Microarrays are a widely used research tool in gene expression analysis. A large variety
of preprocessing methods for raw intensity measures is available to establish gene expression values. Normalization is the key stage in preprocessing methods, since
it removes systematic variations in microarray data. Then, the subsequent analyses
may be highly dependent on normalization strategy employed. Our research focuses
on detecting rhythmic signals in measured circadian gene expressions. We have observed
that rhythmicity detection depends not only upon the rhythmicity detection
algorithm but also upon the normalization strategy employed. We analyze the effects
of well-known normalization strategies in literature within three different rhythmicity
detection algorithms; JTK, RAIN and our recently proposal ORI, a novel statistical
methodology based on Order Restricted Inference. The results obtained are compared
using artificial microarray data and publicly available circadian data bases.
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