qaProcess.cellnumber {flowQ} | R Documentation |
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
.
qaProcess.cellnumber(set, grouping=NULL, outdir, cFactor=2, absolute.value=NULL, two.sided = FALSE, name="cell number", sum.dimensions=c(7,7), pdf=TRUE, ...)
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. |
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
.
An object of class qaProcess
.
Florian Hahne
writeQAReport
, qaReport
,
qaProcess
, qaProcess.marginevents
,
qaProcess.timeflow
, qaProcess.timeline
## Not run: data(GvHD) dest <- tempdir() qp <- qaProcess.cellnumber(GvHD, outdir=dest, cFactor=2) qp ## End(Not run)