There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
When running experiments that involve multiple high density oligonucleotide arrays,
it is important to remove sources of variation between arrays of non-biological origin.
Normalization is a process for reducing this variation. It is common to see non-linear
relations between arrays and the standard normalization provided by Affymetrix does
not perform well in these situations.
We present three methods of performing normalization at the probe intensity level.
These methods are called complete data methods because they make use of data from
all arrays in an experiment to form the normalizing relation. These algorithms are
compared to two methods that make use of a baseline array: a one number scaling based
algorithm and a method that uses a non-linear normalizing relation by comparing the
variability and bias of an expression measure. Two publicly available datasets are
used to carry out the comparisons. The simplest and quickest complete data method
is found to perform favorably.
Software implementing all three of the complete data normalization methods is available
as part of the R package Affy, which is a part of the Bioconductor project http://www.bioconductor.org.
Additional figures may be found at http://www.stat.berkeley.edu/~bolstad/normalize/index.html