maNormScale {marray} | R Documentation

## Simple scale normalization function

### Description

This function is a simple wrapper function around the main normalization function `maNormMain`. It allows the user to choose from a set of two basic scale normalization procedures. The function operates on an object of class `"marrayRaw"` (or possibly `"marrayNorm"`, if normalization is performed in several steps) and returns an object of class `"marrayNorm"`. This function can be used to conormalize a batch of arrays (`norm="globalMAD"` option).

### Usage

    maNormScale(mbatch, norm=c("globalMAD", "printTipMAD"), subset=TRUE, geo=TRUE,  Mscale=TRUE, echo=FALSE)

### Arguments

`mbatch` | An object of class `"marrayRaw"`, containing intensity data for the batch of arrays to be normalized. An object of class `marrayNorm` may also be passed if normalization is performed in several steps.
`norm` | A character string specifying the normalization procedures: globalMAD for global scale normalization using the median absolute deviation (MAD), this allows between slide scale normalization printTipMAD for within-print-tip-group scale normalization using the median absolute deviation (MAD). This argument can be specified using the first letter of each method.
`subset` | A "logical" or "numeric" vector indicating the subset of points used to compute the normalization values.
`geo` | If `TRUE`, the MAD of each group is divided by the geometric mean of the MADs across groups (cf. Yang et al. (2002)). This allows observations to retain their original units.
`Mscale` | If `TRUE`, the scale normalization values are stored in the slot `maMscale` of the object of class `"marrayNorm"` returned by the function, if `FALSE`, these values are not retained.
`echo` | If `TRUE`, the index of the array currently being normalized is printed.

### Details

See `maNormMain` for details and more general procedures.

### Value

`mnorm` | An object of class `"marrayNorm"`, containing the normalized intensity data.

### Author(s)

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine.

### References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, The Analysis of Gene Expression Data: Methods and Software, Springer, New York.

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

Y. H. Yang, S. Dudoit, P. Luu, D. M. Lin, V. Peng, J. Ngai, and T. P. Speed (2002). Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Research, Vol. 30, No. 4.

### See Also

`maNormMain`, `maNorm`.

### Examples

    # Examples use swirl dataset, for description type ? swirl
    data(swirl)

    # Global median normalization followed by global MAD normalization for
    # only arrays 2 and 3 in the batch swirl

    mnorm1<-maNorm(swirl[,2:3], norm="m")
    mnorm2<-maNormScale(mnorm1, norm="g")

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