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 flTable can be obtained by running flFit with dr.method = 'model' as argument in the fl.control object.
control: A fl.control object created with fl.control, defining relevant fitting options.
dr.method: (Character) Perform either a smooth spline fit on response parameter vs. concentration data ('spline') or fit a biosensor response model with 'model' (proposed by Meyer et al., 2019).
dr.parameter: (Character or numeric) The response parameter in the output table to be used for creating a dose response curve. See fl.drFit for further details. Default: 'max_slope.spline', which represents the maximum slope of the spline fit Typical options include: 'max_slope.linfit', 'dY.linfit', 'max_slope.spline', and 'dY.spline'.

Value

An object of class drFit.

raw.data: Data that passed to the function as flTable.
drTable: Dataframe containing condition identifiers, fit options, and results of the dose-response analysis.
drFittedModels: List of all drFitModel objects generated by the call of fl.drFitModel for each distinct experiment.
control: Object of class fl.control created with the call of fl.control.

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'

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

# \donttest{
# Load example dataset
input <- read_data(data.fl = system.file('lac_promoters.xlsx', package = 'QurvE'),
                   sheet.fl = 2 )
#> Sample data are stored in columns. If they are stored in row format, please run read_data() with data.format = 'row'.

# 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'))
#> 
#> === Dose-Response Estimation via Model Fit ==============================
#> ---------------------------------------------------
#> --> Checking data ...
#> --> Number of distinct tests found: 3 
#> --> Valid datasets per test: 
#>       TestID         Number
#>       pSEVA634rκ∙GFP 7     
#>       pSEVA634r∙GFP  7     
#>       pSEVA634∙GFP   7     
#> 
#> 
#> === Dose response curve estimation ================
#> --- EC 50 -----------------------------------------
#> --> pSEVA634rκ∙GFP
#> sensitivity: 0.0104 | yEC50: 105 | fold change: 4.72 | leakiness: 36.7
#> 
#> 
#> 
#> 
#> === Dose response curve estimation ================
#> --- EC 50 -----------------------------------------
#> --> pSEVA634r∙GFP
#> sensitivity: 0.0531 | yEC50: 75 | fold change: Inf | leakiness: 0
#> 
#> 
#> 
#> 
#> === Dose response curve estimation ================
#> --- EC 50 -----------------------------------------
#> --> pSEVA634∙GFP
#> sensitivity: 0.0463 | yEC50: 190 | fold change: 11.62 | leakiness: 30.2
#> 
#> 

# Inspect results
summary(drFit)
#>             Test log.x log.y     yEC50    y.min    y.max        fc          K
#> 1 pSEVA634rκ∙GFP FALSE FALSE 104.83198 36.65873 173.0052  4.719346 0.01036183
#> 2  pSEVA634r∙GFP FALSE FALSE  75.10918  0.00000 150.2184       Inf 0.05312423
#> 3   pSEVA634∙GFP FALSE FALSE 190.27551 30.16190 350.3891 11.616942 0.04626945
#>           n yEC50.orig     K.orig             test
#> 1 11.576981  104.83198 0.01036183 max_slope.linfit
#> 2  3.383541   75.10918 0.05312423 max_slope.linfit
#> 3  1.684616  190.27551 0.04626945 max_slope.linfit
plot(drFit)



# }