withinNorm: Within-slide normalization function for cDNA spotted...
In stepNorm: Stepwise normalization functions for cDNA microarrays

Description Usage Arguments Details Value Author(s) References See Also Examples

This function is a wrapper function around fitWtihin and fit2DWithin. It allows the user to choose from a set of thirteen basic location normalization procedures. The function operates on an object of class marrayRaw or marrayNorm and returns an object of class marrayNorm.

withinNorm(marraySet,  y = "maM", subset = TRUE, norm = c("none", 
    "median", "rlm", "loess", "medianPrintTip", "rlmPrintTip", 
    "loessPrintTip", "medianPlate", "rlmPlate", "loessPlate", 
    "aov2D", "rlm2D", "loess2D", "spatialMedian"), ...)

`marraySet`	Object of class `marrayRaw` or class `marrayNorm`, containing intensity data for the batch of arrays to be normalized.
`y`	Name of accessor method for spot statistics, usually the log-ratio `maM`.
`subset`	A "logical" or "numeric" vector indicating the subset of points used to compute the normalization values.
`norm`	A character string specifying the normalization procedures: none: no normalization median: global median location normalization rlm: global intensity or A-dependent robust linear normalization using the `rlm` function loess: global intensity or A-dependent robust nonlinear normalization using the `loess` function medianPrintTip: within-print-tip-group median normalization rlmPrintTip: within-print-tip-group intensity or A-dependent robust linear normalization using the `rlm` function loessPrintTip: within-print-tip-group intensity or A-dependent robust nonlinear normalization using the `loess` function medianPlate: within-well-plate-group median normalization rlmPlate: within-well-plate-group intensity or A-dependent robust linear normalization using the `rlm` function loessPlate: within-well-plate-group intensity or A-dependent robust nonlinear normalization using the `loess` function aov2D: spatial bivariate location normalization using ANOVA rlm2D: spatial bivariate location normalization using the `rlm` function loess2D: spatial bivariate location normalization using the `loess` function spatialMedian: spatial location normalization using a spatial median approach (see Wilson et al. (2003) in reference)
`...`	Misc arguments for the specified `norm` function

The function withinNorm dispatches to the function fitWithin or fit2DWithin with specified arguments according to the choice of norm. For instance, when norm="loess" for global intensity dependent robust nonlinear normalization, withinNorm calls fitWithin(fun="loess") with the default span parameter set at 0.4. If a different span is preferred, it should be input by span=0.2 through the argument ... in the withinNorm function (see example below). For more details see fitWithin, fit2DWithin and individual fitting functions such as loessfit.

An object of class marrayNorm, containing the normalized intensity data.

Yuanyuan Xiao, yxiao@itsa.ucsf.edu,
Jean Yee Hwa Yang, jean@biostat.ucsf.edu

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.

D. L. Wilson, M. J. Buckley, C. A. Helliwell and I. W. Wilson (2003). New normalization methods for cDNA microarray data. Bioinformatics, Vol. 19, pp. 1325-1332.

seqWithinNorm, stepWithinNorm, fitWithin, fit2DWithin, loessfit, rlmfit.

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

# Apply loess normalization for the first slide, span=0.4
## Not run: 
res.swirl1 <- withinNorm(swirl[,1], norm="loess")
## End(Not run)

# Apply loess normalization for the first slide, span=0.2
## Not run: 
res.swirl1 <- withinNorm(swirl[,1], norm="loess", span=0.2)
## End(Not run)