fit2DWithin {stepNorm} | R Documentation |
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.
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: |
... |
Misc arguements 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 repsonses. |
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.
## 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)