expressionQCPipeline {beadarray} | R Documentation

## 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

### 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.

### Author(s)

Mark Dunning

### See Also

`poscontPlot` `imageplot` `outlierplot` `controlProbeDetection`

### Examples

    if(require(beadarrayExampleData)){

    ## Not run:

    data(exampleBLData)

    expressionQCPipeline(exampleBLData, horizontal=T)

    ## End(Not run)

    }

[Package beadarray version 2.36.1 Index]