sigmoidalFitFormula: sigmoidalFitFormula

In wilkelab/sicegar: Analysis of Single-Cell Viral Growth Curves

Description

Calculates intesities for given time points (x) by using sigmoidal fit model and parameters (maximum, slopeParam, and midpoint).

Usage

sigmoidalFitFormula(x, maximum, slopeParam, midPoint)

Arguments

x	the "time" (time) column of the dataframe.
maximum	the maximum intensity that the sigmoidal function can reach while time approaches infinity.
slopeParam	the slope parameter of the sigmoidal function at the steppest point.
midPoint	the x axis value of the steppest point in the function.

Value

Returns the predicted intensities for given time points with the given sigmoidal fit parameters.

Examples

time <- seq(3, 24, 0.5)

#simulate intensity data and add noise
noise_parameter <- 0.1
intensity_noise <- stats::runif(n = length(time), min = 0, max = 1) * noise_parameter
intensity <- sigmoidalFitFormula(time, maximum = 4, slopeParam = 1, midPoint = 8)
intensity <- intensity + intensity_noise

dataInput <- data.frame(intensity = intensity, time = time)
normalizedInput <- normalizeData(dataInput)
parameterVector <- sigmoidalFitFunction(normalizedInput, tryCounter = 2)

#Check the results
if(parameterVector$isThisaFit){
 intensityTheoretical <- sigmoidalFitFormula(time,
                                             maximum = parameterVector$maximum_Estimate,
                                             slopeParam = parameterVector$slopeParam_Estimate,
                                             midPoint = parameterVector$midPoint_Estimate)

 comparisonData <- cbind(dataInput, intensityTheoretical)

 require(ggplot2)
 ggplot(comparisonData) +
   geom_point(aes(x = time, y = intensity)) +
   geom_line(aes(x = time, y = intensityTheoretical)) +
   expand_limits(x = 0, y = 0)
}

if(!parameterVector$isThisaFit){
  print(parameterVector)
}

wilkelab/sicegar documentation built on May 8, 2021, 11:08 p.m.