QUALIFIER (version 1.16.1)

plot-methods: plot the statistics for a particular cell population of a group of samples

Description

plot the statistics for a particular cell population of a group of samples,this method is usually called after qaCheck to visualize the QA results.

Usage

"plot"(x, y, ...)

Arguments

x

a qaTask object

y

a formula describing the variables to be used for plotting. see qaCheck for more details.

...

arguments to control the output.

pop:a character scalar indicating the population name.If provided,it overwrites the pop slot in qaTask object.

subset:a logical expression as a filter. see qaCheck for more details.

width,height:size specification for the svg output.

dest: a character specifying the output path. It is NULL by default, which indicates using the regular R device as the output. Otherwise it outputs to a svg file.

plotAll: a logical/character scalar indicating whether to plot the 1D/2D density plot for all the individual FCS files together with the summary plot(either xyplot or bwplot). It is only valid when dest is specified as non-null path. It is FALSE by default,indicating that only the FCS files that are marked as outliers by qaCheck are plotted. If TRUE, all FCS files are plotted ,which should be used cautously since it could be time consuming for a large dataset. When it is "none",no scatter plot will be generated.

scatterPlot: a logical scalar. When TRUE, the density(scatter) plot is plotted instead of the summary plot(xyplot/bwplot)

scatterPar: A list storing all the fliwViz arguments. see xyplot

par:A list storing all the lattice arguments.If provided,it overwrites the par slot of qaTask object.

outerStrip: a logical indicating whether to enable useOuterStrips

strip.lines,strip.left.lines: arguments passed to useOuterStrips

Details

The method does the same thing as qaCheck in terms of parsing the formula and selecting the gated population,statistics and subsetting the samples. The difference is that it reads the outliers detection results saved in database and hightlight them in the summary plots. Two kinds of lattice plots are currently supported:xyplot and bwplot(boxplot),depends on the plotType in qaTask object. When the output path is provided by dest, the svg plot is generated. In svg plot, each dot or box (or only the one marked as outliers) is annotated by the tooltip or hyperlink.which further points to the individual density plot of the gated population.

with scatterPlot and subset arguments, scatter plots can be generated for the selected FCS files or sample groups,which allows users to investigate the individual outlier groups or files.

See Also

qaCheck,qaReport

Examples

## Not run: 
# 
# data("ITNQASTUDY")
# checkListFile<-file.path(system.file("data",package="QUALIFIER"),"qaCheckList.csv.gz")
# qaTask.list<-read.qaTask(db,checkListFile)
# 
# #using formula to summing up the percentage of boundary events of each channel
# #using the cutoff function to detect the FCS files that has the higher percentage of boundary events
# #than the upper threshold provided by uBound
# #Note that the percentages of all channels for each fcs file ("name" here indicates the fcs file name)
# #are summed up through the formula
# qaCheck(qaTask.list[["BoundaryEvents"]]
# 		,sum(proportion) ~ RecdDt | name
# 		,outlierfunc=outlier.cutoff
# 		,uBound=0.0003
# 		)
# 
# plot(qaTask.list[["BoundaryEvents"]],proportion ~ RecdDt | channel)
# 
# 
# 
# #using Interquartile Range based outlier detection function
# #to find the outliers that has significant variance of MNC cell population among aliquots
# #here the formula is implicitly provided by qaTask object
# 
# qaCheck(qaTask.list[["MNC"]],outlierfunc=qoutlier,alpha=1.5)
# 
# plot(qaTask.list[["MNC"]])
# ## End(Not run)