QurvE (version 1.1.1)

growth.drFit: Perform a dose-response analysis on response vs. concentration data

Description

growth.drFit serves to determine dose-response curves on every condition in a dataset. The response parameter can be chosen from every physiological parameter in a gcTable table which is obtained via growth.gcFit. growth.drFit calls the functions growth.drFitSpline and growth.drBootSpline, or growth.drFitModel to generate a table with estimates for EC50 and respecting statistics.

Usage

growth.drFit(
  gcTable,
  control = growth.control(dr.method = "model", dr.model = c("gammadr", "multi2", "LL.2",
    "LL.3", "LL.4", "LL.5", "W1.2", "W1.3", "W1.4", "W2.2", "W2.3", "W2.4", "LL.3u",
    "LL2.2", "LL2.3", "LL2.3u", "LL2.4", "LL2.5", "AR.2", "AR.3", "MM.2"),
    dr.have.atleast = 6, dr.parameter = "mu.linear", nboot.dr = 0, smooth.dr = NULL,
    log.x.dr = FALSE, log.y.dr = FALSE)
)

Value

An object of class drFit.

raw.data

Data that passed to the function as gcTable.

drTable

Dataframe containing condition identifiers, fit options, and results of the dose-response analysis.

drBootSplines

List of all drBootSpline objects generated by the call of growth.drBootSpline for each distinct experiment.

drFittedSplines

List of all drFitSpline objects generated by the call of growth.drFitSpline for each distinct experiment.

control

Object of class grofit.control containing list of options passed to the function as control.

Arguments

gcTable

A dataframe containing the data for the dose-response curve estimation. Such table of class gcTable can be obtained by running growth.gcFit.

control

A grofit.control object created with growth.control, defining relevant fitting options.

dr.method

(Character) Define the method used to perform a dose-responde analysis: smooth spline fit ('spline') or model fitting ('model').

dr.model

(Character) Provide a list of models from the R package 'drc' to include in the dose-response analysis (if dr.method = 'model'). If more than one model is provided, the best-fitting model will be chosen based on the Akaike Information Criterion.

dr.have.atleast

(Numeric) Minimum number of different values for the response parameter one should have for estimating a dose response curve. Note: All fit procedures require at least six unique values. Default: 6.

dr.parameter

(Character or numeric) The response parameter in the output table to be used for creating a dose response curve. See growth.drFit for further details. Default: 'mu.linfit', which represents the maximum slope of the linear regression. Typical options include: 'mu.linfit', 'lambda.linfit', 'dY.linfit', 'mu.spline', 'dY.spline', 'mu.model', and 'A.model'.

smooth.dr

(Numeric) Smoothing parameter used in the spline fit by smooth.spline during dose response curve estimation. Usually (not necessesary) in (0; 1]. See smooth.spline for further details. Default: NULL.

log.x.dr

(Logical) Indicates whether ln(x+1) should be applied to the concentration data of the dose response curves. Default: FALSE.

log.y.dr

(Logical) Indicates whether ln(y+1) should be applied to the response data of the dose response curves. Default: FALSE.

nboot.dr

(Numeric) Defines the number of bootstrap samples for EC50 estimation. Use nboot.dr = 0 to disable bootstrapping. Default: 0.

Details

Common response parameters used in dose-response analysis:

Linear fit:
- mu.linfit: Growth rate
- lambda.linfit: Lag time
- dY.linfit: Density increase
- A.linfit: Maximum measurement

Spline fit:
- mu.spline: Growth rate
- lambda.spline: Lag time
- A.spline: Maximum measurement
- dY.spline: Density increase
- integral.spline: Integral

Parametric fit:
- mu.model: Growth rate
- lambda.model: Lag time
- A.model: Maximum measurement
- integral.model: Integral'

References

Matthias Kahm, Guido Hasenbrink, Hella Lichtenberg-Frate, Jost Ludwig, Maik Kschischo (2010). grofit: Fitting Biological Growth Curves with R. Journal of Statistical Software, 33(7), 1-21. DOI: 10.18637/jss.v033.i07

See Also

Other growth fitting functions: growth.gcBootSpline(), growth.gcFitLinear(), growth.gcFitModel(), growth.gcFitSpline(), growth.gcFit(), growth.workflow()

Examples

# \donttest{
# Create random growth data set
rnd.data1 <- rdm.data(d = 35, mu = 0.8, A = 5, label = 'Test1')
rnd.data2 <- rdm.data(d = 35, mu = 0.6, A = 4.5, label = 'Test2')

rnd.data <- list()
rnd.data[['time']] <- rbind(rnd.data1$time, rnd.data2$time)
rnd.data[['data']] <- rbind(rnd.data1$data, rnd.data2$data)

# Run growth curve analysis workflow
gcFit <- growth.gcFit(time = rnd.data$time,
                       data = rnd.data$data,
                       parallelize = FALSE,
                       control = growth.control(fit.opt = 's',
                                                suppress.messages = TRUE))

# Perform dose-response analysis
drFit <- growth.drFit(gcTable = gcFit$gcTable,
             control = growth.control(dr.parameter = 'mu.spline'))

# Inspect results
summary(drFit)
plot(drFit)
# }