qsplineNormalise {beadarray}R Documentation

Qsplinw Normalisation for BeadLevelList objects

Description

Normalizes expression intensities by fitting a smoothing curves to quantiles

Function is based on the 'normalize.qspline' function in 'affy'

Usage

qsplineNormalise(BLData)

Arguments

BLData can be either an BeadLevelList or BeadSummaryList object

Details

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.

Value

Produces an BeadLevelList or BeadSummaryList (depending on input type) containing normalised intensity values.

Author(s)

Laurent and C Workman

References

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

Examples


data(BLData)

#BLData.q = qsplineNormalise(BLData)

#All distributions are fixed to be the same

#boxplot(log2(BLData.q$R)~col(BLData.q$R))


[Package beadarray version 1.0.0 Index]