seqWithinNorm: Sequential within-slide normalization function
In stepNorm: Stepwise normalization functions for cDNA microarrays

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

This function conducts cDNA microarray normalization in a sequential fashion. In a two-color cDNA array setting, within-slide normalization calibrates signals from the two channels to remove non-biological 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 log-ratio `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 calculating 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: global intensity or A-dependent robust nonlinear normalization using the `loess` function rlm: global intensity or A-dependent robust linear normalization using the `rlm` function median: global median location normalization none: no normalization for the A bias If not specified, `loess` normalization will be applied.
`PT`	A character string specifying the normalization model for the adjustment of print-tip or PT bias: median: within-print-tip-group median normalization rlm: within-print-tip-group robust linear normalization using the `rlm` function loess: within-print-tip-group robust nonlinear normalization using the `loess` function none: no normalization for the PT bias If not specified, `median` normalization within print-tip will be applied.
`PL`	A character string specifying the normalization model for the adjustment of well-plate or PL bias: median: within-well-plate median normalization rlm: within-well-plate robust linear normalization using the `rlm` function loess: within-well-plate robust nonlinear normalization using the `loess` function none: no normalization for the PL bias If not specified, `median` normalization within well-plate will be applied.
`Spatial2D`	A character string specifying the normalization model for the adjustment of spatial 2D bias: none: no normalization for the spatial 2D bias 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) If not specified, no normalization will be carried out in this step.
`criterion`	Character string specifying the criterion: AIC: the AIC criterion is used; see `calcAIC`. BIC: the BIC criterion is used; see `calcBIC`. 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 non-biological variations of a two-color cDNA microarray include the dependence of ratio measurements (M) on intensity (A), print-tip 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. 1325-1332.

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")