Package 'QurvE'

Internal function used to fit a biosensor response model with nlsLM

Description

Calculates the values of biosensor response model for given time points and response parameters.

Usage

biosensor.eq(x, y.min, y.max, K, n)

Arguments

Value

A vector of fluorescence values

References

Meyer, A.J., Segall-Shapiro, T.H., Glassey, E. et al. Escherichia coli “Marionette” strains with 12 highly optimized small-molecule sensors. Nat Chem Biol 15, 196–204 (2019). DOI: 10.1038/s41589-018-0168-3

Examples

n <- seq(1:10)
conc <- rev(10*(1/2)^n)
fit <- biosensor.eq(conc, 300, 82000, 0.85, 2)

Description

Export an R object as .RData file

Usage

export_RData(object, out.dir = tempdir(), out.nm = class(object))

Arguments

Value

NULL

Examples

if(interactive()){
df <- data.frame('A' = seq(1:10), 'B' = rev(seq(1:10)))

export_RData(df)
}

Description

Export a tabular object as tab-separated .txt file

Usage

export_Table(table, out.dir = tempdir(), out.nm = deparse(substitute(table)))

Arguments

Value

NULL

Examples

if(interactive()){
df <- data.frame('A' = seq(1:10), 'B' = rev(seq(1:10)))

export_Table(df)
}

Description

A fl.control object is required to perform various computations on fluorescence data stored within grodata objects (created with read_data or parse_data). A fl.control object is created automatically as part of fl.workflow.

Usage

fl.control(
  fit.opt = c("l", "s"),
  x_type = c("growth", "time"),
  norm_fl = TRUE,
  t0 = 0,
  tmax = NA,
  min.growth = NA,
  max.growth = NA,
  log.x.lin = FALSE,
  log.x.spline = FALSE,
  log.y.lin = FALSE,
  log.y.spline = FALSE,
  lin.h = NULL,
  lin.R2 = 0.97,
  lin.RSD = 0.05,
  lin.dY = 0.05,
  dr.parameter = "max_slope.spline",
  dr.method = c("model", "spline"),
  dr.have.atleast = 5,
  smooth.dr = NULL,
  log.x.dr = FALSE,
  log.y.dr = FALSE,
  nboot.dr = 0,
  biphasic = FALSE,
  interactive = FALSE,
  nboot.fl = 0,
  smooth.fl = 0.75,
  growth.thresh = 1.5,
  suppress.messages = FALSE,
  neg.nan.act = FALSE,
  clean.bootstrap = TRUE
)

Arguments

Value

Generates a list with all arguments described above as entries.

References

Meyer, A.J., Segall-Shapiro, T.H., Glassey, E. et al. Escherichia coli “Marionette” strains with 12 highly optimized small-molecule sensors. Nat Chem Biol 15, 196–204 (2019). DOI: 10.1038/s41589-018-0168-3

Examples

# default option
control_default <- fl.control()
# user defined
control_manual <- fl.control(fit.opt = c('s'),
                             smooth.fl = 0.6,
                             x_type = 'time',
                             t0 = 2)

Description

Fit a biosensor model (Meyer et al., 2019) to response vs. concentration data

Usage

fl.drFit(
  flTable,
  control = fl.control(dr.method = "model", dr.parameter = "max_slope.spline")
)

Arguments

Details

Common response parameters used in dose-response analysis:

Linear fit:
- max_slope.linfit: Fluorescence increase rate
- lambda.linfit: Lag time
- dY.linfit: Maximum Fluorescence - Minimum Fluorescence
- A.linfit: Maximum fluorescence

Spline fit:
- max_slope.spline: Fluorescence increase rate
- lambda.spline: Lag time
- dY.spline: Maximum Fluorescence - Minimum Fluorescence
- A.spline: Maximum fluorescence
- integral.spline: Integral

Parametric fit:
- max_slope.model: Fluorescence increase rate
- lambda.model: Lag time
- dY.model: Maximum Fluorescence - Minimum Fluorescence
- A.model: Maximum fluorescence
- integral.model: Integral'

Value

An object of class drFit.

References

Meyer, A.J., Segall-Shapiro, T.H., Glassey, E. et al. Escherichia coli “Marionette” strains with 12 highly optimized small-molecule sensors. Nat Chem Biol 15, 196–204 (2019). DOI: 10.1038/s41589-018-0168-3

Examples

# Load example dataset
input <- read_data(data.fl = system.file('lac_promoters_fluorescence.txt', package = 'QurvE'),
                   csvsep.fl = "\t")

# Run fluorescence curve analysis workflow
fitres <- flFit(fl_data = input$fluorescence,
                time = input$time,
                parallelize = FALSE,
                control = fl.control(x_type = 'time', norm_fl = FALSE,
                                     suppress.messages = TRUE))

# Perform dose-response analysis
drFit <- fl.drFit(flTable = fitres$flTable,
                  control = fl.control(dr.method = 'model',
                                       dr.parameter = 'max_slope.linfit'))

# Inspect results
summary(drFit)
plot(drFit)

Description

fl.drFitModel fits the biosensor model proposed by Meyer et al. (2019) to the provided response (e.g., max_slope.spline vs. concentration data to determine the leakiness, sensitivity, induction fold-change, and cooperativity.

Usage

fl.drFitModel(conc, test, drID = "undefined", control = fl.control())

Arguments

Value

A drFitFLModel object.

• yEC50: Response value related to EC50.

• y.min: Minimum fluorescence ('leakiness', if lowest concentration is 0).

• y.max: Maximum fluorescence.

• fc: Fold change (y.max divided by y.min).

• K: Concentration at half-maximal response ('sensitivity').

• n: Cooperativity.

• yEC50.orig: Response value for EC50 in original scale, if a transformation was applied.

• K.orig: K in original scale, if a transformation was applied.

• test.nm: Test identifier extracted from test.

Use plot.drFitModel to visualize the model fit.

References

Meyer, A.J., Segall-Shapiro, T.H., Glassey, E. et al. Escherichia coli “Marionette” strains with 12 highly optimized small-molecule sensors. Nat Chem Biol 15, 196–204 (2019). DOI: 10.1038/s41589-018-0168-3

Examples

# Create concentration values via a serial dilution
conc <- c(0, rev(unlist(lapply(1:18, function(x) 10*(2/3)^x))),10)

# Simulate response values via biosensor equation
response <- biosensor.eq(conc, y.min = 110, y.max = 6000, K = 0.5, n = 2) +
            0.01*6000*rnorm(10)

# Perform fit
TestRun <- fl.drFitModel(conc, response, drID = 'test', control = fl.control())

print(summary(TestRun))
plot(TestRun)

Description

fl.report requires a flFitRes object and creates a report in PDF and HTML format that summarizes all results obtained.

Usage

fl.report(
  flFitRes,
  out.dir = tempdir(),
  out.nm = NULL,
  ec50 = FALSE,
  format = c("pdf", "html"),
  export = FALSE,
  parallelize = TRUE,
  ...
)

Arguments

Details

The template .Rmd file used within this function can be found within the QurvE package installation directory.

Value

NULL

Examples

# load example dataset
## Not run: 
input <- read_data(data.growth = system.file("lac_promoters_growth.txt", package = "QurvE"),
                   data.fl = system.file("lac_promoters_fluorescence.txt", package = "QurvE"),
                   csvsep = "\t",
                   csvsep.fl = "\t")

# Run workflow
res <- fl.workflow(grodata = input, ec50 = FALSE, fit.opt = 's',
                   x_type = 'time', norm_fl = TRUE,
                   dr.parameter = 'max_slope.spline',
                   suppress.messages = TRUE,
                   parallelize = FALSE)

fl.report(res, out.dir = tempdir(), parallelize = FALSE)

## End(Not run)

Description

fl.workflow runs fl.control to create a fl.control object and then performs all computational fitting operations based on the user input. Finally, if desired, a final report is created in PDF or HTML format that summarizes all results obtained.

Usage

fl.workflow(
  grodata = NULL,
  time = NULL,
  growth = NULL,
  fl_data = NULL,
  ec50 = TRUE,
  mean.grp = NA,
  mean.conc = NA,
  fit.opt = c("l", "s"),
  x_type = c("growth", "time"),
  norm_fl = TRUE,
  t0 = 0,
  tmax = NA,
  min.growth = 0,
  max.growth = NA,
  log.x.lin = FALSE,
  log.x.spline = FALSE,
  log.y.lin = FALSE,
  log.y.spline = FALSE,
  lin.h = NULL,
  lin.R2 = 0.97,
  lin.RSD = 0.07,
  lin.dY = 0.05,
  biphasic = FALSE,
  interactive = FALSE,
  dr.parameter = "max_slope.spline",
  dr.method = c("model", "spline"),
  dr.have.atleast = 5,
  smooth.dr = NULL,
  log.x.dr = FALSE,
  log.y.dr = FALSE,
  nboot.dr = 0,
  nboot.fl = 0,
  smooth.fl = 0.75,
  growth.thresh = 1.5,
  suppress.messages = FALSE,
  neg.nan.act = FALSE,
  clean.bootstrap = TRUE,
  report = NULL,
  out.dir = NULL,
  out.nm = NULL,
  export.fig = FALSE,
  export.res = FALSE,
  parallelize = TRUE,
  ...
)

Arguments

Value

A flFitRes object that contains all computation results, compatible with various plotting functions of the QurvE package and with fl.report.

Examples

# load example dataset
input <- read_data(data.growth = system.file("lac_promoters_growth.txt", package = "QurvE"),
                   data.fl = system.file("lac_promoters_fluorescence.txt", package = "QurvE"),
                   csvsep = "\t",
                   csvsep.fl = "\t")

# Run workflow
res <- fl.workflow(grodata = input, ec50 = FALSE, fit.opt = "s",
                   x_type = "time", norm_fl = TRUE,
                   dr.parameter = "max_slope.spline",
                   suppress.messages = TRUE,
                   parallelize = FALSE)

plot(res, data.type = "raw", legend.ncol = 3, basesize = 15)

Description

fl.gcBootSpline resamples the fluorescence-'x' value pairs in a dataset with replacement and performs a spline fit for each bootstrap sample.

Usage

flBootSpline(
  time = NULL,
  growth = NULL,
  fl_data,
  ID = "undefined",
  control = fl.control()
)

Arguments

Value

A gcBootSpline object containing a distribution of fluorescence parameters and a flFitSpline object for each bootstrap sample. Use plot.gcBootSpline to visualize all bootstrapping splines as well as the distribution of physiological parameters.

See Also

Other fluorescence fitting functions: flFitSpline(), flFit()

Examples

# load example dataset
input <- read_data(data.growth = system.file("lac_promoters_growth.txt", package = "QurvE"),
                   data.fl = system.file("lac_promoters_fluorescence.txt", package = "QurvE"),
                   csvsep = "\t",
                   csvsep.fl = "\t")

# Extract time and normalized fluorescence data for single sample
time <- input$time[4,]
data <- input$norm.fluorescence[4,-(1:3)] # Remove identifier columns

# Perform linear fit
TestFit <- flBootSpline(time = time,
                       fl_data = data,
                       ID = 'TestFit',
                       control = fl.control(fit.opt = 's', x_type = 'time',
                       nboot.fl = 50))

plot(TestFit, combine = TRUE, lwd = 0.5)

Description

flFit performs all computational fluorescence fitting operations based on the user input.

Usage

flFit(
  fl_data,
  time = NULL,
  growth = NULL,
  control = fl.control(),
  parallelize = TRUE,
  ...
)

Arguments

Details

Common response parameters used in dose-response analysis:

Linear fit:
- max_slope.linfit: Fluorescence increase rate
- lambda.linfit: Lag time
- dY.linfit: Maximum Fluorescence - Minimum Fluorescence
- A.linfit: Maximum fluorescence

Spline fit:
- max_slope.spline: Fluorescence increase rate
- lambda.spline: Lag time
- dY.spline: Maximum Fluorescence - Minimum Fluorescence
- A.spline: Maximum fluorescence
- integral.spline: Integral

Parametric fit:
- max_slope.model: Fluorescence increase rate
- lambda.model: Lag time
- dY.model: Maximum Fluorescence - Minimum Fluorescence
- A.model: Maximum fluorescence
- integral.model: Integral'

Value

An flFit object that contains all fluorescence fitting results, compatible with various plotting functions of the QurvE package.

See Also

Other workflows: growth.gcFit(), growth.workflow()

Other fluorescence fitting functions: flBootSpline(), flFitSpline()

Other dose-response analysis functions: growth.drBootSpline(), growth.drFitSpline(), growth.gcFit(), growth.workflow()

Examples

# load example dataset
input <- read_data(data.growth = system.file("lac_promoters_growth.txt", package = "QurvE"),
                   data.fl = system.file("lac_promoters_fluorescence.txt", package = "QurvE"),
                   csvsep = "\t",
                   csvsep.fl = "\t" )

# Define fit controls
control <- fl.control(fit.opt = "s",
             x_type = "time", norm_fl = TRUE,
             dr.parameter = "max_slope.spline",
             dr.method = "model",
             suppress.messages = TRUE)

# Run curve fitting workflow
res <- flFit(fl_data = input$norm.fluorescence,
             time = input$time,
             control = control,
             parallelize = FALSE)

summary(res)

Description

Determine maximum slopes from using a heuristic approach similar to the “growth rates made easy”-method of Hall et al. (2013).

Usage

flFitLinear(
  time = NULL,
  growth = NULL,
  fl_data,
  ID = "undefined",
  quota = 0.95,
  control = fl.control(x_type = c("growth", "time"), log.x.lin = FALSE, log.y.lin =
    FALSE, t0 = 0, min.growth = NA, lin.h = NULL, lin.R2 = 0.98, lin.RSD = 0.05, lin.dY =
    0.05, biphasic = FALSE)
)

Arguments

Value

A gcFitLinear object with parameters of the fit. The lag time is estimated as the intersection between the fit and the horizontal line with $y=y_0$, where y0 is the first value of the dependent variable. Use plot.gcFitSpline to visualize the linear fit.

• y0: Minimum fluorescence value considered for the heuristic linear method.

• dY: Difference in maximum fluorescence and minimum fluorescence

• A: Maximum fluorescence

• y0_lm: Intersection of the linear fit with the abscissa.

• max_slope: Maximum slope of the linear fit.

• tD: Doubling time.

• slope.se: Standard error of the maximum slope.

• lag: Lag X.

• x.max_start: X value of the first data point within the window used for the linear regression.

• x.max_end: X value of the last data point within the window used for the linear regression.

• x.turn: For biphasic: X at the inflection point that separates two phases.

• max.slope2: For biphasic: Slope of the second phase.

• tD2: Doubling time of the second phase.

• y0_lm2: For biphasic: Intersection of the linear fit of the second phase with the abscissa.

• lag2: For biphasic: Lag time determined for the second phase..

• x.max2_start: For biphasic: X value of the first data point within the window used for the linear regression of the second phase.

• x.max2_end: For biphasic: X value of the last data point within the window used for the linear regression of the second phase.

References

Hall, BG., Acar, H, Nandipati, A and Barlow, M (2014) Growth Rates Made Easy. Mol. Biol. Evol. 31: 232-38, DOI: 10.1093/molbev/mst187

Petzoldt T (2022). growthrates: Estimate Growth Rates from Experimental Data. R package version 0.8.3, https://CRAN.R-project.org/package=growthrates.

Examples

# load example dataset
input <- read_data(data.growth = system.file("lac_promoters_growth.txt", package = "QurvE"),
                   data.fl = system.file("lac_promoters_fluorescence.txt", package = "QurvE"),
                   csvsep = "\t",
                   csvsep.fl = "\t")

# Extract time and normalized fluorescence data for single sample
time <- input$time[4,]
data <- input$norm.fluorescence[4,-(1:3)] # Remove identifier columns

# Perform linear fit
TestFit <- flFitLinear(time = time,
                       fl_data = data,
                       ID = "TestFit",
                       control = fl.control(fit.opt = "l", x_type = "time",
                       lin.R2 = 0.95, lin.RSD = 0.1,
                       lin.h = 20))

plot(TestFit)

Description

flFitSpline performs a smooth spline fit on the dataset and determines the greatest slope as the global maximum in the first derivative of the spline.

Usage

flFitSpline(
  time = NULL,
  growth = NULL,
  fl_data,
  ID = "undefined",
  control = fl.control(biphasic = FALSE, x_type = c("growth", "time"), log.x.spline =
    FALSE, log.y.spline = FALSE, smooth.fl = 0.75, t0 = 0, min.growth = NA)
)

Arguments

Details

If biphasic = TRUE, the following steps are performed to define a second phase:

1. Determine local minima within the first derivative of the smooth spline fit.

2. Remove the 'peak' containing the highest value of the first derivative (i.e., $mu_{max}$) that is flanked by two local minima.

3. Repeat the smooth spline fit and identification of maximum slope for later time values than the local minimum after $mu_{max}$.

4. Repeat the smooth spline fit and identification of maximum slope for earlier time values than the local minimum before $mu_{max}$.

5. Choose the greater of the two independently determined slopes as $mu_{max}2$.

Value

A flFitSpline object. The lag time is estimated as the intersection between the tangent at the maximum slope and the horizontal line with $y = y_0$, where y0 is the first value of the dependent variable. Use plot.flFitSpline to visualize the spline fit and derivative over time.

• A: Maximum fluorescence.

• dY: Difference in maximum fluorescence and minimum fluorescence.

• max_slope: Maximum slope of fluorescence-vs.-x data (i.e., maximum in first derivative of the spline).

• x.max: Time at the maximum slope.

• lambda: Lag time.

• b.tangent: Intersection of the tangent at the maximum slope with the abscissa.

• max_slope2: For biphasic course of fluorescence: Maximum slope of fluorescence-vs.-x data of the second phase.

• lambda2: For biphasic course of fluorescence: Lag time determined for the second phase.

• x.max2: For biphasic course of fluorescence: Time at the maximum slope of the second phase.

• b.tangent2: For biphasic course of fluorescence: Intersection of the tangent at the maximum slope of the second phase with the abscissa.

• integral: Area under the curve of the spline fit.

See Also

Other fluorescence fitting functions: flBootSpline(), flFit()

Examples

# load example dataset
input <- read_data(data.growth = system.file("lac_promoters_growth.txt", package = "QurvE"),
                   data.fl = system.file("lac_promoters_fluorescence.txt", package = "QurvE"),
                   csvsep = "\t",
                   csvsep.fl = "\t")

# Extract time and normalized fluorescence data for single sample
time <- input$time[4,]
data <- input$norm.fluorescence[4,-(1:3)] # Remove identifier columns

# Perform linear fit
TestFit <- flFitSpline(time = time,
                       fl_data = data,
                       ID = 'TestFit',
                       control = fl.control(fit.opt = 's', x_type = 'time'))

plot(TestFit)

Description

A grofit.control object is required to perform various computations on growth data stored within grodata objects (created with read_data or parse_data). A grofit.control object is created automatically as part of growth.workflow.

Usage

growth.control(
  neg.nan.act = FALSE,
  clean.bootstrap = TRUE,
  suppress.messages = FALSE,
  fit.opt = c("a"),
  t0 = 0,
  tmax = NA,
  min.growth = NA,
  max.growth = NA,
  log.x.gc = FALSE,
  log.y.lin = TRUE,
  log.y.spline = TRUE,
  log.y.model = TRUE,
  lin.h = NULL,
  lin.R2 = 0.97,
  lin.RSD = 0.1,
  lin.dY = 0.05,
  biphasic = FALSE,
  interactive = FALSE,
  nboot.gc = 0,
  smooth.gc = 0.55,
  model.type = c("logistic", "richards", "gompertz", "gompertz.exp", "huang", "baranyi"),
  dr.method = c("model", "spline"),
  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 = c("mu.linfit", "lambda.linfit", "dY.linfit", "A.linfit", "mu.spline",
    "lambda.spline", "dY.spline", "A.spline", "mu.model", "lambda.model",
    "dY.orig.model", "A.orig.model"),
  smooth.dr = NULL,
  log.x.dr = FALSE,
  log.y.dr = FALSE,
  nboot.dr = 0,
  growth.thresh = 1.5
)

Arguments

Value

Generates a list with all arguments described above as entries.

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

Examples

# default option
control_default <- growth.control()
# user defined
control_manual <- growth.control(fit.opt = c('s', 'm'),
                                 smooth.gc = 0.5,
                                 model.type = c('huang', 'baranyi'))

Description

growth.drBootSpline resamples the values in a dataset with replacement and performs a spline fit for each bootstrap sample to determine the EC50.

Usage

growth.drBootSpline(conc, test, drID = "undefined", control = growth.control())

Arguments

Value

An object of class drBootSpline containing a distribution of growth parameters and a drFitSpline object for each bootstrap sample. Use plot.drBootSpline to visualize all bootstrapping splines as well as the distribution of EC50.

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 dose-response analysis functions: flFit(), growth.drFitSpline(), growth.gcFit(), growth.workflow()

Examples

conc <- c(0, rev(unlist(lapply(1:18, function(x) 10*(2/3)^x))),10)
response <- c(1/(1+exp(-0.7*(4-conc[-20])))+rnorm(19)/50, 0)

TestRun <- growth.drBootSpline(conc, response, drID = 'test',
               control = growth.control(log.x.dr = TRUE, smooth.dr = 0.8,
                                        nboot.dr = 50))

print(summary(TestRun))
plot(TestRun, combine = TRUE)

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)
)

Arguments

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'

Value

An object of class drFit.

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

# 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)

Description

Fit various models to response vs. concentration data of a single sample to determine the EC50.

Usage

growth.drFitModel(conc, test, drID = "undefined", control = growth.control())

Arguments

Value

A drFitModel object.

References

Christian Ritz, Florent Baty, Jens C. Streibig, Daniel Gerhard (2015). Dose-Response Analysis Using R. PLoS ONE 10(12): e0146021. DOI: 10.1371/journal.pone.0146021

Examples

conc <- c(0, rev(unlist(lapply(1:18, function(x) 10*(2/3)^x))),10)
response <- c(1/(1+exp(-0.7*(4-conc[-20])))+rnorm(19)/50, 0)

TestRun <- growth.drFitModel(conc, response, drID = 'test')

print(summary(TestRun))
plot(TestRun)

Description

growth.drFitSpline performs a smooth spline fit determines the EC50 as the concentration at the half-maximum value of the response parameter of the spline.

Usage

growth.drFitSpline(conc, test, drID = "undefined", control = growth.control())

Arguments

Details

During the spline fit with smooth.spline, higher weights are assigned to data points with a concentration value of 0, as well as to x-y pairs with a response parameter value of 0 and pairs at concentration values before zero-response parameter values.

Value

A drFitSpline object.

• EC50: Concentration at half-maximal response.

• yEC50: Response value related to EC50.

• EC50.orig: EC50 value in original scale, if a transformation was applied.

• yEC50.orig: Response value for EC50 in original scale, if a transformation was applied.

Use plot.drFitSpline to visualize the spline fit.

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

Christian Ritz, Florent Baty, Jens C. Streibig, Daniel Gerhard (2015). Dose-Response Analysis Using R. PLoS ONE 10(12): e0146021. DOI: 10.1371/journal.pone.0146021

See Also

Other dose-response analysis functions: flFit(), growth.drBootSpline(), growth.gcFit(), growth.workflow()

Examples

conc <- c(0, rev(unlist(lapply(1:18, function(x) 10*(2/3)^x))),10)
response <- c(1/(1+exp(-0.7*(4-conc[-20])))+rnorm(19)/50, 0)

TestRun <- growth.drFitSpline(conc, response, drID = 'test',
              control = growth.control(log.x.dr = TRUE, smooth.dr = 0.8))

print(summary(TestRun))

plot(TestRun)

Description

growth.gcBootSpline resamples the growth-time value pairs in a dataset with replacement and performs a spline fit for each bootstrap sample.

Usage

growth.gcBootSpline(time, data, gcID = "undefined", control = growth.control())

Arguments

Value

A gcBootSpline object containing a distribution of growth parameters and a gcFitSpline object for each bootstrap sample. Use plot.gcBootSpline to visualize all bootstrapping splines as well as the distribution of physiological parameters.

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.drFit(), growth.gcFitLinear(), growth.gcFitModel(), growth.gcFitSpline(), growth.gcFit(), growth.workflow()

Examples

# Create random growth dataset
rnd.dataset <- rdm.data(d = 35, mu = 0.8, A = 5, label = 'Test1')

# Extract time and growth data for single sample
time <- rnd.dataset$time[1,]
data <- rnd.dataset$data[1,-(1:3)] # Remove identifier columns

# Introduce some noise into the measurements
data <- data + stats::runif(97, -0.01, 0.09)

# Perform bootstrapping spline fit
TestFit <- growth.gcBootSpline(time, data, gcID = 'TestFit',
              control = growth.control(fit.opt = 's', nboot.gc = 50))

plot(TestFit, combine = TRUE, lwd = 0.5)

Description

growth.gcFit performs all computational growth fitting operations based on the user input.

Usage

growth.gcFit(time, data, control = growth.control(), parallelize = TRUE, ...)

Arguments

Value

A gcFit object that contains all growth fitting results, compatible with various plotting functions of the QurvE package.

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 workflows: flFit(), growth.workflow()

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

Other dose-response analysis functions: flFit(), growth.drBootSpline(), growth.drFitSpline(), growth.workflow()

Examples

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

Description

Determine maximum growth rates from the log-linear part of a growth curve using a heuristic approach similar to the “growth rates made easy”-method of Hall et al. (2013).

Usage

growth.gcFitLinear(
  time,
  data,
  gcID = "undefined",
  quota = 0.95,
  control = growth.control(t0 = 0, tmax = NA, log.x.gc = FALSE, log.y.lin = TRUE,
    min.growth = NA, max.growth = NA, lin.h = NULL, lin.R2 = 0.97, lin.RSD = 0.1, lin.dY
    = 0.05, biphasic = FALSE)
)

Arguments

Details

The algorithm works as follows:

1. Fit linear regressions (Theil-Sen estimator) to all subsets of h consecutive, log-transformed data points (sliding window of size h). If for example $h=5$, fit a linear regression to points 1 ... 5, 2 ... 6, 3 ... 7 and so on.

2. Find the subset with the highest slope $mu_{max}$. Do the R² and relative standard deviation (RSD) values of the regression meet the in lin.R2 and lin.RSD defined thresholds and do the data points within the regression window account for a fraction of at least lin.dY of the total growth increase? If not, evaluate the subset with the second highest slope, and so on.

3. Include also the data points of adjacent subsets that have a slope of at least $quota \cdot mu{max}$, e.g., all regression windows that have at least 95% of the maximum slope.

4. Fit a new linear model to the extended data window identified in step 3.

If biphasic = TRUE, the following steps are performed to define a second growth phase:

1. Perform a smooth spline fit on the data with a smoothing factor of 0.5.

2. Calculate the second derivative of the spline fit and perform a smooth spline fit of the derivative with a smoothing factor of 0.4.

3. Determine local maxima and minima in the second derivative.

4. Find the local minimum following $mu_{max}$ and repeat the heuristic linear method for later time values.

5. Find the local maximum before $mu_{max}$ and repeat the heuristic linear method for earlier time values.

6. Choose the greater of the two independently determined slopes as $mu_{max}2$.

Value

A gcFitLinear object with parameters of the fit. The lag time is estimated as the intersection between the fit and the horizontal line with $y=y_0$, where y0 is the first value of the dependent variable. Use plot.gcFitSpline to visualize the linear fit.

• y0: Minimum growth value considered for the heuristic linear method.

• dY: Difference in maximum growth and minimum growth.

• A: Maximum growth.

• y0_lm: Intersection of the linear fit with the abscissa.

• mumax: Maximum growth rate (i.e., slope of the linear fit).

• tD: Doubling time.

• mu.se: Standard error of the maximum growth rate.

• lag: Lag time.

• tmax_start: Time value of the first data point within the window used for the linear regression.

• tmax_end: Time value of the last data point within the window used for the linear regression.

• t_turn: For biphasic growth: Time of the inflection point that separates two growth phases.

• mumax2: For biphasic growth: Growth rate of the second growth phase.

• tD2: Doubling time of the second growth phase.

• y0_lm2: For biphasic growth: Intersection of the linear fit of the second growth phase with the abscissa.

• lag2: For biphasic growth: Lag time determined for the second growth phase..

• tmax2_start: For biphasic growth: Time value of the first data point within the window used for the linear regression of the second growth phase.

• tmax2_end: For biphasic growth: Time value of the last data point within the window used for the linear regression of the second growth phase.

References

Hall, BG., Acar, H, Nandipati, A and Barlow, M (2014) Growth Rates Made Easy. Mol. Biol. Evol. 31: 232-38, DOI: 10.1093/molbev/mst187

Petzoldt T (2022). growthrates: Estimate Growth Rates from Experimental Data. R package version 0.8.3, https://CRAN.R-project.org/package=growthrates.

Theil, H.(1992). A rank-invariant method of linear and polynomial regression analysis. In: Henri Theil’s contributions to economics and econometrics. Springer, pp. 345–381. DOI: 10.1007/978-94-011-2546-8_20

See Also

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

Examples

# Create random growth dataset
rnd.dataset <- rdm.data(d = 35, mu = 0.8, A = 5, label = "Test1")

# Extract time and growth data for single sample
time <- rnd.dataset$time[1,]
data <- rnd.dataset$data[1,-(1:3)] # Remove identifier columns

# Perform linear fit
TestFit <- growth.gcFitLinear(time, data, gcID = "TestFit",
                 control = growth.control(fit.opt = "l"))

plot(TestFit)

Description

growth.gcFitModel determines a parametric growth model that best describes the data.

Usage

growth.gcFitModel(time, data, gcID = "undefined", control = growth.control())

Arguments

Value

A gcFitModel object that contains physiological parameters and information about the best fit. Use plot.gcFitModel to visualize the parametric fit and growth equation.

• A: Maximum growth.

• dY: Difference in maximum growth and minimum growth of the fitted model.

• mu: Maximum growth rate (i.e., maximum in first derivative of the spline).

• lambda: Lag time.

• b.tangent: Intersection of the tangent at the maximum growth rate with the abscissa.

• fitpar: For some models: list of additional parameters used in the equations describing the growth curve.

• integral: Area under the curve of the parametric fit.

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.drFit(), growth.gcBootSpline(), growth.gcFitLinear(), growth.gcFitSpline(), growth.gcFit(), growth.workflow()

Examples

# Create random growth dataset
rnd.dataset <- rdm.data(d = 35, mu = 0.8, A = 5, label = 'Test1')

# Extract time and growth data for single sample
time <- rnd.dataset$time[1,]
data <- rnd.dataset$data[1,-(1:3)] # Remove identifier columns

# Perform parametric fit
TestFit <- growth.gcFitModel(time, data, gcID = 'TestFit',
                 control = growth.control(fit.opt = 'm'))

plot(TestFit, basesize = 18, eq.size = 1.5)

Description

growth.gcFitSpline performs a smooth spline fit on the dataset and determines the highest growth rate as the global maximum in the first derivative of the spline.

Usage

growth.gcFitSpline(
  time,
  data,
  gcID = "undefined",
  control = growth.control(biphasic = FALSE)
)

Arguments

Details

If biphasic = TRUE, the following steps are performed to define a second growth phase:

1. Determine local minima within the first derivative of the smooth spline fit.

2. Remove the 'peak' containing the highest value of the first derivative (i.e., $mu_{max}$) that is flanked by two local minima.

3. Repeat the smooth spline fit and identification of maximum slope for later time values than the local minimum after $mu_{max}$.

4. Repeat the smooth spline fit and identification of maximum slope for earlier time values than the local minimum before $mu_{max}$.

5. Choose the greater of the two independently determined slopes as $mu_{max}2$.

Value

A gcFitSpline object. The lag time is estimated as the intersection between the tangent at the maximum slope and the horizontal line with $y = y_0$, where y0 is the first value of the dependent variable. Use plot.gcFitSpline to visualize the spline fit and derivative over time.

• A: Maximum growth.

• dY: Difference in maximum growth and minimum growth.

• mu: Maximum growth rate (i.e., maximum in first derivative of the spline).

• tD: Doubling time.

• t.max: Time at the maximum growth rate.

• lambda: Lag time.

• b.tangent: Intersection of the tangent at the maximum growth rate with the abscissa.

• mu2: For biphasic growth: Growth rate of the second growth phase.

• tD2: Doubling time of the second growth phase.

• lambda2: For biphasic growth: Lag time determined for the second growth phase.

• t.max2: For biphasic growth: Time at the maximum growth rate of the second growth phase.

• b.tangent2: For biphasic growth: Intersection of the tangent at the maximum growth rate of the second growth phase with the abscissa.

• integral: Area under the curve of the spline fit.

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.drFit(), growth.gcBootSpline(), growth.gcFitLinear(), growth.gcFitModel(), growth.gcFit(), growth.workflow()

Examples

# Create random growth dataset
rnd.dataset <- rdm.data(d = 35, mu = 0.8, A = 5, label = 'Test1')

# Extract time and growth data for single sample
time <- rnd.dataset$time[1,]
data <- rnd.dataset$data[1,-(1:3)] # Remove identifier columns

# Perform spline fit
TestFit <- growth.gcFitSpline(time, data, gcID = 'TestFit',
                 control = growth.control(fit.opt = 's'))

plot(TestFit)

Description

growth.report requires a grofit object and creates a report in PDF and HTML format that summarizes all results.

Usage

growth.report(
  grofit,
  out.dir = tempdir(),
  out.nm = NULL,
  ec50 = FALSE,
  format = c("pdf", "html"),
  export = FALSE,
  parallelize = TRUE,
  ...
)

Arguments

Details

The template .Rmd file used within this function can be found within the QurvE package installation directory.

Value

NULL

Examples

## Not run: 
# Create random growth data set
  rnd.data <- rdm.data(d = 35, mu = 0.8, A = 5, label = 'Test1')


  # Run growth curve analysis workflow
  res <- growth.workflow(time = rnd.data$time,
                         data = rnd.data$data,
                         fit.opt = 's',
                         ec50 = FALSE,
                         export.res = FALSE,
                         suppress.messages = TRUE,
                         parallelize = FALSE)

  growth.report(res, out.dir = tempdir(), parallelize = FALSE)

## End(Not run)

Description

growth.workflow runs growth.control to create a grofit.control object and then performs all computational fitting operations based on the user input. Finally, if desired, a final report is created in PDF or HTML format that summarizes all results obtained.

Usage

growth.workflow(
  grodata = NULL,
  time = NULL,
  data = NULL,
  ec50 = TRUE,
  mean.grp = NA,
  mean.conc = NA,
  neg.nan.act = FALSE,
  clean.bootstrap = TRUE,
  suppress.messages = FALSE,
  fit.opt = c("a"),
  t0 = 0,
  tmax = NA,
  min.growth = NA,
  max.growth = NA,
  log.x.gc = FALSE,
  log.y.lin = TRUE,
  log.y.spline = TRUE,
  log.y.model = TRUE,
  biphasic = FALSE,
  lin.h = NULL,
  lin.R2 = 0.97,
  lin.RSD = 0.1,
  lin.dY = 0.05,
  interactive = FALSE,
  nboot.gc = 0,
  smooth.gc = 0.55,
  model.type = c("logistic", "richards", "gompertz", "gompertz.exp", "huang", "baranyi"),
  dr.method = c("model", "spline"),
  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"),
  growth.thresh = 1.5,
  dr.have.atleast = 6,
  dr.parameter = c("mu.linfit", "lambda.linfit", "dY.linfit", "A.linfit", "mu.spline",
    "lambda.spline", "dY.spline", "A.spline", "mu.model", "lambda.model",
    "dY.orig.model", "A.orig.model"),
  smooth.dr = 0.1,
  log.x.dr = FALSE,
  log.y.dr = FALSE,
  nboot.dr = 0,
  report = NULL,
  out.dir = NULL,
  out.nm = NULL,
  export.fig = FALSE,
  export.res = FALSE,
  parallelize = TRUE,
  ...
)

Arguments

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'

Value

A grofit object that contains all computation results, compatible with various plotting functions of the QurvE package and with growth.report.

See Also

Other workflows: flFit(), growth.gcFit()

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

Other dose-response analysis functions: flFit(), growth.drBootSpline(), growth.drFitSpline(), growth.gcFit()

Examples

# 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
  res <- growth.workflow(time = rnd.data$time,
                         data = rnd.data$data,
                         fit.opt = 's',
                         ec50 = FALSE,
                         export.res = FALSE,
                         suppress.messages = TRUE,
                         parallelize = FALSE)

# Load custom dataset
  input <- read_data(data.growth = system.file('2-FMA_toxicity.csv', package = 'QurvE'))

  res <- growth.workflow(grodata = input,
                         fit.opt = 's',
                         ec50 = TRUE,
                         export.res = FALSE,
                         suppress.messages = TRUE,
                         parallelize = FALSE)

  plot(res)

Description

Find indices of maxima an minima in a data series

Usage

inflect(x, threshold = 1)

Arguments

Value

a list with 1. a vector of minima and 2. a vector of maxima.

Author(s)

Evan Friedland

Examples

# Pick a desired threshold to plot up to
n <- 3
# Generate Data
randomwalk <- 100 + cumsum(rnorm(50, 0.2, 1)) # climbs upwards most of the time
bottoms <- lapply(1:n, function(x) inflect(randomwalk, threshold = x)$minima)
tops <- lapply(1:n, function(x) inflect(randomwalk, threshold = x)$maxima)
# Color functions
cf.1 <- grDevices::colorRampPalette(c('pink','red'))
cf.2 <- grDevices::colorRampPalette(c('cyan','blue'))
plot(randomwalk, type = 'l', main = 'Minima & Maxima\nVariable Thresholds')
for(i in 1:n){
  points(bottoms[[i]], randomwalk[bottoms[[i]]], pch = 16, col = cf.1(n)[i], cex = i/1.5)
}
for(i in 1:n){
  points(tops[[i]], randomwalk[tops[[i]]], pch = 16, col = cf.2(n)[i], cex = i/1.5)
}
legend('topleft', legend = c('Minima',1:n,'Maxima',1:n),
       pch = rep(c(NA, rep(16,n)), 2), col = c(1, cf.1(n),1, cf.2(n)),
       pt.cex =  c(rep(c(1, c(1:n) / 1.5), 2)), cex = .75, ncol = 2)

Description

lm_window performs a linear regression with the Theil-Sen estimator on a subset of data.

Usage

lm_parms(m)

lm_window(x, y, i0, h = 5)

Arguments

Value

linear model object of class lm (lm_window) resp. vector with parameters of the fit (lm_parms).

References

Hall, B. G., H. Acar and M. Barlow 2013. Growth Rates Made Easy. Mol. Biol. Evol. 31: 232-238 doi:10.1093/molbev/mst197

Examples

# Create random growth dataset
rnd.dataset <- rdm.data(d = 35, mu = 0.8, A = 5, label = "Test1")

# Extract time and growth data for single sample
time <- rnd.dataset$time[1,]
data <- as.numeric(rnd.dataset$data[1,-(1:3)]) # Remove identifier columns
data.log <- log(data/data[1])

# Perform linear fit on 8th window of size 8
linreg <- lm_window(time, data.log, 8, h=8)

summary(linreg)

lm_parms(linreg)

Description

Function to estimate the area under a curve given as x and y(x) values

Usage

low.integrate(x, y)

Arguments

Details

The function uses the the R internal function smooth.spline.

Value

Numeric value: Area under the smoothed spline.

See Also

smooth.spline

Examples

# Create random growth dataset
rnd.dataset <- rdm.data(d = 35, mu = 0.8, A = 5, label = 'Test1')

# Extract time and growth data for single sample
time <- rnd.dataset$time[1,]
data <- as.numeric(rnd.dataset$data[1,-(1:3)]) # Remove identifier columns

plot(time, data)

print(low.integrate(time, data))

Description

parse_data takes a raw export file from a plate reader experiment (or similar device), extracts relevant information and parses it into the format required to run growth.workflow. If more than one read type is found the user is prompted to assign the correct reads to growth or fluorescence.

Usage

parse_data(
  data.file = NULL,
  map.file = NULL,
  software = c("Gen5", "Gen6", "Biolector", "Chi.Bio", "GrowthProfiler", "Tecan",
    "VictorNivo", "VictorX3"),
  convert.time = NULL,
  sheet.data = 1,
  sheet.map = 1,
  csvsep.data = ";",
  dec.data = ".",
  csvsep.map = ";",
  dec.map = ".",
  subtract.blank = TRUE,
  calib.growth = NULL,
  calib.fl = NULL,
  calib.fl2 = NULL,
  fl.normtype = c("growth", "fl2")
)

Arguments

Details

Metadata provided as map.file needs to have the following layout:

Value

A grodata object suitable to run growth.workflow. See read_data for its structure.

Examples

if(interactive()){
grodata <- parse_data(data.file = system.file("fluorescence_test_Gen5.xlsx", package = "QurvE"),
                      sheet.data = 1,
                      map.file = system.file("fluorescence_test_Gen5.xlsx", package = "QurvE"),
                      sheet.map = "mapping",
                      software = "Gen5",
                      convert.time = "y = x * 24", # convert days to hours
                      calib.growth = "y = x * 3.058") # convert absorbance to OD values
}

Description

Extract relevant data from a raw data export file generated with the "Gen5" or "Gen6" software.

Usage

parse_Gen5Gen6(input)

Arguments

Value

a list of length two containing growth and/or fluorescence dataframes in the first and second element, respectively. The first column in these dataframes represents a time vector.

Examples

if(interactive()){
input <- read_file(filename = system.file("fluorescence_test_Gen5.xlsx", package = "QurvE") )
parsed <- parse_Gen5Gen6(input)
}

Description

Extract relevant data from a raw data export file generated from the software of Perkin Elmer's "Victor Nivo" plate readers.

Usage

parse_victornivo(input)

Arguments

Value

a list of length two containing growth and/or fluorescence dataframes in the first and second element, respectively. The first column in these dataframes represents a time vector.

Examples

if(interactive()){
input <- read_file(filename = system.file("nivo_output.csv", package = "QurvE"), csvsep = "," )
parsed <- parse_victornivo(input)
}

Description

Extract relevant data from a raw data export file generated from the software of Perkin Elmer's "Victor X3" plate readers.

Usage

parse_victorx3(input)

Arguments

Value

a list of length two containing growth and/or fluorescence dataframes in the first and second element, respectively. The first column in these dataframes represents a time vector.

Examples

if(interactive()){
input <- read_file(filename = system.file("victorx3_output.txt", package = "QurvE") )
parsed <- parse_victorx3(input)
}

Description

plot.dr_parameter gathers parameters from the results of a dose-response analysis and compares a chosen parameter between each condition in a column plot. Error bars represent the 95% confidence interval (only shown for > 2 replicates).

Usage

## S3 method for class 'dr_parameter'
plot(
  x,
  param = c("EC50", "EC50.Estimate", "y.max", "y.min", "fc", "K", "n", "yEC50",
    "drboot.meanEC50", "drboot.meanEC50y", "EC50.orig", "yEC50.orig"),
  names = NULL,
  exclude.nm = NULL,
  basesize = 12,
  reference.nm = NULL,
  label.size = NULL,
  plot = TRUE,
  export = FALSE,
  height = 7,
  width = NULL,
  out.dir = NULL,
  out.nm = NULL,
  ...
)