seqWithinNorm {stepNorm}  R Documentation 
This function conducts cDNA microarray normalization in a seqential fashion. In a twocolor cDNA array setting, withinslide normalization calibrates signals from the two channels to remove nonbiological variation introduced by various processing steps.
seqWithinNorm(marraySet, y = "maM", subset = TRUE, loss.fun = square, A = c("loess", "rlm", "median", "none"), PT = c("median", "rlm", "loess", "none"), PL = c("median", "rlm", "loess", "none"), Spatial2D = c("none", "aov2D", "rlm2D", "loess2D", "spatialMedian"), criterion = c("BIC", "AIC"))
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
logratio maM . 
subset 
A "logical" or "numeric" vector indicating the subset of points used to compute the normalization values. 
loss.fun 
The loss function used in calucating deviance, the
default uses squared sum of residuals; for absolute sum of
residuals, use abs 
A 
A character string specifying the normalization model
for the adjustment of intensity or A bias:
loess normalization will be applied.

PT 
A character string specifying the normalization model
for the adjustment of printtip or PT bias:
median normalization within printtip will be applied.

PL 
A character string specifying the normalization model
for the adjustment of wellplate or PL bias:
median normalization within wellplate will be applied.

Spatial2D 
A character string specifying the normalization model
for the adjustment of spatial 2D bias:

criterion 
Character string specifying the criterion:
If no specification, BIC is used. Note that here we don't use
the criterion to choose normalization model in each step. Criterion
is calculated solely for informaion purpose. 
Typical systematic nonbiological variations of a twocolor cDNA
microarray include the dependence of ratio measurements (M) on
intensity (A), printtip IDs (PT), plate IDs (PL) and spatial
heterogeneity of the slide (Spatial 2D). The sequential normalization
procedure in seqWithinNorm
normalizes a slide in a sequential
fashion: A > PT > PL > Spatial2D. In each step
one kind of variation is targeted for correction, and the user chooses
the normalization method as desired. We calculate the AIC/BIC
criterion along the normalization steps, but they are not used for
selection of models.
An object of class "list":
normdata 
an object of class marrayNorm , containing
the normalized intensity data. 
res 
a list of the sequential normalization result for each slide within the marray dataset. Each list component is also a list containing the name of the biases, deviance, equivalent number of parameters, AIC/BIC value for a certain slide. 
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. 13251332.
stepWithinNorm
, withinNorm
,
fitWithin
, fit2DWithin
,
calcAIC
, calcBIC
.
# Examples use swirl dataset, for description type ? swirl data(swirl) # Apply sequential normalization for the first slide # default: loess(A)>median(PT)>median(PL)> none (Spatial2D) ## Not run: res.swirl1 < seqWithinNorm(swirl[,1]) # normalized data norm.swirl < res.swirl1[[1]] # sequential normalization information step.swirl < res.swirl1[[2]] ## End(Not run) # median(A)>median(PT)>median(PL)>none(Spatial2D) res.swirl < seqWithinNorm(swirl[,1], A="median",PT="median",PL="median",Spatial2D="none")