warpSet {flowStats} | R Documentation

## Normalization based on landmark registration

### Description

This function will perform a normalization of flow cytometry data based on warping functions computed on high-density region landmarks for individual flow channels.

### Usage

    warpSet(x, stains, grouping = NULL, monwrd = TRUE, subsample=NULL,
    peakNr=NULL, clipRange=0.01, nbreaks=11, fres, ...)

### Arguments

`x` | A `flowSet`.
`stains` | A character vector of flow parameters in `x` to be normalized.
`grouping` | A character indicating one of the phenotypic variables in the `phenoData` slot of `x` used as a grouping factor. The within-group and between-group variance is computed and a warning is issued in case the latter is bigger than the former, indicating the likely removal of signal by the normalization procedure.
`monwrd` | Logical. Compute strictly monotone warping functions. This gets directly passed on to `landmarkreg`.
`subsample` | Numeric. Reduce the number of events in each `flowSet` by sub sampling for all density estimation steps and the calculation of the warping functions. This can increase computation time for large data sets, however it might reduce the accuracy of the density estimates. To be used with care.
`peakNr` | Numeric scalar. Force a fixed number of peaks to use for the normalization.
`clipRange` | Only use peaks within a clipped data range. Essentially, the number indicates the percent of clipping on both sides of the data range, e.g. `min(x) - 0.01 * diff(range(x))`.
`nbreaks` | The number of spline sections used to approximate the data. Higher values produce more accurate results, however this comes with the cost of increaseqd computing times. For most data, the default setting is good enough.
`fres` | A named list of `filterResultList` objects. This can be used to speed up the process since the `curv1Filter` step can take quite some time.
`...` | Further arguments that are passed on to `landmarkreg`.

### Details

Normalization is archived by first identifying high-density regions (landmarks) for each `flowFrame` in the `flowSet` for a single channel and subsequently by computing warping functions for each `flowFrame` that best align these landmarks. This is based on the algorithm implemented in the `landmarkreg` function in the `fda` package. An intermediate step classifies the high-density regions, see `landmarkMatrix` for details.

Please note that this normalization is on a channel-by-channel basis. Multiple channels are normalized in a loop.

### Value

The normalized `flowSet`.

### Note

We currently use a patched fda version.

### Author(s)

Florian Hahne

### References

J.O. Ramsay and B.W. Silverman: Applied Functional Data Analysis, Springer 2002

### See Also

`curv1Filter`, `landmarkMatrix`

### Examples

    data(ITN)
    dat <- transform(ITN, "CD4"=asinh(CD4), "CD3"=asinh(CD3), "CD8"=asinh(CD8))
    lg <- lymphGate(dat, channels=c("CD3", "SSC"),
    preselection="CD4",scale=1.5)
    dat <- Subset(dat, lg$n2gate)
    datr <- warpSet(dat, "CD8", grouping="GroupID", monwrd=TRUE)
    if(require(flowViz)){
    d1 <- densityplot(~CD8, dat, main="original", filter=curv1Filter("CD8"))
    d2 <- densityplot(~CD8, datr, main="normalized", filter=curv1Filter("CD8"))
    plot(d1, split=c(1,1,2,1))
    plot(d2, split=c(2,1,2,1), newpage=FALSE)
    }

[Package flowStats version 1.2.0 Index]