Normalize arrays
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 |
abatch |
an |
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
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