R: Compute the split-scale transformation describe by FL. Battye
splitScaleTransform
R Documentation
Compute the split-scale transformation describe by FL. Battye
Description
The split scale transformation described by Francis L. Battye [B15] (Figure 13) consists of a logarithmic
scale at high values and a linear scale at low values with a fixed transition point chosen so that the slope
(first derivative) of the transform is continuous at that point. The scale extends to the negative of the
transition value that is reached at the bottom of the display.
A name to assign to the transformation. Used by the transform/filter integration routines.
maxValue
Maximum value the transformation is applied to, e.g., 1023
transitionChannel
Where to split the linear versus the
logarithmic transformation, e.g., 64
r
Range of the logarithm part of the display, ie. it may be
expressed as the maxChannel - transitionChannel considering the
maxChannel as the maximum value to be obtained after the transformation.
Value
Returns values giving the inverse of the biexponential within a certain tolerance. This function should be used with care as numerical inversion routines often have problems with the inversion process due to the large range of values that are essentially 0. Do not be surprised if you end up with population splitting about w and other odd artifacts.
Author(s)
N. LeMeur
References
Battye F.L. A Mathematically Simple Alternative to the Logarithmic Transform for Flow
Cytometric Fluorescence Data
Displays. http://www.wehi.edu.au/cytometry/Abstracts/AFCG05B.html.
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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> library(flowCore)
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/flowCore/splitScaleTransform.Rd_%03d_medium.png", width=480, height=480)
> ### Name: splitScaleTransform
> ### Title: Compute the split-scale transformation describe by FL. Battye
> ### Aliases: splitScaleTransform
> ### Keywords: methods
>
> ### ** Examples
>
>
> data(GvHD)
> ssTransform <- splitScaleTransform("mySplitTransform")
> after.1 <- transform(GvHD, transformList('FSC-H', ssTransform))
>
> opar = par(mfcol=c(2, 1))
> plot(density(exprs(GvHD[[1]])[, 1]), main="Original")
> plot(density(exprs(after.1[[1]])[, 1]), main="Split-scale Transform")
>
>
>
>
>
> dev.off()
null device
1
>