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Normalization Overview



In GeneLinkerô the term normalization is used to describe scaling, translation, or any other numerical transformation of the data besides filtering. These transformations fall into three broad categories:

Any number of these normalizations can be applied to dataset in succession. For instance, it may be appropriate to scale samples to correct for non-biological variations, and then place genes on a common scale before clustering, association mining or supervised learning takes place.


Techniques for Correcting Non-Biological Variation Between Samples


Techniques for Adjusting Two-Color Data

The Lowess normalization automatically merges the treatment and control channels into adjusted ratios. Any other operation on a two-color table automatically uses the unadjusted ratios.

Note: Lowess is the only normalization option for incomplete two-color datasets.


Techniques for Placing Different Genes on a Similar Scale


Related Topics:

Filtering Overview

Clustering Overview