qaProcess.cellnumber {flowQ} | R Documentation

## 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=c(7,7), 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.

### 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`.

### Value

An object of class `qaProcess`.

### Author(s)

Florian Hahne

### See Also

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

### Examples

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

[Package flowQ version 1.4.0 Index]