BASHDiffuse {beadarray} | R Documentation

## BASH - Diffuse Defect Analysis

### Description

Creates a list of probes marked as being in diffuse defects.

### Usage

    BASHDiffuse(inten, probeIDs, wts=NULL, neighbours = NULL, E = NULL, n = 3,
        compact = NULL, sig = 0.0001, invasions = 10, cutoff = 8, cinvasions = 10,
        twotail = FALSE, einvasions = 20, outlierFun = illuminaOutlierMethod, ...)

### Arguments

`inten` | the (transformed) intensities associated with beads on an array.
`probeIDs` | the probe identities associated with those beads (i.e. indicating which beads are of which sort)
`wts` | weights indicating any beads to be masked in calculations.
`neighbours` | A Neighbours matrix such as that generated by `generateNeighbours`. Compulsory - the function will no longer generate this for you.
`E` | Numerical vector - The error image to use. Optional - if left blank, it will be computed, using `generateE` using `bgfilter = "median"`.
`n` | Specify a cut-off for outliers (e.g. as n median absolute deviations (MADs) from the median). The default value is 3
`compact` | Vector - Optional. BeadIDs of beads in compact defects to remove from the analysis.
`sig` | Numerical - Significance level of binomial test.
`invasions` | Integer - Number of invasions to use to find the kernel (see below).
`cutoff` | Integer - Size a cluster must be to be labelled a diffuse defect.
`cinvasions` | Integer - Number of invasions used when closing the image.
`twotail` | Logical - If TRUE, then we analyse positive and negative outliers separately, and then combine the diffuse defect images at the end.
`einvasions` | Integer - Number of invasions used when forming the error image `E`.
`outlierFun` | the choice of outlier calling function to use.
`...` | Additional arguments to be passed to `outmeth`.

### Details

`BASHDiffuse` finds "diffuse defects" on an array. A diffuse defect is defined as a region containing an unusually large number of (not necessarily connected) outliers.

Firstly, we consider the error image `E`, and find outlier beads on this image. Outliers for a particular bead type are determined using a 3 MAD cut-off from the median.

We now consider an area around each bead (known as the "kernel"). The kernel is found by an invasion process using the neighbours matrix - we choose the beads which can be reached from the central bead in `cinvasions` steps.

We count how many beads are in the kernel, and how many of these are marked as outliers. Using a binomial test, we work out if there are significantly more outliers in the kernel than would be expected if the outliers were equally distributed over the entire array. If so, then the central bead is marked as a diffuse defect.

Lastly, we run a clustering algorithm and a closing algorithm similar to those in `BASHCompact`.

### Value

A vector consisting of the BeadIDs of beads considered diffuse defects.

### Author(s)

Jonathan Cairns

### References

J. M. Cairns, M. J. Dunning, M. E. Ritchie, R. Russell, and A. G. Lynch (2008). BASH: a tool for managing BeadArray spatial artefacts. Bioinformatics 15; 24(24)

### See Also

`BASH`, `generateNeighbours`

### Examples

    ## Not run:

    if(require(beadarrayExampleData)){

    data(exampleBLData)
    o <- BASHDiffuse(getBeadData(exampleBLData,array=1,what="Grn"), getBeadData(exampleBLData,array=1,what="ProbeIDs"))
    o <- BASHDiffuse(getBeadData(exampleBLData,array=1,what="Grn"), getBeadData(exampleBLData,array=1,what="ProbeIDs"), sig = 0.00001) ##stricter significance value, perhaps more useful on a BeadChip.
    o <- BASHDiffuse(getBeadData(exampleBLData,array=1,what="Grn"), getBeadData(exampleBLData,array=1,what="ProbeIDs"), cutoff = 12) ##only larger defects will be found with this setting

    }

    ## End(Not run)

[Package beadarray version 2.34.0 Index]