beadarray (version 2.22.2)

expressionQCPipeline: Flexible bead-level QC pipeline

Description

Function to produce various QC plots and HTML summary pages for bead-level data.

Usage

expressionQCPipeline(BLData, transFun = logGreenChannelTransform, qcDir = "QC", plotType = ".jpeg", horizontal = TRUE, controlProfile = NULL, overWrite=FALSE,nSegments=9,outlierFun=illuminaOutlierMethod,tagsToDetect = list(housekeeping = "housekeeping", Biotin = "biotin", Hybridisation = "cy3_hyb"),zlim=c(5,7),positiveControlTags = c("housekeeping", "biotin"), hybridisationTags =  c("cy3_hyb"), negativeTag= "negative", boxplotFun = logGreenChannelTransform, imageplotFun = logGreenChannelTransform)

Arguments

BLData

a beadLevelData object

transFun

what transformation function to apply

qcDir

a directory to write output to

plotType

desired file extension for plots (jpeg or png)

horizontal

if TRUE imageplots and outlier plots are produced with longest edge on x axis

controlProfile

a data frame defining all control types. not required if annotation information is stored in the bead-level object

overWrite

if FALSE any plots that exist in the directory will not be recreated

nSegments

how many segments each section is divided into

outlierFun

a function to removed outliers

tagsToDetect

which control types to used in the detection metrics

zlim

the range of the imageplots

boxplotFun

what transformation function to be used in boxplots

imageplotFun

what transformation function to be used for imageplots

positiveControlTags

character strings defining which positive controls to plot

hybridisationTags

additional control types to be plotted

negativeTag

character string to identify which control type in the control profile corresponds to negative controls

...

other plot arguments

Details

This function is a convient way of automatically generating QC plots for each section within a beadLevelData object. The following plots are produced for each section. i) scatter plots of all bead observation of the positive controls. See poscontPlot. ii) Further scatter plots of other controls of interest using poscontPlot. iii) imageplot (imageplot) of section data after applying transformation function iv) plot of outlier locations using specified outlier function. A HTML page displaying all the plots is produced.

After plots have been produced for each section, makeQCTable is run to make a table of mean and standard deviations for the defined control types, followed by the results of calculateOutlierStats and controlProbeDetection for each section and written to a HTML page in the requested directory.

The function should be able to run automatically for expression data that has its annotation stored using setAnnotation or using readIllumina. Otherwise the controlProfile data frame can be used to define the control types on the array and their associated ArrayAddressIDs. Similarly, the function assumes single-channel data but a transformation function can be passed.

See Also

poscontPlot imageplot outlierplot controlProbeDetection

Examples

if(require(beadarrayExampleData)){

## Not run: 
# 
# data(exampleBLData)
# 
# expressionQCPipeline(exampleBLData, horizontal=T)
# 
# ## End(Not run)

}