rangeGate: Find most likely separation between positive and negative...
In flowStats: Statistical methods for the analysis of flow cytometry data

Description Usage Arguments Details Value Methods Author(s) See Also Examples

The function tries to find a reasonable split point between the two hypothetical cell populations "positive" and "negative".

rangeGate(x, stain, alpha="min", sd=2, plot=FALSE, borderQuant=0.1,
absolute=TRUE, filterId="defaultRectangleGate", positive=TRUE,
refLine=NULL, simple = FALSE,...)

rangeFilter(stain, alpha="min", sd=2, borderQuant=0.1, 
filterId="defaultRangeFilter")

`x`	A `flowSet` or `flowFrame`.
`stain`	A character scalar giving the flow parameter for which to compute the separation.
`alpha`	A tuning parameter that controls the location of the split point between the two populations. This has to be a numeric in the range `[0,1]`, where values closer to 0 will shift the split point closer to the negative population and values closer to 1 will shift towards the positive population. Additionally, the value of `alpha` can be `"min"`, in which case the split point will be selected as the area of lowest local density between the two populations.
`sd`	For the case where there is only a single population, the algorithm falls back to esitmating the mode of this population and a robust measure of the variance of it distribution. The `sd` tuning parameter controls how far away from the mode the split point is set.
`plot`	Create a plot of the results of the computation.
`borderQuant`	Usualy the instrument is set up in a way that the positive population is somewhere on the high end of the measurement range and the negative population is on the low end. This parameter allows to disregard populations with mean values in the extreme quantiles of the data range. It's value should be in the range `[0,1]`.
`absolute`	Logical controling whether to classify a population (positive or negative) relative to the theoretical measurment range of the instrument or the actual range of the data. This can be set to `TRUE` if the alignment of the measurment range is not optimal and the bulk of the data is on one end of the theoretical range.
`filterId`	Character, the name assigned to the resulting filter.
`positive`	Create a range gate that includes the positive (`TRUE`) or the negative (`FALSE`) population.
`refLine`	Either `NULL` or a numeric of lenth 1. If `NULL`, this parameter is ignored. When it is set to a numeric, the minor sub-population (if any) below this reference line will be igored while determining the separator between positive and negative.
`simple`	`logical` scalar indicating whether to use a simple peak finding version of density1d algorithm.
`...`	Further arguments.

The algorithm first tries to identify high density regions in the data. If the input is a flowSet, density regions will be computed on the collapsed data, hence it should have been normalized before (see warpSet for one possible normalization technique). The high density regions are then clasified as positive and negative populations, based on their mean value in the theoretical (or absolute if argument absolute=TRUE) measurement range. In case there are only two high-density regions the lower one is usually clasified as the negative populations, however the heuristics in the algorithm will force the classification towards a positive population if the mean value is already very high. The absolute and borderQuant arguments can be used to control this behaviour. The split point between populations will be drawn at the value of mimimum local density between the two populations, or, if the alpha argument is used, somewhere between the two populations where the value of alpha forces the point to be closer to the negative (0 - 0.5) or closer to the positive population (0.5 - 1).

If there is only a single high-density region, the algorithm will fall back to estimating the mode of the distribution (hubers) and a robust measure of it's variance and, in combination with the sd argument, set the split point somewhere in the right or left tail, depending on the classification of the region.

For more than two populations, the algorithm will still classify each population into positive and negative and compute the split point between those clusteres, similar to the two population case.

The rangeFilter class and constructor provide the means to treat rangeGate as regular flowCore filters.

A range gate, more explicitely an object of class rectangleGate.

%in%: signature(x = "flowFrame", table = "rangeFilter"): the work horse for doing the actual filtering. Internally, this simply calls the rangeGate function.

Florian Hahne, Kyongryun Lee

warpSet, rangeGate, rectangleGate

library(flowCore)
data(GvHD)
dat <- GvHD[pData(GvHD)$Patient==10]
dat <- transform(dat, "FL4-H"=asinh(`FL4-H`), "FL3-H"=asinh(`FL3-H`))
rg <- rangeGate(dat, "FL4-H", plot=TRUE)
rg
split(dat, rg)

## Test rangeGate when settting refLine=0; it does not do anything since
## there is no sub-population below zero.
rangeGate(dat, "FL4-H", plot=FALSE, refLine=0)

rf <- rangeFilter("FL4-H")
filter(dat, rf)