R: Perform a smooth spline fit on fluorescence data

flFitSpline {QurvE}

R Documentation

Perform a smooth spline fit on fluorescence data

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

`time`	Vector of the independent variable: time (if `x_type = 'time'` in `fl.control` object.
`growth`	Vector of the independent variable: growth (if `x_type = 'growth'` in `fl.control` object.
`fl_data`	Vector of dependent variable: fluorescence.
`ID`	(Character) The name of the analyzed sample.
`control`	A `fl.control` object created with `fl.control`, defining relevant fitting options.
`biphasic`	(Logical) Shall `flFitLinear` and `flFitSpline` try to extract fluorescence parameters for two different phases (as observed with, e.g., regulator-promoter systems with varying response in different growth stages) (`TRUE`) or not (`FALSE`)?
`x_type`	(Character) Which data type shall be used as independent variable? Options are `'growth'` and `'time'`.
`log.x.spline`	(Logical) Indicates whether ln(x+1) should be applied to the independent variable for spline fits. Default: `FALSE`.
`log.y.spline`	(Logical) Indicates whether ln(y/y0) should be applied to the fluorescence data for spline fits. Default: `FALSE`
`smooth.fl`	(Numeric) Parameter describing the smoothness of the spline fit; usually (not necessary) within (0;1]. `smooth.gc=NULL` causes the program to query an optimal value via cross validation techniques. Especially for datasets with few data points the option `NULL` might cause a too small smoothing parameter. This can result a too tight fit that is susceptible to measurement errors (thus overestimating slopes) or produce an error in `smooth.spline` or lead to overfitting. The usage of a fixed value is recommended for reproducible results across samples. See `smooth.spline` for further details. Default: `0.55`
`t0`	(Numeric) Minimum time value considered for linear and spline fits.
`min.growth`	(Numeric) Indicate whether only values above a certain threshold should be considered for linear regressions or spline fits.

Details

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

Determine local minima within the first derivative of the smooth spline fit.
Remove the 'peak' containing the highest value of the first derivative (i.e., mu_{max}) that is flanked by two local minima.
Repeat the smooth spline fit and identification of maximum slope for later time values than the local minimum after mu_{max}.
Repeat the smooth spline fit and identification of maximum slope for earlier time values than the local minimum before mu_{max}.
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.

`x.in`	Raw x values provided to the function as `time` or `growth`.
`fl.in`	Raw fluorescence data provided to the function as `fl_data`.
`raw.x`	Filtered x values used for the spline fit.
`raw.fl`	Filtered fluorescence values used for the spline fit.
`ID`	(Character) Identifies the tested sample.
`fit.x`	Fitted x values.
`fit.fl`	Fitted fluorescence values.
`parameters`	List of determined parameters.

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.

`spline`	`smooth.spline` object generated by the `smooth.spline` function.
`spline.deriv1`	list of time ('x') and growth ('y') values describing the first derivative of the spline fit.
`reliable`	(Logical) Indicates whether the performed fit is reliable (to be set manually).
`fitFlag`	(Logical) Indicates whether a spline fit was successfully performed on the data.
`fitFlag2`	(Logical) Indicates whether a second phase was identified.
`control`	Object of class `fl.control` containing list of options passed to the function as `control`.

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)

[Package QurvE version 1.1.1 Index]