backgroundCorrect {limma} | R Documentation |

Background correct microarray expression intensities.

backgroundCorrect(RG, method="subtract", offset=0, printer=RG$printer, verbose=TRUE)

`RG` |
an `RGList` object or a unclassed list containing the same components as an `RGList` |

`method` |
character string specifying correction method. Possible values are `"none"` , `"subtract"` , `"half"` , `"minimum"` , `"movingmin"` , `"edwards"` , `"normexp"` or `"rma"` . |

`offset` |
numeric value to add to intensities |

`printer` |
a list containing printer layout information, see `PrintLayout-class` |

`verbose` |
logical, should progress be reported to standard output |

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.
If `method="movingmin"`

then the background estimates are replaced with the minimums of the backgrounds of the spot and its eight neighbors, i.e., the background is replaced by a moving minimum of 3x3 grids of spots.

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"`

a log-linear interpolation method is used to adjust lower intensities as in Edwards (2003).
If `method="normexp"`

a convolution of normal and exponential distributions is fitted to the foreground intensities using the background intensities as a covariate, and the expected signal given the observed foreground becomes the corrected intensity.
This results in a smooth monotonic transformation of the background subtracted intensities such that all the corrected intensities are positive.
See Smyth (2005) and `normexp.fit`

for more details.

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.

Background correction (background subtraction) is also performed by the `normalizeWithinArrays`

method for `RGList`

objects, so it is not necessary to call `backgroundCorrect`

directly unless one wants to use a method other than simple subtraction.
Calling `backgroundCorrect`

before `normalizeWithinArrays`

will over-ride the default background correction.

An `RGList`

object in which components `R`

and `G`

are background corrected
and components `Rb`

and `Gb`

are removed.

Gordon Smyth

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.

Yang, Y. H., Buckley, M. J., Dudoit, S., and Speed, T. P. (2002). Comparison of methods for image analysis on cDNA microarray data. *Journal of Computational and Graphical Statistics* 11, 108-136.

Yang, Y. H., Buckley, M. J., and Speed, T. P. (2001). Analysis of microarray images. *Briefings in Bioinformatics* 2, 341-349.

An overview of background correction functions is given in `04.Background`

.

RG <- new("RGList", list(R=c(1,2,3,4),G=c(1,2,3,4),Rb=c(2,2,2,2),Gb=c(2,2,2,2))) backgroundCorrect(RG) backgroundCorrect(RG, method="half") backgroundCorrect(RG, method="minimum") backgroundCorrect(RG, offset=5)

[Package *limma* version 2.4.7 Index]