clean.fp: Remove Non-stationary Events

In rogerswt/wadeTools: Wade's Hodgepodge of Flow Cytometry Functions

Remove Non-stationary Events

Description

This function examines time-dependent data. It looks for intervals of acquisition where there is some departure from the norm (initial stabilization, temporary bubbles or clogs, running out of sample at the end), and marks them for deletion.

Usage

clean.fp(ff, parameters = NULL, nbin = 96, show = FALSE)

Arguments

ff	The input flowFrame
parameters	Parameters to consider in the fingerprinting
nbin	Number of time slices
show	Logical, should we show our work?

Details

Thus function uses Cytometric Fingerprinting flowFP to detect similarities/differences in the multivariate probability distribution, over time. It does this by "slicing" the flowFrame into nbin slices, each containing an equal number of events (thus it's not equal time slices, but equal probability slices).

A recommendation is to use at least one parameter from each laser, in order to be sensitive to laser fluctuations. I often use SSC-A plus one FL parameter from each laser.

Value

The original flowFrame, with an additional parameter called clean, which has the value 1 for events to be kept, and 0 for events that should be ditched. It's up to the user to apply this filter.

Examples

# get some example data
filename = system.file("extdata", "example1.fcs", package = "wadeTools")
ff = get_sample(filename)

params = colnames(ff)[c(4, 7, 11, 13, 15)]   # SSC-A plus one from each laser

# tag events for removal, plot a picture
ff_tmp = clean.fp(ff = ff, parameters = params, show = TRUE)

# filter out the events in "bad" slices
ff_clean = Subset(ff, rectangleGate("clean" = c(0.5, Inf))

rogerswt/wadeTools documentation built on Feb. 16, 2023, 7:47 a.m.