normalize.qspline: Normalize arrays
In affy: Methods for Affymetrix Oligonucleotide Arrays

Description Usage Arguments Details Value Author(s) References

normalizes arrays in an AffyBatch each other or to a set of target intensities

normalize.AffyBatch.qspline(abatch,type=c("together", "pmonly", "mmonly",
                 "separate"), ...)

normalize.qspline(x, target = NULL, samples = NULL, 
                  fit.iters = 5, min.offset = 5, 
                  spline.method = "natural", smooth = TRUE,
                  spar = 0, p.min = 0, p.max = 1.0, 
                  incl.ends = TRUE, converge = FALSE, 
                  verbose = TRUE, na.rm = FALSE)

`x`	a `data.matrix` of intensities
`abatch`	an `AffyBatch`
`target`	numerical vector of intensity values to normalize to. (could be the name for one of the celfiles in 'abatch').
`samples`	numerical, the number of quantiles to be used for spline. if (0,1], then it is a sampling rate.
`fit.iters`	number of spline interpolations to average.
`min.offset`	minimum span between quantiles (rank difference) for the different fit iterations.
`spline.method`	specifies the type of spline to be used. Possible values are ‘"fmm"’, ‘"natural"’, and ‘"periodic"’.
`smooth`	logical, if ‘TRUE’, smoothing splines are used on the quantiles.
`spar`	smoothing parameter for ‘splinefun’, typically in (0,1].
`p.min`	minimum percentile for the first quantile.
`p.max`	maximum percentile for the last quantile.
`incl.ends`	include the minimum and maximum values from the normalized and target arrays in the fit.
`converge`	(currently unimplemented)
`verbose`	logical, if ‘TRUE’ then normalization progress is reported.
`na.rm`	logical, if ‘TRUE’ then handle NA values (by ignoring them).
`type`	a string specifying how the normalization should be applied. See details for more.
`...`	optional parameters to be passed through.

This normalization method uses the quantiles from each array and the target to fit a system of cubic splines to normalize the data. The target should be the mean (geometric) or median of each probe but could also be the name of a particular chip in the abatch object.

Parameters setting can be of much importance when using this method. The parameter fit.iter is used as a starting point to find a more appropriate value. Unfortunately the algorithm used do not converge in some cases. If this happens, the fit.iter value is used and a warning is thrown. Use of different settings for the parameter samples was reported to give good results. More specifically, for about 200 data points use samples = 0.33, for about 2000 data points use samples = 0.05, for about 10000 data points use samples = 0.02 (thanks to Paul Boutros).

The type argument should be one of "separate","pmonly","mmonly","together" which indicates whether to normalize only one probe type (PM,MM) or both together or separately.

a normalized AffyBatch.

Laurent and Workman C.

Christopher Workman, Lars Juhl Jensen, Hanne Jarmer, Randy Berka, Laurent Gautier, Henrik Bjorn Nielsen, Hans-Henrik Saxild, Claus Nielsen, Soren Brunak, and Steen Knudsen. A new non-linear normal- ization method for reducing variability in dna microarray experiments. Genome Biology, accepted, 2002

affy documentation built on Nov. 8, 2020, 8:18 p.m.