Bioinformatics, Volume I Data, Sequence Analysis and Evolution

92 Durinck
1.5. Data Filtering
1.6. Detection of
Differentially
Expressed Genes Genes
This type of normalization can be used under the assumption
that only a small subset of genes is differentially expressed
between the two samples. If this assumption does not hold, one
can rely on spiked-in RNA to calculate the lowess fit and use
that fit to adjust the data (11). Other frequently used methods
to normalize cDNA microarrays are qspline (12) and VSN (7)
normalization.
A between-slide normalization, which corrects for a difference
in scale, can be applied if the variance of the ratios differs a lot
between the different slides. A boxplot of the ratios grouped per
slide will reveal the necessity of such between-slide normalization.
ANOVA (13) is an alternative to the types of normalization
methods described in the preceding and aims to estimate the
size of different effects, including dye, gene, array, and sample.
Changes in gene expression across the samples are estimated by
the sample x gene interaction terms of the model (13).
Prior to detection of differentially expressed genes, a data-filtering
step can be applied. It is recommended to eliminate all genes
that are not expressed over all samples.
For cDNA microarrays, genes for which the foreground