flowQ (version 1.32.0)

qaProcess.cellnumber: Create QA process of type 'cellnumber'

Description

This function takes a flowSet as input and creates all necessary output for a 'cellnumber' type QA process. Objects created by this function can be laid out as HTML using writeQAReport.

Usage

qaProcess.cellnumber(set, grouping=NULL, outdir, cFactor=2, absolute.value=NULL, two.sided = FALSE, name="cell number", sum.dimensions=NULL, pdf=TRUE, ...)

Arguments

set

A flowSet.

grouping

A character vector defining one of the variables in the phenoData of set used as a grouping variable. If this argument is used and if absolute.value is NULL, outlier detection will be performed within groups rather than across all samples.

outdir

The directory to which the graphical output is to be saved. If multiple QA processes are to be combined, make sure to use the same directory for all of them.

cFactor

The outlier threshold at which the QA criterion is considered to have failed. This is essentially the factor of standard deviations away from the average number of cells per sample, either in both directions if two.sided=TRUE or only towards smaller event numbers if two.sided=FALSE.

absolute.value

An absolute event count below which the QA criterion is considered to be failed. If this argument is not NULL, cFactor and two.sided are ignored.

two.sided

Perform a two-sided outlier detection, i.e., report both unproportionally high and low event numbers.

name

The name of the process used for the headings in the HTML output.

sum.dimensions

The dimensions of the pdf deviced in inches used for the summary plot.

pdf

Logical indicating whether to create vectorized versions of images for this quality process. This should be set to FALSE if disk space is critical, since the pdf versions of the image consume much more space on the hard drive compared to the bitmap version.

...

Further arguments.

Value

An object of class qaProcess.

Details

QA processes of type 'cellnumber' detect aberations in the number of events per sample. These are either determined dynamically as outliers from the typical distribution of event counts for the whole set, or, if absolute.value is not NULL, by an absolute cutoff value. If there is a natural grouping among the samples, this can be specified using the grouping argument. In this case, the outlier detection will be performed within its respective group for a particular sample. For more details on how to layout qaProcess objects to HTML, see writeQAReport and qaReport.

See Also

writeQAReport, qaReport, qaProcess, qaProcess.marginevents, qaProcess.timeflow, qaProcess.timeline

Examples

## Not run: 
# data(GvHD)
# dest <- file.path(tempdir(), "flowQ")
# qp <- qaProcess.cellnumber(GvHD, outdir=dest, cFactor=2)
# qp
# ## End(Not run)