stepNorm (version 1.44.0)

fit2DWithin: Bivariate location normalization function for cDNA microarray data

Description

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.

Usage

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

Arguments

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

Value

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.

Details

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).

References

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.

See Also

fitWithin

Examples

## 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)