QUALIFIER (version 1.16.1)

queryStats: Perform the quality assessment for the qaTask object

Description

queryStats method queries stats entries from db by qaTask object and formula

codeclearCheck function removes the outlier results detected by the previous qaCheck call on a particular gating set.

Perform the quality assessment for a particular QA Task based on the information provided by qaTask object.

Usage

queryStats(x, ...)
clearCheck(obj, gsid)
"qaCheck"(obj, ...)

Arguments

x

a qaTask object

...

formula: a formula describing the variables to be used for QA. When it is omitted or NULL, the formula stored in qaTask is used. It is generally of the form y ~ x | g1 * g2 * ... , y is the statistics to be checked in this QA, It must be one of the four types:

"MFI": Median Fluorescence Intensity of the cell population specified by qaTask,

"proportion": the percentage of the cell population specified by qaTask in the parent population,

"count": the number of events of the cell population specified by qaTask,

"spike": the variance of intensity over time of each channel ,which indicating the stability of the fluorescence intensity.

x is normally used to specify the variable plotted on x-axis in plot method. when plotType of the qaTask is "bwplot", it is also taken as the conditioning variable that divides the samples into subgroups within which the outlierfunc is applied.

g1,g2,.... are the conditioning variables, which are used to divide the samples into subgroups and perform QA check whitin each individual groups.They may also be omitted,in which case the outliers detection is peformed in the entire sample set.

subset: a logical expression used as a filter.It follows the same syntax as the "subset" expression in subset.

Usage:

subset=channel%in%c('FITC-A')

subset=Tube=='CD8/CD25/CD4/CD3/CD62L'&channel%in%c('FITC-A')

outlierfunc:a function to be used for outlier detection. see outlierFunctions for more details.

gOutlierfunc:a function to be used for group outlier detection. see outlierFunctions for more details. rFunc:a function for fitting regression model within each individual subgroup.

isTerminal:a logical scalar indicating whether the pop is at terminal node of the gating path.

fixed:a logical scalar indicating whether the pop name is matched as it is .By default it is FALSE,which matches the gating path as the regular expression

obj

a qaTask object

gsid

an integer that uniquely identifies a gating set object. if missing, the latest added gating set is selected.

Details

qaCheck method parses the formula stored in qaTask or explicitly provided by the argument and select the appropriate gated population,extract the statistics that is pre-calculated by getQAStats and perform the outlier detection within a certain sample groups specified by the conditioning variables or x term in formula. Then the outliers detection results are save in database and ready for query or plotting.

See Also

plot,getQAStats

Examples

## Not run: 
# 
# data("ITNQASTUDY")
# checkListFile<-file.path(system.file("data",package="QUALIFIER"),"qaCheckList.csv.gz")
# qaTask.list<-read.qaTask(db,checkListFile)
# 
# 
# #using t-distribution based outlier detection function
# #applied the linear regression on each group to detect the significant MFI change over time
# qaCheck(qaTask.list[["MFIOverTime"]]
# 		,outlierfunc=outlier.t
# 		,rFunc=rlm
# 		,alpha=0.05
# )
# plot(qaTask.list[["MFIOverTime"]],y=MFI~RecdDt|stain
# 		,subset="channel%in%c('FITC-A')"
# 		,rFunc=rlm
# )
# 
# 
# #detect the outliers that has lower percentage of RBC Lysis than the threshold provided by lBound
# qaCheck(qaTask.list[["RBCLysis"]]
# 		,formula=proportion ~ RecdDt | Tube
# 		,outlierfunc=outlier.cutoff
# 		,lBound=0.8
# 		)
# 
# plot(qaTask.list[["RBCLysis"]])
# ## End(Not run)