Package {toxdrc}

toxdrc: Pipeline for Dose-Response Curve Analysis

Description

Provides a variety of tools for assessing dose response curves, with an emphasis on toxicity test data. The main feature of this package are modular functions which can be combined through the namesake pipeline, 'runtoxdrc', to automate the analysis for large and complex datasets. This includes optional data preprocessing steps, like outlier detection, solvent effects, blank correction, averaging technical replicates, and much more. Additionally, this pipeline is adaptable to any long form dataset, and does not require specific column or group naming to work.

Author(s)

Maintainer: Jack Salole salolej@mcmaster.ca

See Also

Useful links:

• https://github.com/jsalole/toxdrc

• Report bugs at https://github.com/jsalole/toxdrc/issues

Simulated acute lethality test data with a quantal endpoint

Description

A simulated acute lethality study, for demonstrating and testing the quantal path through [runtoxdrc()]. Organisms are scored affected or unaffected at each concentration, so the response is a count out of a known group size rather than a measurement.

Usage

acutetox

Format

## 'acutetox' A data frame with 54 rows and 8 columns:

TestID

Combination of Test_Number, Substance and Replicate

Test_Number

Identifying number of each test substance

Substance

Substance label, named for the response pattern it shows

Replicate

Experimental replicate, A to C

Conc

Concentration in arbitrary units; 0 is the control

Affected

Number of organisms affected in the group

Total

Number of organisms exposed in the group, 20 throughout

Prop

Affected fraction, 'Affected / Total'

Details

**These data are simulated, not measured.** Counts are computed deterministically from known log-logistic curves rather than sampled at random, so the dataset is identical across R versions and the generating values can be recovered exactly. Replicate variation is a fixed offset of 0, +1 and -1 affected organisms. The generating script is in 'data-raw/acutetox.R'.

Three substances are included, each exercising a different path through the '"quantal"' preset:

'Graded' (Test_Number 101)

A clean concentration-response with no control mortality, generated with an ED50 of 10 and a slope of 0.9. Pooled response runs 0, 0.10, 0.25, 0.50, 0.75, 0.90, so three concentrations sit inside the partial-effect band and a model fits normally. LC50 is 10 by construction.

'Steep' (Test_Number 102)

No partial responses anywhere: nothing affected at or below 10, everything affected at 32 and above. The slope is unidentifiable, so no dose-response model can be fitted and [interpolateECx()] applies instead. The expected estimate is the geometric mean of the bracketing concentrations, \sqrt{10 \times 32} = 17.889.

'Background' (Test_Number 103)

As 'Graded', but with 10 percent control mortality, so the lower limit is not 0 and 'LL.3u' has a reason to be preferred over 'LL.2'. The absolute 50 percent point is 7.804 rather than 10, because the ED50 parameter marks the midpoint between the curve's limits, which is no longer the midpoint between 0 and 1 once the lower limit is raised.

Source

Simulated for this package. See 'data-raw/acutetox.R'.

See Also

[cellglow] and [toxresult] for continuous endpoints, [toxdrc_preset()] for the matching configuration.

Examples

# The proportion column with a group size, which is the default path.
runtoxdrc(
  dataset  = acutetox,
  Conc     = Conc,
  Response = Prop,
  N        = Total,
  IDcols   = c("Test_Number", "Substance"),
  preset   = "quantal",
  quiet    = TRUE
)

# The same data supplied as counts instead.
runtoxdrc(
  dataset  = acutetox,
  Conc     = Conc,
  Response = Affected,
  N        = Total,
  IDcols   = c("Test_Number", "Substance"),
  preset   = "quantal",
  endpoint = toxdrc_endpoint(type = "binomial", response.type = "count"),
  quiet    = TRUE
)

Average response variable

Description

'averageresponse()' averages a given response variable by the experimental group, such as concentration or exposure length.

Usage

averageresponse(
  dataset,
  Conc,
  Response,
  N = NULL,
  IDcols = NULL,
  type = c("continuous", "binomial"),
  list_obj = NULL,
  quiet = FALSE
)

Arguments

Value

A collapsed 'dataset' with one row for each level of 'Conc'. If 'list_obj' is provided, returns this within a list as 'list_obj$dataset', along with an unmodified copy as 'list_obj$pre_average_dataset'.

Examples

averageresponse(
  dataset = toxresult,
  Conc = Conc,
  Response = RFU,
  IDcols = c("TestID", "Test_Number", "Dye", "Type", "Replicate")
)

Blank correct response variable

Description

'blankcorrect()' subtracts a calculated correction value from all responses.

Usage

blankcorrect(
  dataset,
  Conc,
  blank_group = "Blank",
  Response,
  list_obj = NULL,
  quiet = FALSE
)

Arguments

Value

A modified 'dataset' with an additional column, 'c_response'. If 'list_obj' is provided, returns this within a list as 'list_obj$dataset', along with statistics of the correction value as 'list_obj$blank_stats'.

Examples

blankcorrect(
     dataset = toxresult,
     Conc = Conc,
     blank_group = "Blank",
     Response = RFU
   )

Example toxicity test data with multiple experimental groups.

Description

A subset of data from a study using the RTgill-W1 assay (ISO 21115/OECD 249). Briefly, cells are exposed to a toxicant and the fluorescent signal is measured using 3 indicators.

Usage

cellglow

Format

## 'cellglow' A data frame with 1,080 rows and 7 columns:

TestID

Combination of Test_Number, Dye, Type, and Replicate

Test_Number

Identifying number of each effluent sample

Conc

Concentration of reference toxicant (3,4 dichloranaline). 0 is solvent control, "control" is a lab control

RFU

Fluoresence produced as determined by a plate reader

Dye

Three cell viability indicators; aB = alamarBlue, CFDA = 5-CFDA-AM, NR = Neutral Red

Type

Only spiked exists in this dataset; indicates a reference toxicant was added to the effluent.

Replicate

The experimental replicate; replication occured at a well-plate level.

Details

Data collected as part of a study. Full dataset is available within a data repository: Salole, Jack; Wilson, Joanna; Taylor, Lisa, 2025, "RTgill-W1 Assay - Optimization and Effluent Testing", https://doi.org/10.5683/SP3/ES7GDM, Borealis, V2.

Source

https://doi.org/10.5683/SP3/ES7GDM

Check for an effect

Description

'checktoxicity()' flags if the response variable exceeds a limit in either direction as evidence of an effect.

Usage

checktoxicity(
  dataset,
  Conc,
  Response,
  effect,
  type = c("relative", "absolute"),
  direction = c("below", "above"),
  reference_group = "0",
  target_group = NULL,
  list_obj = NULL,
  quiet = FALSE
)

Arguments

Value

TRUE if the response variable exceeds a limit in either direction and FALSE otherwise. If 'list_obj' is provided, returns this within a list as 'list_obj$effect'.

Examples

checktoxicity(
 dataset = toxresult,
 Conc = Conc,
 Response = RFU,
 effect = 0.5
)

Configuration functions for the runtoxdrc pipeline

Description

An overview of the modular configuration functions used by [runtoxdrc()]. These configuration functions provide default lists of parameters for customizing different stages of the pipeline to reduce the number of arguments required in [runtoxdrc()]. Each function returns a named list of configuration parameters suitable for passing directly to [runtoxdrc()].

Details

**Available configuration functions:**

• [toxdrc_endpoint()] - Continuous or quantal response, and how it is supplied

• [toxdrc_qc()] - Quality control and filtering options

• [toxdrc_normalization()] - Blank correction and normalization

• [toxdrc_toxicity()] - Toxicity threshold and response-level options

• [toxdrc_modelling()] - Model selection, fitting criteria, and EDx calculation

• [toxdrc_output()] - Output settings

See Also

[runtoxdrc()], [toxdrc_endpoint()], [toxdrc_qc()], [toxdrc_normalization()], [toxdrc_toxicity()], [toxdrc_modelling()], [toxdrc_output()]

Check for groups with high CV

Description

This function calculates the coefficient of variation (CV) of each of the exposure conditions, and flags them if they exceed a set value.

Usage

flagCV(dataset, Conc, Response, max_val = 30, list_obj = NULL, quiet = FALSE)

Arguments

Value

A modified 'dataset' with an additional column, 'CVflag'. If 'list_obj' is provided, returns this within a list as 'list_obj$dataset', along with a summary of the CV results as 'list_obj$CVresults'.

Examples

df <- data.frame(x = rep(1:2, each = 3), y = c(10, 11, 9, 20, 40, 60))
flagCV(dataset = df, Conc = x, Response = y, max_val = 30)

Get point estimates from model

Description

Generate point estimates from a dose response curve.

Usage

getECx(
  dataset,
  model,
  EDx = 0.5,
  interval = c("tfls", "fls", "delta", "none"),
  level = 0.95,
  type = c("relative", "absolute"),
  quiet = FALSE,
  EDargs.supplement = list(),
  list_obj = NULL
)

Arguments

Value

A dataframe of the point estimates. If 'list_obj' is provided, returns this within a list as 'list_obj$effectmeasure'.

See Also

[modelcomp()] to fit the model, and [drc::ED()] for the underlying estimation.

Generate metadata from a dataframe

Description

Collects identifying or important values from an experimental replicate.

Usage

getmetadata(dataset, IDcols = NULL, list_obj = NULL, quiet = FALSE)

Arguments

Value

A 1 row dataframe of the identifying parameters of an experimental replicate. If 'list_obj' is provided, returns this within a list as 'list_obj$metadata'.

Estimate a point estimate by log-linear interpolation

Description

'interpolateECx()' estimates an effect concentration by interpolating between the observed concentrations that bracket the target response, linearly on log concentration. It is intended for quantal data where no concentration produced a partial effect, so no dose-response model can be fitted.

Usage

interpolateECx(
  dataset,
  Conc,
  Response,
  EDx = 0.5,
  type = c("relative", "absolute"),
  partial.tol = 0.2,
  list_obj = NULL,
  quiet = FALSE
)

Arguments

Details

Interpolating linearly on log concentration is the standard treatment of a test with no partial responses. Between the highest concentration with no effect, 'A', and the lowest with complete effect, 'B', the estimate is

EC_p = A \times (B/A)^p

so the EC50 is \sqrt{A \times B}, the geometric mean of the two. That value is the geometric midpoint and does not depend on the shape of the underlying curve, which is what makes it defensible when the shape is unknown.

The same is not true of other effect levels. An EC10 estimated this way lands just above 'A', and an EC90 just below 'B', so both largely restate the bracketing concentrations rather than estimating anything from them. Where they fall between 'A' and 'B' is a consequence of the straight-line assumption, which is precisely what the data cannot support. Requesting a level other than 0.5 when no partial responses exist therefore raises a warning.

Interpolation never extrapolates. If the target response is not bracketed by two observed concentrations, the estimate is NA.

Because the estimate comes from two points rather than a fitted model, there is no standard error and no confidence interval. Those columns are returned as NA so the output matches [getECx()].

Value

A dataframe of point estimates with the same columns as [getECx()]: 'Effect Measure', 'Estimate', 'Std. Error', 'Lower' and 'Upper', the last three being NA. If 'list_obj' is provided, returns this within a list as 'list_obj$effectmeasure'.

See Also

[getECx()] for model-based estimation, and [runtoxdrc()] for using this automatically when a quantal test has no partial responses.

Examples

# No partial responses: nothing affected at 10, everything at 30.
quantal <- data.frame(
  Conc = c(0, 10, 30, 100),
  Prop = c(0, 0, 1, 1)
)
interpolateECx(quantal, Conc = Conc, Response = Prop, quiet = TRUE)
# EC50 is sqrt(10 * 30) = 17.32

Compare model fits and select best model

Description

Data is fitted to provided models, typically from the drc package. Models fitted successfully are compared using multiple goodness-of-fit scores, and organized using the score given as the 'metric' argument.

For a continuous response the default 'model_list' holds a single model, so by default this fits 'LL.4' rather than choosing between candidates. Supply more than one entry to make 'metric' meaningful. For a quantal endpoint the default holds two, 'LL.2' and 'LL.3u', which differ in whether background response in the controls is estimated.

Usage

modelcomp(
  dataset,
  Conc,
  Response,
  N = NULL,
  model_list = NULL,
  metric = c("IC", "Res var", "Lack of fit"),
  type = c("continuous", "binomial"),
  list_obj = NULL,
  quiet = FALSE
)

Arguments

Details

The 'model_list' argument requires a specific style. The argument must be a named list, where each name is the shorthand of the model function held in that entry, and each entry is the *result* of calling that function. An example of this is 'list("LL.4" = LL.4(), "W1.4" = W1.4())'.

The names are not cosmetic: they are used to label the comparison table and to retrieve the winning model, so a name that disagrees with the function it holds is rejected rather than silently ignored.

Models that fail to converge are retained in the comparison table as 'NA' rows and are never selected as the best model.

**Ordering of 'metric'.** '"IC"' and '"Res var"' are ordered ascending, since a smaller value indicates the better model. '"Lack of fit"' is ordered descending, because it is a goodness-of-fit p-value where a larger value means less evidence against the model. Note that this differs from [drc::mselect()], which sorts every criterion ascending.

Value

A fitted drm model. If 'list_obj' is provided, returns this within the list as 'list_obj$best_model', along with the model name ('list_obj$best_model_name'), and the model comparison dataframe ('list_obj$model_df'). If model fitting fails, returns NULL.

See Also

[drc::getMeanFunctions()] for compatible models and their shorthand for 'model_list'.

Examples

toxresult2 <- toxresult[!toxresult$Conc %in% c ("Control", "Blank"),]
toxresult2$Conc <- as.numeric(toxresult2$Conc)
modelcomp(toxresult2, Conc, RFU, metric = "IC")

Normalize response variable

Description

Express a response variable relative to a reference group.

Usage

normalizeresponse(
  dataset,
  Conc,
  reference_group = "0",
  Response,
  list_obj = NULL,
  quiet = FALSE
)

Arguments

Value

A modified 'dataset' with an additional column, 'normalized response'. If 'list_obj' is provided, returns this within a list as 'list_obj$dataset', along with summary statistics surrounding the reference group as 'list_obj$normalize_response_summary'.

Examples

normalizeresponse(
 dataset = toxresult,
 Conc = Conc,
 Response = RFU
)

Check for positive control effect

Description

This function evaluates the difference between a two groups to determine if the difference between them exceeds a set amount. Commonly used to determine if a solvent introduces effects.

Usage

pctl(
  dataset,
  Conc,
  reference_group = "Control",
  positive_group = 0,
  Response,
  max_diff = 10,
  list_obj = NULL,
  quiet = FALSE
)

Arguments

Value

A modified 'dataset' with an additional column, 'Validity'. If 'list_obj' is provided, returns this within a list as 'list_obj$dataset', along with statistics of the positive and reference group as 'list_obj$pctlresults'.

Examples

pctl(
 dataset = toxresult,
 Conc = Conc,
 Response = RFU,
 reference_group = "Control",
 positive_group = "0"
)

Print a toxdrc preset

Description

Print a toxdrc preset

Usage

## S3 method for class 'toxdrc_preset'
print(x, ...)

Arguments

Value

'x', invisibly.

Remove outliers iteratively using Grubbs' test

Description

Removes statistical outliers from each group of ‘Conc' by applying Grubbs’ test repeatedly, dropping the most extreme value each time the test is significant at p < 0.05. Groups of two or fewer observations are left untouched, since Grubbs' test needs at least three.

Usage

removeoutliers(dataset, Conc, Response, list_obj = NULL, quiet = FALSE)

Arguments

Value

A modified 'dataset' with outliers removed. If 'list_obj' is provided, returns this within a list as 'list_obj$dataset', along with dataframe of removed outliers as 'list_obj$removed_outliers'.

Examples

df <- data.frame(x = rep(1:2, each = 3),y = c(3, 5, 7, 3, 4, 30))
removeoutliers(dataset = df, Conc = x, Response = y)

Point estimation pipeline

Description

'runtoxdrc()' is the pipeline for function in the toxdrc package. This function allows the automated analysis of large datasets, while maintaining a consistent process for each subset of data.

Usage

runtoxdrc(
  dataset,
  Conc,
  Response,
  N = NULL,
  IDcols = NULL,
  quiet = FALSE,
  preset = NULL,
  endpoint = NULL,
  qc = NULL,
  normalization = NULL,
  toxicity = NULL,
  modelling = NULL,
  output = NULL
)

Arguments

Value

By default, returns a list of lists with each subset of data having its own entry. Each subset contains dataframes, models, and other objects that track the pipeline process. If 'output = list(condense = TRUE)', the results are summarized into a single dataframe containing the 'IDcols' and model information of each data subset.

Quantal endpoints

Setting 'endpoint = toxdrc_endpoint(type = "binomial")' fits models with 'drc::drm(type = "binomial")', weighted by 'N', and defaults to comparing 'LL.2' against 'LL.3u'. Replicates are pooled by group size rather than averaged.

Three preprocessing steps are refused for quantal data, because they assume a continuous response on an unbounded scale: 'blank.correction', 'normalize.resp', and 'outlier.test'. Each raises an error naming the alternative. '"Res var"' is likewise unavailable as 'model.metric', since a binomial fit has no residual variance.

Setting 'modelling = toxdrc_modelling(interpolate = TRUE)' handles the degenerate case where no concentration produced a partial effect. There the slope is unidentifiable, so no model can be fitted at all, and the estimate comes from log-linear interpolation between the bracketing concentrations instead. Such entries are marked with 'best_model_name = "interpolated"' and carry no confidence interval. See [interpolateECx()].

See Also

[toxdrc_preset()] for ready-made configurations, and [config_runtoxdrc()] for the individual settings they are built from.

Examples

  analyzed_data <- runtoxdrc(
 dataset = cellglow,
 Conc = Conc,
 Response = RFU,
 IDcols = c("Test_Number", "Dye", "Replicate", "Type"),
 quiet = TRUE,
 normalization = toxdrc_normalization(
   blank.correction = TRUE,
   normalize.resp = TRUE
 ),
 modelling = toxdrc_modelling(EDx = c(0.2, 0.5, 0.7))
)

Endpoint configuration for the runtoxdrc pipeline.

Description

Declares whether the response is a continuous measurement or a quantal (binary) endpoint such as mortality, and how a quantal response is supplied.

Usage

toxdrc_endpoint(
  type = c("continuous", "binomial"),
  response.type = c("proportion", "count")
)

Arguments

Details

A quantal endpoint is the affected fraction of a group of organisms, and fitting it correctly requires knowing how many organisms were in each group. A response of 0.1 from 1 of 10 and from 10 of 100 are the same number but carry very different weight, so 'runtoxdrc()' requires the 'N' argument when 'type = "binomial"'. Models are then fitted with 'drc::drm(type = "binomial")' and weighted by group size.

Several preprocessing steps assume a continuous response and are refused for quantal data: blank correction, normalization to a reference group, and Grubbs' outlier removal. See [runtoxdrc()] for what to use instead.

Value

A named list containing the endpoint configuration for use in [runtoxdrc()].

See Also

[config_runtoxdrc], [runtoxdrc()], [modelcomp()]

Examples

toxdrc_endpoint()
toxdrc_endpoint(type = "binomial")
toxdrc_endpoint(type = "binomial", response.type = "count")

Modelling configuration for the runtoxdrc pipeline.

Description

Defines how dose-response models are fitted, selected, and how point estimates (EDx) are calculated.

Usage

toxdrc_modelling(
  model.list = NULL,
  model.metric = c("IC", "Res var", "Lack of fit"),
  EDx = 0.5,
  interval = c("tfls", "fls", "delta", "none"),
  level = 0.95,
  type = c("relative", "absolute"),
  quiet = FALSE,
  EDargs.supplement = list(),
  interpolate = FALSE,
  partial.tol = 0.2
)

Arguments

Value

A named list containing model fitting and selection settings for use in [runtoxdrc()].

See Also

[config_runtoxdrc], [runtoxdrc()], [drc::ED()], [drc::getMeanFunctions()], [modelcomp()]

Examples

toxdrc_modelling(EDargs.supplement = list(interval = "delta", level = 0.9))

Set normalization configuration for the runtoxdrc pipeline.

Description

Control blank correction and normalization of the response variable.

Usage

toxdrc_normalization(
  blank.correction = FALSE,
  blank.label = "Blank",
  normalize.resp = FALSE,
  relative.label = 0
)

Arguments

Value

A named list containing normalization configuration for use in [runtoxdrc()].

See Also

[config_runtoxdrc], [runtoxdrc()], [blankcorrect()], [normalizeresponse()]

Examples

toxdrc_normalization(blank.correction = TRUE, relative.label = "Control")

Output configuration for the runtoxdrc pipeline.

Description

Defines how [runtoxdrc()] output is returned.

Usage

toxdrc_output(
  condense = FALSE,
  sections = c("ID", "effectmeasure", "best_model_name", "effect")
)

Arguments

Value

A named list containing output configuration for use in [runtoxdrc()].

See Also

[config_runtoxdrc], [runtoxdrc()]

Examples

toxdrc_output()
toxdrc_output(condense = TRUE)

Ready-made configurations for common study designs

Description

'toxdrc_preset()' returns a complete set of configuration objects for a kind of study, suitable for passing to [runtoxdrc()] as 'preset'. The point is less to save typing than to record which combinations of settings belong together, which is not obvious from the individual configuration helpers.

Usage

toxdrc_preset(name = c("continuous", "quantal", "normalized"))

Arguments

Details

A preset is an ordinary named list of the same objects [runtoxdrc()] takes individually, so it can be printed, inspected, and modified. Anything passed explicitly to [runtoxdrc()] overrides the corresponding entry, which makes a preset a starting point rather than a black box.

**Available presets:**

'"continuous"'

A measured response on an arbitrary scale, fitted with ‘LL.4'. No preprocessing. This is the source of [runtoxdrc()]’s own defaults, so using it changes nothing; it exists so the baseline has a name and cannot drift from the defaults it defines.

'"quantal"'

A binary endpoint such as mortality, with few organisms per group. Fitted as binomial data weighted by 'N', comparing 'LL.2' against 'LL.3u' so that background response in the controls is accounted for only if the data call for it. Log-linear interpolation is enabled, which covers the common case of a test with no partial responses, where no model can be fitted at all. Requires the 'N' argument to [runtoxdrc()].

Effect levels are relative, which for a quantal fit is algebraically Abbott's correction: the EC50 is the concentration affecting half of the organisms that would otherwise have survived, rather than half of all organisms. The two are identical unless there is control mortality, and so differ only when 'LL.3u' is selected. Pass 'modelling = toxdrc_modelling(type = "absolute")' to report raw response levels instead.

'"normalized"'

A continuous response in arbitrary units, blank corrected and expressed relative to a control group. Because normalization puts the control at 1 and complete effect at 0 by construction, the toxicity threshold is absolute rather than relative, and the models fix those bounds instead of estimating them: 'LL.2' fixes both, and 'LL.3' estimates the upper limit for data where responses run above 1 through noise or stimulation.

Presets set 'blank.label' and 'relative.label' to their defaults, since they cannot know how your groups are labelled. Override the whole 'normalization' block if yours differ; see examples.

Value

A named list of configuration objects, with class 'toxdrc_preset', containing 'endpoint', 'qc', 'normalization', 'toxicity', 'modelling', and 'output'.

See Also

[runtoxdrc()], [config_runtoxdrc]

Examples

toxdrc_preset("quantal")

# A preset is a starting point. To keep its settings but change the group
# labels, pass a replacement for that block only.

runtoxdrc(
  dataset       = cellglow,
  Conc          = Conc,
  Response      = RFU,
  IDcols        = c("Test_Number", "Dye", "Replicate", "Type"),
  quiet         = TRUE,
  preset        = toxdrc_preset("normalized"),
  normalization = toxdrc_normalization(
    blank.correction = TRUE,
    normalize.resp   = TRUE,
    blank.label      = "Blank",
    relative.label   = 0
  )
)

Set quality control options for the runtoxdrc pipeline.

Description

Control outlier detection, CV calculation, averaging of response variable, and testing for positive control effects.

Usage

toxdrc_qc(
  outlier.test = FALSE,
  cv.flag = TRUE,
  cvflag.lvl = 30,
  pctl.test = FALSE,
  pctl.lvl = 10,
  ref.label = "Control",
  pctl.label = 0,
  avg.resp = TRUE
)

Arguments

Value

A named list containing the quality control configuration for use in [runtoxdrc()].

See Also

[config_runtoxdrc], [runtoxdrc()], [pctl()], [removeoutliers()], [flagCV()]

Examples

toxdrc_qc(outlier.test = TRUE, cvflag.lvl = 20)

Toxicity configuration for the runtoxdrc pipeline.

Description

Defines how toxicity is determined for model fitting.

Usage

toxdrc_toxicity(
  toxic.lvl = 0.7,
  toxic.type = c("relative", "absolute"),
  toxic.direction = c("below", "above"),
  comp.group = 0,
  target.group = NULL
)

Arguments

Value

A named list containing toxicity determination settings for use in [runtoxdrc()].

See Also

[config_runtoxdrc], [runtoxdrc()], [checktoxicity()]

Examples

toxdrc_toxicity(toxic.lvl = 0.5, toxic.direction = "above")

Example toxicity test data from a single experimental subset.

Description

A subset of data from a study using the RTgill-W1 assay (ISO 21115/OECD 249). Briefly, cells are exposed to a toxicant and the fluorescent signal is measured using 3 indicators.

Usage

toxresult

Format

## 'toxresult' A data frame with 1,080 rows and 7 columns:

TestID

Combination of Test_Number, Dye, Type, and Replicate

Test_Number

Identifying number of each effluent sample

Conc

Concentration of reference toxicant (3,4 dichloranaline). 0 is solvent control, "control" is a lab control

RFU

Fluoresence produced as determined by a plate reader

Dye

Three cell viability indicators; aB = alamarBlue, CFDA = 5-CFDA-AM, NR = Neutral Red

Type

Only spiked exists in this dataset; indicated a reference toxicant was added to the effluent.

Replicate

The experimental replicate; replication occured at a well-plate level.

Details

Data collected as part of a study. Full dataset is available within a data repository: Salole, Jack; Wilson, Joanna; Taylor, Lisa, 2025, "RTgill-W1 Assay - Optimization and Effluent Testing", https://doi.org/10.5683/SP3/ES7GDM, Borealis, V2.

Source

https://doi.org/10.5683/SP3/ES7GDM