normalizeWithinArrays {limma} | R Documentation

## Normalize Within Arrays

### Description

Normalize the expression log-ratios for one or more two-colour spotted microarray experiments so that the log-ratios average to zero within each array or sub-array.

### Usage

    normalizeWithinArrays(object, layout, method="printtiploess", weights=object$weights, span=0.3, iterations=4, controlspots=NULL, df=5, robust="M")
    MA.RG(object, log.transform=TRUE)
    RG.MA(object)

### Arguments

`object` | object of class `list`, `RGList` or `MAList` containing two-color microarray data
`layout` | list specifying the dimensions of the spot matrix and the grid matrix
`method` | character string specifying the normalization method. Choices are `"none"`, `"median"`, `"loess"`, `"printtiploess"`, `"composite"` and `"robustspline"`. A partial string sufficient to uniquely identify the choice is permitted.
`weights` | numeric matrix or vector of the same size and shape as the components of `object`. Will use by default weights found in `object` if they exist.
`span` | numeric scalar giving the smoothing parameter for the `loess` fit
`iterations` | number of iterations used in loess fitting. More iterations give a more robust fit.
`controlspots` | numeric or logical vector specifying the subset of spots which are non-differentially expressed control spots, for use with `method="composite"`
`df` | degrees of freedom for spline if `method="robustspline"`
`robust` | robust regression method if `method="robustspline"`. Choices are `"M"` or `"MM"`.
`log.transform` | logical indicating whether intensities should be log2 transformed. If `FALSE` then intensities are assumed to be already logged.

### Details

Normalization is intended to remove from the expression measures any systematic trends which arise from the microarray technology rather than from differences between the probes or between the target RNA samples hybridized to the arrays.

This function normalizes M-values (log-ratios) for dye-bias within each array. Apart from `method="none"` and `method="median"`, all the normalization methods make use of the relationship between dye-bias and intensity. The loess normalization methods were proposed by Yang et al (2001, 2002). Smyth and Speed (2003) give a detailed statement of the methods.

More information on the loess control parameters `span` and `iterations` can be found under `loessFit`. The default values given here are equivalent to those for the older function `stat.ma` in the SMA package.

The `"robustspline"` method calls `normalizeRobustSpline`.

`MA.RG` converts an unlogged `RGList` object into an `MAList` object. `MA.RG(object)` is equivalent to `normalizeWithinArrays(object,method="none")`.

`RG.MA(object)` converts back from an `MAList` object to a `RGList` object with intensities on the log2 scale.

### Value

An object of class `MAList`.

### Author(s)

Gordon Smyth

### References

Yang, Y. H., Dudoit, S., Luu, P., and Speed, T. P. (2001). Normalization for cDNA microarray data. In Microarrays: Optical Technologies and Informatics, M. L. Bittner, Y. Chen, A. N. Dorsel, and E. R. Dougherty (eds), Proceedings of SPIE, Vol. 4266, pp. 141-152.

Yang, Y. H., Dudoit, S., Luu, P., Lin, D. M., Peng, V., Ngai, J., and Speed, T. P. (2002). Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Research 30(4):e15.

Smyth, G. K., and Speed, T. P. (2003). Normalization of cDNA microarray data. In: METHODS: Selecting Candidate Genes from DNA Array Screens: Application to Neuroscience, D. Carter (ed.). To appear.

### See Also

An overview of LIMMA functions for normalization is given in 4.Normalization.

See also `normalizeBetweenArrays` and `maNorm` in the marrayNorm package.

### Examples

    #  See normalizeBetweenArrays

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