Methodological study of affine transformations of gene expression data with
proposed normalization method
Henrik Bengtsson and Ola Hössjer
Centre for Mathematical Sciences
Lund Institute of Technology,
A detailed methodological study of affine models for gene expression data
is carried out. We focus on two-channel comparative studies although the
findings are not restricted to such. We find that the affine model is capable
of explaining many of the commonly observed discrepancies and systematic
effects in gene expression levels and the observed log-ratios. Focus is also
on data obtained by the two-color spotted microarray technology, but most
of the discussion applies equally well to other gene expression techniques
such as single-channel hybridization methods and quantitative real-time PCR.
The affine model can also explain non-linear systematic effects commonly
observed when log-ratios obtained by different gene expression technologies
are compared. A high-quality cDNA microarray data set is used to demonstrate
the power of the affine model. In the light of the affine model, the strengths
and the weaknesses of the most commonly used normalization methods are discussed.
Based on the affine model we propose a novel method to normalize one or multiple
arrays simultaneously where each array has been hybridized with one, two
or more samples. A package with all necessary method to read and normalize
the data have been written in the R language and is made available for free.
microarrays; affine transformation; bias; logarithm; non-linear systematic
effects; robust normalization; background correction