fl.drFit {QurvE} | R Documentation |
Fit a biosensor model (Meyer et al., 2019) to response vs. concentration data
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
flTable |
A dataframe containing the data for the dose-response model estimation. Such table of class |
control |
A |
dr.method |
(Character) Perform either a smooth spline fit on response parameter vs. concentration data ( |
dr.parameter |
(Character or numeric) The response parameter in the output table to be used for creating a dose response curve. See |
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
.
raw.data |
Data that passed to the function as |
drTable |
Dataframe containing condition identifiers, fit options, and results of the dose-response analysis. |
drFittedModels |
List of all |
control |
Object of class |
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)