qaCheck-methods: Perform the quality assessment for the qaTask object
In QUALIFIER: Quality Control of Gated Flow Cytometry Experiments

Description Usage Arguments Details Author(s) See Also Examples

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.

queryStats(x, ...)

clearCheck(obj, gsid)

## S4 method for signature 'qaTask'
qaCheck(obj, ...)

`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.

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.

Mike Jiang,Greg Finak

Maintainer: Mike Jiang <wjiang2@fhcrc.org>

plot,getQAStats

## 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)