gaussNorm: Per-channel normalization based on landmark registration
In flowStats: Statistical methods for the analysis of flow cytometry data

Description Usage Arguments Details Value Author(s) Examples

This funciton normalizes a set of flow cytometry data samples by identifying and aligning the high density regions (landmarks or peaks) for each channel. The data of each channel is shifted in such a way that the identified high density regions are moved to fixed locations called base landmarks.

1 2	gaussNorm (flowset, channel.names, max.lms=2, base.lms=NULL, peak.density.thr=0.05, peak.distance.thr=0.05, debug=FALSE, fname='')

`flowset`	A `flowSet`.
`channel.names`	A character vector of flow parameters in `flowset` to be normalized.
`max.lms`	A numeric vector of the maximum number of base landmarks to be used for normalizing each channel. If it has only one value that will be used as the maximum number of base landmarks for all the channels.
`base.lms`	A list of vector for each channel that contains the base landmarks for normalizing that channel. If not specified the base landmarks are computed from the set of extracted landmarks.
`peak.density.thr`	The peaks with density value less than "peak.density.thr times maximum peak density" are discarded.
`peak.distance.thr`	The sequences of peaks that are located closer than "peak.distance.thr times range of data" are identified. Then for each sequence only one peak (the one with the highest intensity value) is used as a landmark. In other words no two landmarks are located closer than "peak.distance.thr times range of data" to each other.
`debug`	Logical. Forces the function to draw before and after normalization plots for each sample. The plot of the i-th sample is stored in `paste(fname, i)` file.
`fname`	The pre- and post- normalization plots of the i-th sample is stored in `paste(fname, i)` file if debug is set to `TRUE`. If default value is used the plots are drawn on separate X11 windows for each sample. In this case, the function waits for a user input to draw the plots for the next sample.

Normalization is archived in three phases: (i) identifying high-density regions (landmarks) for each flowFrame in the flowSet for a single channel; (ii) computing the best matching between the landmarks and a set of fixed reference landmarks for each channel called base landmarks; (iii) manipulating the data of each channel in such a way that each landmark is moved to its matching base landmark. Please note that this normalization is on a channel-by-channel basis. Multiple channels are normalized in a loop.

A list with items flowset: normalized flowSet. confidence: a confidence measure of the normalization procedure.

Alireza Hadj Khodabakhshi

library(flowCore)
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)
datr <- gaussNorm(dat, "CD8")$flowset
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)
}