fit2DWithin: Bivariate location normalization function for cDNA microarray...
In stepNorm: Stepwise normalization functions for cDNA microarrays

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

This function performs 2D location normalization on cDNA micoroarray. It operates on class marrayRaw or class marrayNorm. It allows the user to choose from a set of four basic normalization procedures.

1 2	fit2DWithin(x1.fun = "maSpotRow", x2.fun = "maSpotCol", y.fun = "maM", subset=TRUE, fun = aov2Dfit, ...)

`x1.fun`	Name of accessor method for spot row coordinates, usually `maSpotRow`.
`x2.fun`	Name of accessor method for spot column coordinates, usually `maSpotCol`.
`y.fun`	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.
`fun`	Character string specifying the normalization procedures: rlm2Dfit for robust linear regression using the `rlm` function loess2Dfit for robust local regression using the `loess` function aov2Dfit for linear regression using the `lm` function spatialMedfit for spatial median normalization
`...`	Misc arguments for `fun`

The spot statistic named in y is regressed on spot row and column coordinates, using the function specified by the argument fun. Typically, rlm2Dfit and loess2Dfit, which treat row and column coordinates as numeric vectors, require a lot fewer parameters than aov2Dfit which specifies these two variables as categorical. spatialMedfit could yet fit the most complicated model, depending on size of the smoothing window specified; details see Wison et al (2003).

The function fit2DWithin returns a function (F) with bindings for x1.fun, x2.fun, y.fun, subset and fun. When the function F is evaluated with an object of class marrayNorm or marrayRaw, it carries out normalization and returns an object of class marrayFit that contains the normalization information as a list with the following components:

`varfun`	: A character vector of names of predictor variables.
`x`	: A numeric matrix of predictor variables.
`y`	: A numeric matrix of responses.
`residuals`	: A numeric matrix of normalized values (typically log ratios (M)).
`fitted`	: A numeric matrix of the fitted values.
`enp`	: The equivalent number of parameters; see `loess`.
`df.residual`	: The residual degrees of freedom.
`fun`	: A character string indicating the name of the function used for normalization.

Note that the residuals component stores the normalized ratios.

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.

fitWithin

## use the swirl data as example
data(swirl)

## 2D rlm normalization
rlm2D <- fit2DWithin(fun="rlm2Dfit")
swirl1.rlm <- rlm2D(swirl[,1])
norm.M <- swirl1.rlm$residuals ## matrix of normalized ratios

## 2D loess normalization, default span=0.2
loess2D <- fit2DWithin(fun="loess2Dfit")
swirl1.loess <- loess2D(swirl[,1])
## 2D loess normalization, span=0.4
## Not run: 
loess2D.1 <- fit2DWithin(fun="loess2Dfit", span=0.4)
swirl1.loess.1 <- loess2D.1(swirl[,1])
## End(Not run)

## 2D aov normalization
aov2D <- fit2DWithin(fun="aov2Dfit")
swirl1.aov <- aov2D(swirl[,1])

## 2D spatial median normalization, default window width=3
spatialMed2D <- fit2DWithin(fun="spatialMedfit")
swirl1.spatialMed <- spatialMed2D(swirl[,1])
## 2D loess normalization, window width=9
## Not run: 
spatialMed2D.1 <- fit2DWithin(fun="spatialMedfit", width=9)
swirl1.spatialMed.1 <- spatialMed2D.1(swirl[,1])
## End(Not run)