specific RT range
Description
Filter peaks to specific RT range
Usage
.filter_peak(
chrom_res,
transition_id,
samples_ids = NULL,
peak_start,
peak_end,
smoothed = FALSE
)
Arguments
chrom_res |
ChromRes object |
transition_id |
Transition ID |
samples_ids |
Sample ID. If NULL, all samples will be used |
peak_start |
Minimum RT value. If NULL, all RT values will be used |
peak_end |
Maximum RT value. If NULL, all RT values will be used |
smoothed |
Logical. If TRUE, use smoothed chromatogram. Default is FALSE |
Value
Dataframe with RT, intensity and sample_id
Create a new methods database from a YAML file
Description
Create a new methods database from a YAML file
Usage
.parse_cmpds(path)
Arguments
path |
Path to the YAML file containing method information |
Parse targetlynx CSV file
Description
Parse targetlynx CSV file
Usage
.parse_tlynx_csv(filepath, first_cmpd)
Arguments
filepath |
path to the targetlynx CSV file |
first_cmpd |
name of the first compound, if not provided it will be extracted from the file |
Value
a list of data frames, each containing the data for a compound
Parse targetlynx
Description
Peaks must be integrated and checked
Usage
.parse_tlynx_xml(xmlpath, drop_prefix = FALSE)
Arguments
xmlpath |
xml targetlynx output |
drop_prefix |
logical. If TRUE, drop the prefix from the sample name |
Value
A list of Dataframe with each compound
Subsetting method for MultiPlate
Description
Subsetting method for MultiPlate
Usage
## S4 method for signature 'MultiPlate'
x[[i, j, ...]]
Arguments
x |
MultiPlate object |
i |
index |
j |
index |
... |
additional arguments |
Value
PlateObj object
Add double blank (DB) to a plate
Description
Add double blank (DB) to a plate
Usage
add_DB(plate, analytical = FALSE, group = NA)
Arguments
plate |
PlateObj object |
analytical |
logical. If TRUE, the blank is bioanalytical, if FALSE it is analytical. |
group |
A string for bioanalytical group. |
Value
PlateObj
Examples
plate <- generate_96() |>
add_DB()
Add dilution quality control (DQC) to the plate
Description
Add dilution quality control (DQC) to the plate
Usage
add_DQC(plate, conc, fac, rep = 5, group = NA)
Arguments
plate |
PlateObj object |
conc |
numeric. Concentration of the DQC well. |
fac |
numeric. Factor of the DQC well. |
rep |
numeric. Number of replicates. Default is 5. |
group |
A string for bioanalytical group. The current implementation does not check ULOQ or LLOQ boundaries. |
Add quality control samples to the plate
Description
A function to add QCs to plate. This function assumes adherence to ICH guideline M10 on bioanalytical method validation and study sample analysis Geneva, Switzerland (2022). If you are not following this guideline, you can set 'reg = TRUE' to ignore the restrictions.
Usage
add_QC(
plate,
lqc_conc,
mqc_conc,
hqc_conc,
extra = NULL,
n_qc = 3,
qc_serial = TRUE,
reg = TRUE,
group = NA
)
Arguments
plate |
PlateObj object |
lqc_conc |
low quality control concentration |
mqc_conc |
medium quality control concentration |
hqc_conc |
high quality control concentration |
extra |
numeric vector of extra QC concentrations. |
n_qc |
number of QC sets. Default is 3 |
qc_serial |
logical. If TRUE, QCs are placed serially |
reg |
logical. Indicates if restrictions should not be applied to the QC samples. Default is TRUE |
group |
A string for bioanalytical group. |
Value
PlateObj
Add blank to the plate Can be either double blank (DB), CS0IS+ or CS+IS0
Description
Add blank to the plate Can be either double blank (DB), CS0IS+ or CS+IS0
Usage
add_blank(plate, IS = TRUE, analyte = FALSE, analytical = FALSE, group = NA)
Arguments
plate |
PlateObj object |
IS |
logical. If TRUE, add IS to the well. |
analyte |
logical. If TRUE, add analyte to the well. |
analytical |
logical. If FALSE, the blank is analytical, if TRUE it is bioanalytical. |
group |
A string for bioanalytical group. |
Value
PlateObj
Add calibration curve to the plate
Description
Add calibration curve to the plate
Usage
add_cs_curve(plate, plate_std, rep = 1, group = NA)
Arguments
plate |
PlateObj |
plate_std |
character |
rep |
numeric. Number of technical replicates. Default is 1. |
group |
A string for bioanalytical group. |
Value
PlateObj
Examples
plate <- generate_96() |>
add_cs_curve(c(1, 3, 5, 10, 50, 100, 200))
plot(plate)
Add samples to plate with pharmacokinetic attributes
Description
Add samples to plate with pharmacokinetic attributes
Usage
add_samples(plate, samples, prefix = NA, dil = NA, group = NA, rep = 1)
Arguments
plate |
PlateObj |
samples |
A vector representing samples names. Must be unique. |
prefix |
A prefix to be added before samples names. Default is "Sub". |
dil |
A vector representing samples' dilution factor. Must be same length as samples. |
group |
A vector representing samples' bioanalytical group. Must be same length as samples. |
rep |
Number of technical replicates for each combination. Default is 1. |
Details
final name will be of form. Prefix-SampleName-Time-Concentration-Factor samples must be a unique vector and did not exist in the plate before. Time is either a vector or a single value. If it is a vector, it will be repeated for each sample. Conc, dil, factor and dose are either a vector or a single value. If it is a vector, it must be the corrosponding length of samples.
Allowed routes are "IV", "IM", "IP", "SC", "PO", "INH" which are short for Intravenous, Intramuscular, Intraperitoneal, Subcutaneous, Per Os (oral), Inhalation.
Factor is an arbitrary factor used in the design like food vs fasted, healthy vs diseased, positive genotype ... etc.
Value
PlateObj
Examples
plate <- generate_96() |>
add_samples(paste0("T", 1:12))
Add samples from the sample log to the plate
Description
Add samples from the sample log to the plate
Usage
add_samples_db(plate, logIds, dil = 1, namestyle = 1, group = NA)
Arguments
plate |
PlateObj |
logIds |
A vector of log IDs from the sample log. |
dil |
A vector with length corresponding number of logIds. See details. |
namestyle |
A numeric value indicating the naming style. 1 for long names, 2 for short names. |
group |
A string for bioanalytical group. |
Details
This function will retrieve sample information from the sample log database using the provided log IDs. It constructs sample names based on the specified naming style and adds them to the plate. The 'dil' parameter allows specifying dilution factors for each sample, which will be appended to the sample names. If a single dilution factor is provided, it will be applied to all samples.
Value
PlateObj
Add samples from the sample log to the plate with multiplication
Description
Add samples from the sample log to the plate with multiplication
Usage
add_samples_db2(plate, logIds, dil = c(1, 1), namestyle = 1, group = NA)
Arguments
plate |
PlateObj |
logIds |
A vector of log IDs from the sample log. |
dil |
A vector with length corresponding number of repeats. See details. |
namestyle |
A numeric value indicating the naming style. 1 for long names, 2 for short names. |
group |
A string for bioanalytical group. |
Details
This function is wrapper around 'add_samples_db()' that allows for quick replication of samples by dilution factor vector. For instance, it dil = c(1,10), the samples will repeated twice with one fold and 10 fold dilution factor each time.
Add suitability sample to the plate
Description
Add suitability sample to the plate
Usage
add_suitability(plate, conc, label = "suitability", group = NA)
Arguments
plate |
PlateObj object. |
conc |
numeric. Concentration of the suitability well. |
label |
character. Label for the suitability well. Default is "suitability". |
group |
A string for bioanalytical group. |
Value
PlateObj
gt table of areas
Description
gt table of areas
Usage
area_report.PeakRes(
peaks_res,
normalize = TRUE,
blanks = TRUE,
analytes = TRUE,
standards = TRUE,
QCs = TRUE,
compounds = NULL
)
Arguments
peaks_res |
PeakRes object |
normalize |
logical. If TRUE, normalize the peak area by the IS area. |
blanks |
logical. If TRUE, include blanks |
analytes |
logical. If TRUE, include analytes |
standards |
logical. If TRUE, include standards |
QCs |
logical. If TRUE, include QCs |
compounds |
numeric vector of compound numbers to include. If NULL, include all compounds |
Create Injection Sequence
Description
Create Injection Sequence
Create Injection Sequence from PlateObj (Single Plate)
Create Injection Sequence from MultiPlate (Multiple Plates)
Usage
build_injec_seq(
plate,
method,
rep_DB = 2,
rep_ISblank = 1,
rep_suitability = 1,
rep_blank = 2,
repeat_std = 1,
repeat_qc = 1,
repeat_analyte = 1,
repeat_dqc = 1,
n_explore = 0,
blank_after_top_conc = TRUE,
blank_at_end = TRUE,
blank_every_n = NULL,
injec_vol,
descr = "",
prefix = Sys.Date(),
suffix = "1",
tray = 1,
conc_df = NULL,
grouped = TRUE
)
## S4 method for signature 'PlateObj'
build_injec_seq(
plate,
method,
rep_DB = 2,
rep_ISblank = 1,
rep_suitability = 1,
rep_blank = 2,
repeat_std = 1,
repeat_qc = 2,
repeat_analyte = 1,
repeat_dqc = 1,
n_explore = 0,
blank_after_top_conc = TRUE,
blank_at_end = TRUE,
blank_every_n = NULL,
injec_vol,
descr = "",
prefix = Sys.Date(),
suffix = "1",
tray = 1,
conc_df = NULL,
grouped = TRUE
)
## S4 method for signature 'MultiPlate'
build_injec_seq(
plate,
method,
rep_DB = 2,
rep_ISblank = 1,
rep_suitability = 1,
rep_blank = 2,
repeat_std = 1,
repeat_qc = 1,
repeat_analyte = 1,
repeat_dqc = 1,
n_explore = 0,
blank_after_top_conc = TRUE,
blank_at_end = TRUE,
blank_every_n = NULL,
injec_vol,
descr = "",
prefix = Sys.Date(),
suffix = "1",
tray = 1,
conc_df = NULL,
grouped = TRUE
)
Arguments
plate |
PlateObj object |
method |
choose method from database |
rep_suitability |
Number of re-injections for suitability vial. |
repeat_std |
number of re-injections for calibration standards. Default is 1. |
repeat_qc |
number of re-injections for QC wells. Default is 1 |
repeat_analyte |
number of re-injections for unknown samples. Default is 1 |
n_explore |
A number of exploratory samples to be injected at the top of the entire sequence. Default is 0 |
blank_after_top_conc |
If TRUE, adding blank after high concentrations of standards and QCS. |
blank_at_end |
If True, adding blank at the end of queue. |
blank_every_n |
If no QCs, frequency of injecting blanks between analytes. |
injec_vol |
volume of injection in micro liters. |
descr |
Run description. |
prefix |
string at the beginning of the filename. Default is today's date. |
suffix |
string to be added to the end of the filename. Default is "1". |
tray |
Location in sample manager. |
conc_df |
data.frame matching compound name to a scaling factor. Maximum 20 compounds allowed. |
Details
n_explore controls if exploratory samples are to be injected. A random sample from each CS and QC group will be sampled along with 1 blank sample.
Value
InjecListObj object
InjecListObj object
InjecListObj object
Calculate Summary Statistics for Each Concentration Level For Either Concentration, Area, or Area Ratio
Description
Calculate Summary Statistics for Each Concentration Level For Either Concentration, Area, or Area Ratio
Usage
calc_var_summary(
df,
col = "conc",
acc_cutoff = 0.2,
dev_cutoff = 0.2,
type = "QC"
)
Arguments
df |
Data frame with columns: stdconc (standardized concentration), conc (concentration), area (peak area), area_ratio (area ratio) |
col |
Column to calculate summary for ("conc", "area", or "area_ratio") |
acc_cutoff |
Accuracy threshold (default is 20%) for concentration vs standard concentration |
dev_cutoff |
Deviation threshold (default is 20%) for concentration vs standard concentration |
type |
Type of samples to include ("Standard", "QC", "DQC") |
Author(s)
Omar I. Elashkar
Check Matching of Compound and Transitions in chrom_res and method database
Description
Check Matching of Compound and Transitions in chrom_res and method database
Usage
check_chrom_cmpds(chrom_res, method_id)
Arguments
chrom_res |
ChromRes object |
method_id |
Method ID in the method database This is important to give no error before merging quantification results to ensure consistency. |
Value
TRUE if all compounds and transitions match, otherwise FALSE
chrom_apps
Description
This function creates a shiny app for peak integration.
Usage
chrom_app()
Clear Managed Python Environments
Description
Removes the ephemeral Python environments and cached artifacts managed by reticulate and uv.
Usage
clear_python_deps()
Create Sample List with rigorous design
Description
Create Sample List with rigorous design
Usage
combine_injec_lists(
sample_lists,
n_equi = 10,
equi_pos,
equi_prefix = Sys.Date(),
equi_suffix = "equi",
equi_injec_vol = 0.5
)
Arguments
sample_lists |
a list of sample lists |
n_equi |
number of equilibriation injections |
equi_pos |
position of equilibriation injections. For format check details |
equi_prefix |
prefix for equilibriation injections |
equi_suffix |
suffix for equilibriation injections |
equi_injec_vol |
volume of equilibriation injection |
Details
The equi_pos format will be Row:Column format. E.g: "A,1"
Value
InjecListObj object
Combine plates in MultiPlate object
Description
Combine plates in MultiPlate object
Usage
combine_plates(plates)
Arguments
plates |
list of PlateObj objects |
Value
MultiPlate object
Configure suitability runs
Description
Configure suitability runs by specifying vial position and range of runs to include.
Usage
config_suitability(quantres, vial_pos, start = NULL, end = NULL)
Arguments
quantres |
QuantRes object |
vial_pos |
Vial position to use for suitability (e.g., "2:H,9") |
start |
Start position (1-based index) of runs to include. If NULL, starts from the first run. |
end |
End position (1-based index) of runs to include. If NULL, ends at the last run. |
Value
Updated QuantRes object with suitability configuration.
Create a new study in the database
Description
Create a new study in the database
Usage
create_new_study(df)
Arguments
df |
A data frame with one row containing study details: type, title, description, pkstudy (logical), subject_type |
Value
A data frame with the created study details including generated id, start_date, and status
Calculate Coefficient of variation
Description
Calculate Coefficient of variation
Usage
cv(x, percent = TRUE)
Arguments
x |
vector |
percent |
To return the value as percentage |
Details
A simple calculation of the coefficient of variation (CV) is done as the standard deviation divided by the mean. By default, the result is in percentage.
Value
numeric
Download sample list from database to local spreadsheet with vendor specific format
Description
Download sample list from database to local spreadsheet with vendor specific format
Usage
download_sample_list(sample_list, vendor)
Arguments
sample_list |
dataframe of sample list either from db or from write_injec_seq |
vendor |
currently only 'masslynx', 'masshunter' and 'analyst' are supported |
Details
For all current vendors, the exported format will be in csv format, compatible with the respective software.
Value
dataframe
Estimate Dilution Limit Based on Additive and Proportional Errors and LLOQ
Description
Estimate Dilution Limit Based on Additive and Proportional Errors and LLOQ
Usage
estim_dil_limit(add_err, prop_err, lloq)
Arguments
add_err |
Additive error (constant) |
prop_err |
Proportional error (CV) |
lloq |
Lower limit of quantification |
Author(s)
Omar I. Elashkar
Examples
estim_dil_limit(add_err=0.1, prop_err=0.1, lloq=1)
estim_dil_limit(add_err=1, prop_err=0.1, lloq=55)
Estimate LLOQ From Existing Additive and Proportional errors
Description
Estimate LLOQ From Existing Additive and Proportional errors
Usage
estim_lloq(add_err = 0.04, prop_err = 0.05, cv_lloq = 0.2, cv_lqc = 0.15)
Arguments
add_err |
Additive error (constant) |
prop_err |
Proportional error (CV) |
cv_lloq |
Maximum coefficient of variation at LLOQ |
cv_lqc |
Maximum coefficient of variation at LQC A method to estimate LLOQ from existing additive and proportional errors. The function does inequality constrained optimization to find the LLOQ. |
Author(s)
Omar I. Elashkar
Export Expected RT
Description
Export expected RT values for each peak in the chromatogram.
Usage
export_integration(chrom_res, path)
Arguments
chrom_res |
ChromRes object |
path |
Path to save the file |
Export run
Description
Export run
Usage
export_run(peaks_res, path)
Arguments
peaks_res |
PeakRes object |
path |
path to save csv |
Extract Peak Boundaries
Description
Extract peak boundaries for a given compound ID
Usage
extract_peak_bounds(chrom_res, compound_id)
Arguments
chrom_res |
ChromRes object |
compound_id |
Compound ID The function automatically priortizes observed peak boundaries (manual integration) over expected ones. If observed boundaries are not available, it falls back to expected boundaries. |
Value
Dataframe with compound_id, min, max
Filling orientation of the plate
Description
This function sets the filling scheme of the plate. The filling scheme is used to determine the order in which the samples are filled in the plate. The default filling scheme is horizontal, which means that the samples are filled from left to right and top to bottom. The vertical filling scheme means that the samples are filled from top to bottom and left to right.
Usage
fill_scheme(
plate,
fill = "h",
tbound = "A",
bbound = "H",
lbound = 1,
rbound = 12
)
Arguments
plate |
PlateObj |
fill |
character. Filling scheme. Either "h" for horizontal, "v" for vertical. |
tbound |
character. Top bound of the filling scheme. Default is "A" |
bbound |
character. Bottom bound of the filling scheme. Default is "H" |
lbound |
numeric. Left bound of the filling scheme. Default is 1 |
rbound |
numeric. Right bound of the filling scheme. Default is 12 |
Value
PlateObj
title Filter Chromatogram Peaks
Description
This function filters chromatogram peaks based on transition ID and sample ID.
Usage
filter_chrom(
chrom_res,
transitions_ids = NULL,
samples_id = NULL,
cmpd_ids = NULL
)
Arguments
chrom_res |
ChromRes object |
transitions_ids |
Vector of transition IDs to filter. If NULL, all transitions are returned. |
samples_id |
Sample ID to filter. |
cmpd_ids |
Compound ID to filter. It must be numeric. If NULL, all compounds are returned. |
Estimate Additive and proportional errors from calibration data
Description
Estimate Additive and proportional errors from calibration data
Usage
fit_var(
data,
level = 0.95,
method = "nlminb",
bootstrap = FALSE,
n_boot = 1000
)
Arguments
data |
Data frame with columns: conc (concentration), stdconc (standardized concentration, e.g. conc/LLOQ) |
level |
Confidence level for the CI (default is 0.95) |
method |
Optimization method (default is "nlminb") |
bootstrap |
Logical indicating whether to perform bootstrap (default is TRUE) |
n_boot |
Number of bootstrap samples (default is 1000) |
Author(s)
Omar I. Elashkar
Format and print the results of fit_var
Description
Format and print the results of fit_var
Usage
formated_print(x, digits = 3)
Arguments
x |
Data frame with results |
digits |
Number of digits to display |
Author(s)
Omar I. Elashkar
Generate 96 well plate
Description
Generate 96 well plate
Usage
generate_96(descr = "", start_row = "A", start_col = 1)
Arguments
descr |
plate description. |
start_row |
A letter corresponding to empty rows in a 96 well plate. Default is A. |
start_col |
A number indicating a column number to start with, given the start row. Default is 1. Generate a typical 96 well plate. User need to specify the empty rows which a going to be used across the experiment. |
Value
PlateObj
Examples
plate <- generate_96()
plot(plate)
plate <- generate_96("calibration", start_row = "C", start_col = 11)
plot(plate)
Find Compound ID from compound Name
Description
This function returns the compound ID
Usage
get_compound_ID(chrom_res, compound_name)
Arguments
chrom_res |
ChromRes object |
compound_name |
Compound Name |
Find Sample ID from sample Name
Description
This function returns the sample ID
Usage
get_sample_ID(chrom_res, sample_name)
Arguments
chrom_res |
ChromRes object |
sample_name |
Sample Name |
Find sample names for all samples
Description
Find sample names for all samples
Usage
get_sample_names(chrom_res)
Arguments
chrom_res |
ChromRes object |
Value
data.frame with sample and sample_id
check if default expected RT is set for a compound
Description
check if default expected RT is set for a compound
Usage
has_default_RT(chrom_res, compound_id)
Arguments
chrom_res |
ChromRes object |
compound_id |
Compound ID |
Install Python dependencies for PKbioanalysis
Description
Install Python dependencies for PKbioanalysis
Usage
install_py_dep(..., envname = "PKbioanalysis")
Arguments
... |
Additional arguments passed to reticulate::py_install |
envname |
Name of the virtual environment to create/use. Default is "PKbioanalysis" |
Value
None
integrate Peak with trapzoid method given start and end
Description
integrate Peak with trapzoid method given start and end
Usage
integrate(chrom_res, compound_id, samples_ids, smoothed = TRUE)
Arguments
chrom_res |
ChromRes object. Must have observed RT values |
compound_id |
Compound ID |
samples_ids |
Sample ID. If NULL, all samples will be used |
smoothed |
Logical. If TRUE, use smoothed chromatogram. Default is TRUE |
Check if peak was integrated for a specific compound
Description
Check if peak was integrated for a specific compound
Usage
is_integrated(chrom_res, compound_id, sample_id = NULL)
## S4 method for signature 'ChromRes'
is_integrated(chrom_res, compound_id, sample_id = NULL)
## S4 method for signature 'ChromResBase'
is_integrated(chrom_res, compound_id, sample_id = NULL)
Arguments
chrom_res |
ChromRes or ChromResBase object |
compound_id |
Compound ID |
sample_id |
Sample ID. If NULL, all samples are checked |
Value
logical
Examples
## Not run:
lapply(1:10, \(x) is_integrated(chrom_res, sample_id = 1, compound_id = 1))
## End(Not run)
Return an indicator if the chromatogram is smoothed
Description
Return an indicator if the chromatogram is smoothed
Usage
is_smoothed(chrom_res)
Arguments
chrom_res |
ChromRes object |
Length method for MultiPlate
Description
Length method for MultiPlate
Usage
## S4 method for signature 'MultiPlate'
length(x)
Arguments
x |
MultiPlate object |
Value
number of plates
Create a calibration study with calibration standards and QCs
Description
Create a calibration study with calibration standards and QCs
Usage
make_calibration_study(
plate,
plate_std,
lqc_conc = NULL,
mqc_conc = NULL,
hqc_conc = NULL,
n_qc = NULL,
qc_serial = FALSE,
n_CS0IS0 = 1,
n_CS0IS1 = 2,
n_CS1IS0 = 1,
group = NA
)
Arguments
plate |
PlateObj object |
plate_std |
vector of calibration standards |
lqc_conc |
LQC concentration |
mqc_conc |
MQC concentration |
hqc_conc |
HQC concentration |
n_qc |
number of QC sets |
qc_serial |
logical. If TRUE, QCs are placed serially |
n_CS0IS0 |
number of CS0IS0 (double) blanks |
n_CS0IS1 |
number of CS0IS1 blanks |
n_CS1IS0 |
number of CS1IS0 blanks |
group |
A string for bioanalytical group. |
Value
PlateObj
Create a metabolic study layout
Description
Create a metabolic study layout
Usage
make_metabolic_study(
study = "Metabolic Study",
cmpds,
time_points = c(0, 5, 10, 15, 30, 45, 60, 75, 90, 120),
dose = NA,
n_NAD = 3,
n_noNAD = 2
)
Arguments
study |
study name |
cmpds |
vector of compounds, including any standards |
time_points |
vector of time points |
dose |
dose amount. Default is NA |
n_NAD |
number of NAD positive samples. Default is 3 |
n_noNAD |
number of NAD negative samples. Default is 2 |
Details
Note that this function does not require plate object. It will create a plate object automatically and return MultiPlate object
Value
MultiPlate object
Set plate description
Description
Set plate description
Usage
plate_metadata(plate, descr)
Arguments
plate |
PlateObj |
descr |
character. Description of the plate |
Value
PlateObj
Plot the design of the plate
Description
Plot the design of the plate
Usage
plate_tree(plate, plot = TRUE)
Arguments
plate |
PlateObj object |
plot |
logical. If TRUE, plot the tree |
Value
data.tree Node object or DiagrammeR object plot_tree will focus only on bioanalytical vial types, namely blanks, analytes, standards, QCs. The tree order will be plate_id, then group, then vial type, then entity, then number of technical replicates.
Plotting 96 well plate
Description
Plotting 96 well plate
Usage
## S3 method for class 'PlateObj'
plot(
x,
color = "conc",
Instrument = "",
caption = "",
label_size = 1,
transform_dil = FALSE,
watermark = "auto",
layoutOverlay = FALSE,
path = NULL,
...
)
Arguments
x |
PlateObj |
color |
character. Coloring variable. Choices: "conc", "group", "dil", "study", "time", "factor", "samples", "arm", "sex", "dose", "route", "matrix". Default is "conc" |
Instrument |
A string placed at subtitle |
caption |
A string place at plate caption |
label_size |
numeric. Size of the label. Default is 15 |
transform_dil |
logical. If TRUE, transform the dilution factor to the label |
watermark |
character. If "auto", a watermark is added to the plot. If "none", no watermark is added. Default is "auto" |
layoutOverlay |
logical. If TRUE, overlay the plot layout. Default is FALSE |
path |
If not null, must be a path to save plate image |
... |
additional arguments passed to ggplot2::ggsave |
Value
ggplot object
Examples
plate <- generate_96("new_plate", "C", 11) |>
add_blank(IS = FALSE, analyte = FALSE) |>
add_blank(IS = TRUE, analyte = FALSE) |>
add_samples(c(
"RD_per1", "RD_in1", "RD_T30", "RD_T60", "RD_T90", "RD_per2", "RD_in2",
"EE_in0", "EE_T30", "EE_in30", "EE_T60", "EE_in60", "EE_T90", "EE_in90"
))
plot(plate)
Plotting RT intervals of chromatogram
Description
Plotting RT intervals of chromatogram
Usage
plot_RT.ChromRes(chrom_res)
Arguments
chrom_res |
ChromRes object |
Plot RT
Description
Plot RT
Usage
plot_RT.PeakRes(
peaks_res,
normalize = TRUE,
blanks = TRUE,
analytes = TRUE,
standards = TRUE,
QCs = TRUE,
facet = FALSE,
compounds = NULL
)
Arguments
peaks_res |
PeakRes object |
normalize |
logical. If TRUE, normalize the peak area by the IS area. |
blanks |
logical. If TRUE, plot blanks |
analytes |
logical. If TRUE, plot analytes |
standards |
logical. If TRUE, plot standards |
QCs |
logical. If TRUE, plot QCs |
facet |
logical. If TRUE, facet by compound name |
compounds |
numeric vector of compound numbers to include. If NULL, include all compounds |
Value
ggplot2 object
Plot Chromatogram per Sample for Selected transitions
Description
This function plots chromatograms for selected transitions per sample.
Usage
plot_chrom(
chrom_res,
ncol = 2,
transitions_ids = NULL,
sample_id,
integrated = FALSE,
show_RT = FALSE,
smoothed = FALSE
)
Arguments
chrom_res |
ChromRes object |
ncol |
Number of columns for facet_wrap. If 0, the chromatograms are overlayed in a single plot. |
transitions_ids |
Vector of transition IDs to plot. If NULL, all transitions are plotted. |
sample_id |
Sample ID to plot. |
integrated |
Boolean to show integrated area overlayed |
show_RT |
Boolean to show RT values |
smoothed |
Boolean to show smoothed chromatogram |
Examples
## Not run:
path <- system.file("extdata", "waters_raw_ex", package="PKbioanalysis")
main <- read_chrom(path, method = 1)
plot_chrom(main, ncol = 2, transitions_ids = c(18,19,20), sample_id = 3)
plot_chrom(main, ncol = 3, transitions_ids = c(18,19,20), sample_id = 3)
plot_chrom(main, ncol = 1, transitions_ids = c(18,19,20), sample_id = 3)
plot_chrom(main, ncol = NULL, transitions_ids = c(18,19,20), sample_id = 3)
## End(Not run)
Plot peak areas
Description
Plot peak areas
Usage
plot_peak_areas.PeakRes(
peaks_res,
normalize = TRUE,
blanks = TRUE,
compounds = NULL,
analytes = TRUE,
standards = TRUE,
QCs = TRUE,
type = "bar"
)
Arguments
peaks_res |
PeakRes object |
normalize |
logical. If TRUE, normalize the peak area by the IS area. |
blanks |
logical. If TRUE, plot blanks |
compounds |
numeric vector of compound numbers to include. If NULL, include all compounds |
analytes |
logical. If TRUE, plot analytes |
standards |
logical. If TRUE, plot standards |
QCs |
logical. If TRUE, plot QCs |
type |
character. Either "bar" or "line" |
Value
ggplot2 object
Plot Relationship Between Concentration and CV/SD
Description
Plot Relationship Between Concentration and CV/SD
Usage
plot_var_pattern(df, title = "")
Arguments
df |
Data frame with columns: stdconc (standardized concentration), cv (coefficient of variation), sdev (standard deviation), Type (e.g., "Estimated", "Observed") |
title |
Plot title |
Author(s)
Omar I. Elashkar
Precision per vial
Description
Precision per vial
Usage
precision_per_vial(peaks_res, suitability = FALSE)
Arguments
peaks_res |
PeakRes object |
suitability |
logical. If TRUE, suitability samples are ignored |
Value
ggplot2 object
Filter data
Description
Filter data
Usage
prefilter_precision_data(
x,
type,
acc_cutoff = 0.2,
dev_cutoff = 0.2,
compound_id = NULL
)
## S4 method for signature 'QuantRes'
prefilter_precision_data(
x,
type,
acc_cutoff = 0.2,
dev_cutoff = 0.2,
compound_id = NULL
)
## S4 method for signature 'data.frame'
prefilter_precision_data(x, type, acc_cutoff = 0.2, dev_cutoff = 0.2)
Arguments
x |
Dataframe or QuantRes Object |
type |
QC, DQC, or Standard |
acc_cutoff |
Accuracy cutoff. 20% by default |
dev_cutoff |
Deviation cutoff. 20% by default |
compound_id |
Compound ID to filter. If NULL, all compounds are considered |
Value
Filtered data
Author(s)
Omar I. Elashkar
Quantification App
Description
This function creates a shiny app for quantification after peak integration
Usage
quant_app()
Read Chromatogram Files
Description
This function reads chromatogram files from a directory and returns a data frame with the chromatogram data.
Usage
read_chrom(dir, format = "waters_raw", method)
Arguments
dir |
directory for chromatraograms |
format |
format of the chromatogram files. Options are "waters_raw" and "mzML". |
method |
LC-MS/MS method ID saved available in the database. |
Examples
## Not run:
path <- system.file("extdata", "waters_raw_ex", package="PKbioanalysis")
main <- read_chrom(path, method = 1)
## End(Not run)
Read experiment results
Description
Read experiment results
Usage
read_experiment_results(x, drop_prefix = FALSE, vendor = "targetlynx_xml")
Arguments
x |
path to experiment results. See details |
drop_prefix |
logical. If TRUE, drop the prefix from the sample name |
vendor |
vendor name. Currently only "targetlynx_xml" or "targetlynx_csv" are supported. |
Details
Currently only targetlynx XML or CSV exported files are supported.
Value
QuantRes object with the results of the experiment.
This will save the plate to the database
Description
This will save the plate to the database
Usage
register_plate(plate)
Arguments
plate |
PlateObj object or MultiPlate object |
Value
PlateObj object or list of PlateObj objects
Register a multiple plates at once
Description
Register a multiple plates at once
Usage
## S4 method for signature 'MultiPlate'
register_plate(plate)
Arguments
plate |
MultiPlate object |
Value
a list of RegisteredPlate objects
Register a plate This will save the plate to the database
Description
Register a plate This will save the plate to the database
Usage
## S4 method for signature 'PlateObj'
register_plate(plate)
Arguments
plate |
PlateObj object |
Value
Registered PlateObj object
Convert response to concentration
Description
Convert response to concentration
Usage
response_to_conc(quantres, compound_id, response)
Arguments
quantres |
QuantRes object |
compound_id |
character |
response |
numeric. Must match the response type used in linearity. Either abs_response or rel_response |
Value
numeric
Reverse predict concentration from response
Description
Reverse predict concentration from response
Usage
reverse_predict(fit, newdata, intercept)
Arguments
fit |
lm object |
newdata |
vector or data frame with response values |
intercept |
logical. Whether the model has intercept or not |
Value
numeric. Estimated concentration
Author(s)
Omar I. Elashkar
Get Summary of an object
Description
Get Summary of an object
Usage
run_summary(object)
## S3 method for class 'PeakRes'
run_summary(object)
Arguments
object |
A PeakRes object |
Smooth Chromatogram Peaks
Description
This function smooths chromatogram peaks using different algorithms.
Usage
smooth_chrom(chrom_res, filter = "mean", window = 2, iter = 2)
Arguments
chrom_res |
ChromRes object |
filter |
Filter to use. Options are "mean", "median", "savgol", "gaussian" |
window |
Window size for the filter |
iter |
Number of iterations. If 0, no smoothing is applied. |
bioanalytic_app
Description
This function creates a shiny app for plate management
Usage
study_app()
Value
A shiny app. No default return value. Can return a PlateObj if reuse_plate_button is clicked
Manually Update Observed RT for either all compounds, all next samples, or single compound and sample
Description
Update RT for either all compounds, all next samples, or single compound and sample
Usage
update_RT(
chrom_res,
compound_id,
sample_id = NULL,
peak_start,
peak_end,
manual = FALSE,
target = "single",
force = FALSE
)
Arguments
chrom_res |
ChromRes object |
compound_id |
Compound ID |
sample_id |
Sample ID |
peak_start |
Minimum RT value |
peak_end |
Maximum RT value |
manual |
Manual update. Default is FALSE |
target |
Target of update. Options are "single", "all", "all_next" |
force |
Force update if previous peak exists. Default is FALSE |
Details
Only target = "all" will update the expected RT for all compounds.
Examples
## Not run:
update_RT(chrom_res, compound_id = 1, sample_id = 1,
peak_start = 1, peak_end = 2, target = "single")
## End(Not run)
Write injection sequence to database
Description
Write injection sequence to database
Usage
write_injec_seq(injec_seq)
Arguments
injec_seq |
InjecListObj object |
Value
dataframe