BGandNorm {Agi4x44PreProcess} | R Documentation

## Background Correction and Normalization Between Arrays

### Description

For Background correction it uses the 'backgroundCorrect' function of 'limma' package ('half','normexp'). For Normalization between arrays it uses 'limma' function 'normalizeBetweenArrays' ('quantile','vsn').

### Usage

            BGandNorm(RGlist, BGmethod, NORMmethod, foreground,
            background, offset, makePLOTpre, makePLOTpost)

### Arguments

`RGlist` | an 'RGList' object
`BGmethod` | Method for the BG corection. Possible values are: 'none','half','normexp'. See ?backgroundCorrect in limma package for details
`NORMmethod` | Method for Norm between arrays. Possible values can be: 'none','quantile',vsn'. See ?normalizeBetweenArrays in limma package
`foreground` | Foreground Signal to be used for the analysis. Possible values are 'MeanSignal','ProcessedSignal'
`background` | Background Signal to be used for the BG correction. The values can be: 'BGMedianSignal','BGUsed'
`offset` | numeric value to add to the intensities before log transforming. The offset shrunks the log ratios towards zero at the lower intensities. See limma user guide for details
`makePLOTpre` | density Plots, box plots, MVA plots and RLE plots with the raw signal
`makePLOTpost` | density Plots, box plots, MVA plots and RLE plots with the normalized signal

### Details

In order to make direct comparisons of data coming from different chips it is important to remove sources of variation of non biological nature that may exists between arrays. Systematic non-biological differences between chips become relevant in several obvious ways especially during labeling and hybridization, and bias the relative measures on any two chips when we want to quantify differences due to different treatment between two samples. Normalization is the attempt to compensate for systematic technical differences between chips, to see more clearly the systematic biological differences between samples. First data are background corrected. We produce a Background Subtracted Signal. The Background Signal Used depends on the AFE settings for the type of background method calculation and the settings for spatial detrend. Usually, the Background Signal Used is the sum of the Local Background Signal + the Spatial Detrending Surface Value computed by the AFE software. For the Background correction we use the 'backgroundCorrect' function of 'limma' package with options <'half','normexp'> This function is designed to produce positive corrected intensities. First, any intensity value lower than 0.5 is reset to be equal to 0.5. Besides, and offset value (normally 50) is used. This offset value adds a constant to the intensity values before log-transforming, so that the log ratios are shrunk towards zero at the lower intensities. After background correction, data are normalized between arrays using 'limma' function 'normalizeBetweenArrays' with options <'quantile','vsn'>

For foreground signal,the user can choose between the 'MeanSignal' and the 'ProcessedSignal' and between the 'BGMedianSignal' and the 'BGUsed' for background correction. The user may want to have a look at different graphics (density plots, etc ...) in order to decide what signal is more suitable to use. For details about signal processing see AFE User Guide. 'MeanSignal' is the spot Raw mean signal. 'ProcessedSignal' is the signal processed by the Agilent Feature Extraction image analysis software (AFE). It contains the Multiplicatively Detrend Bacground Substracted Signal if the detrending is selected and it helps. If the detrending does not help, the 'ProcessedSignal' will be the Bacground Subtracted Signal. 'BGMedianSignal' is the Median local background signal. 'BGUsed' depends on the AFE software settings for the type of background method calculation and the setting for the spatial detrend. Usually, the Background Signal Used is the sum of the local bacground + the spatial detrending surface value computed by the AFE software. To view the values used to calculate this variable using different bakground signals and settings of spatial detrend and global background adjust, see Table 33 on page 213 of the AFE User Guide. Limma function 'backgroundCorrect' is used for the BG correction. This function is designed to produce positive intensities. Any intensity value lower less than 0.5 is reset to be equal to 0.5. Additionally, a constant of 50 (normally) is used as a offset that it is added to the intensity values before the log transformation. The propouse of this calculation is to shrunk the log ratios to zero at the lower intensities and thus to reduce the variability of log-ratios for low intensity spots. The optimal choice for the offset is the one which makes the variability of the log-ratios as constant as possible accross the range of intensity values (Smyth, G. in BioC mailing List). If the 'half' method is chosen for Background Correction, the method will substract the chosen BACKGROUND signal to the chosen FOREGROUND signal, to produce positive corrected intensities according to the 'half' method. If the 'normexp' method is selected, then a convolution of normal and exponential distributions is fitted to foreground intensities using background intensities as a covariate, and the expected signal given the observed foreground becomes the corrected intensity. See 'limma' user guide for details.

### Value

a 'RGList' object, containing in 'RGList$G' the log-2 normalized intensities

### Author(s)

Pedro Lopez-Romero

### References

Bolstad, B. M. (2001), Probe level quantile normalization of high density oligonucleotide array data. Unpublished Manuscript: http://bmbolstad.com/stuff/qnorm.pdf

Bolstad, B. M., Irizarry R. A., Astrand, M., and Speed, T. P. (2003), A comparison of normalization methods for high density oligonucleotide array data based on bias and variance. Bioinformatics 19, 185-193.

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

### See Also

Agilent Feature Extraction Reference Guide http://www.Agilent.com See also 'backgroundCorrect' and 'normalizeBetweenArrays' in the limma package and 'vsn' in the vsn package.

### Examples

    ## Not run:
             data(dd)
             ddNORM=BGandNorm(dd,BGmethod='half',NORMmethod='quantile',
                            foreground='MeanSignal',background='BGMedianSignal',
                            offset=50,makePLOTpre=TRUE,makePLOTpost=TRUE)
    ## End(Not run)

[Package Agi4x44PreProcess version 1.2.0 Index]