squeezedVarOutlierMethod {beadarray} | R Documentation

## Identifier outliers on an array section

### Description

An outlier calling method that shrinks the observed variance for a bead-type towards the predicted variance based on all bead-types on the array-section.

### Usage

    squeezedVarOutlierMethod(inten, probeList, wts=1, n = 3, predictNlim=14)

### Arguments

`inten` | a list of intensities
`probeList` | the IDs corresponding to each intensity value
`wts` | Weights associated with beads, indicating those recommended for removal by, for example, `BASH`
`n` | number of SDs cutoff used
`predictNlim` | how many beads of a bead-type must be present for that bead-type to contribute to prediction of variances?

### Details

This function is called within the `summarize` routine of beadarray to exclude outlying beads from an array-section prior to summary. The intensities are not assumed to be on any particular scale and can result from any user-defined transformation function, however a log-transformation is recommended.

Bead-types that have `predictNlim` numbers are used to locally regress bead-type precision against bead-type mean, as well as the squared residual error of bead-type precision against bead-type mean. These are then used as prior values for the distribution of precision to feed into a standard Bayesian calcuation to obtain an estimate of the posterior variance.

Beads with weight zero do not contribute to the outlier calling.

### Value

the positions in the original vector that were determined to be outliers

### Author(s)

Andy Lynch

### See Also

`illuminaOutlierMethod`

### Examples

    if(require(beadarrayExampleData)){

    data(exampleBLData)

    oList = squeezedVarOutlierMethod(logGreenChannelTransform(exampleBLData, 1), getBeadData(exampleBLData, array=1, what="ProbeID"))

    }

[Package beadarray version 2.32.0 Index]