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Normalization Overview

 

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