backgroundCorrect {beadarray} | R Documentation

## Background Correct a BeadLevelList object

### Description

Adapted from 'limma' backgroundCorrect function to correct the foreground intensities of a BLData object using the background values.

### Usage

    backgroundCorrect(object, method = "subtract", offset = 0, verbose = FALSE)

### Arguments

`object` | a BeadLevelList object
`method` | character string specifying correction method. Possible values are '"none"', '"subtract"', '"half"', '"minimum"', '"edwards"', '"normexp"', '"rma"')
`offset` | numeric value to add to intensities
`verbose` | logical. Used when method = '"normexp"'. If TRUE, the parameters estimated by the model are output.

### Details

If 'method="none"' then the corrected intensities are equal to the foreground intensities, i.e., the background intensities are treated as zero. If 'method="subtract"' then this function simply subtracts the background intensities from the foreground intensities which is the usual background correction method.

The remaining methods are all designed to produce positive corrected intensities. If 'method="half"' then any intensity which is less than 0.5 after background subtraction is reset to be equal to 0.5. If 'method="minimum"' then any intensity which is zero or negative after background subtraction is set equal to half the minimum of the positive corrected intensities for that array. If 'method="edwards"' the method of Edwards (2003) is used. If 'method="normexp"' or '"rma"', a normal-exponential convolution model is fitted to the intensities, using different estimation procedures. See Smyth (2005) for further details on normexp.

The 'offset' can be used to add a constant to the intensities before log-transforming, so that the log-ratios are shrunk towards zero at the lower intensities. This may eliminate or reverse the usual 'fanning' of log-ratios at low intensities associated with local background subtraction.

### Value

An 'BeadLevelList' object in which the 'R' and 'G' intensities for each array are background corrected

### Author(s)

Mark Dunning based on the limma function

### References

Edwards, D. E. (2003). Non-linear normalization and background correction in one-channel cDNA microarray studies, Bioinformatics, 19, 825-833.

Smyth, G. K. (2005). Limma: linear models for microarray data. In: Bioinformatics and Computational Biology Solutions using R and Bioconductor, R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W. Huber (eds.), Springer, New York, pages 397-420.

### Examples

    data(BLData)

    #default is to simply subtract Rb from R
    BLData.bc = backgroundCorrect(BLData)

    #Use 'minimum' method to stop negative values appearing
    BLData.min = backgroundCorrect(BLData, method="minimum")

[Package beadarray version 1.4.0 Index]