Main areas: Processing

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##Microarray Normalization DNA microarray technologies allow for the simultaneous measurement of thousands of genomic features, such as gene expression, copy number variation or SNP variant. The accuracy and reproducibility of microarray measurements has been extensively validated in the past years. Despite that, in the measurements of any microarray set, there are always technological artifacts that may hide the true biological signal. Such possible distortion of the microarray measurements may produce signal effects within each arrays but also across different arrays in the set.

Causes of non biological variation in microarray measurements include, of course, differences in the sample preparation and the hybridization process, but also, [dye bias](Dye bias), [cross-hybridization](Cross hybridization) and scanner differences.

The goal of normalization is to adjust for the effects that are due to variations in the technology rather than the biology.

• [More information](Preprocessing for microarrays)

RNA-Seq Normalization

Once the counts data matrix has been created, and before addressing any further analysis, a normalization process may be necessary. For normalization of RNA-Seq data destined to differential expression, please see the section of Differential Expression for RNA-Seq in Main areas: Expression.

• [More information](Preprocessing for RNA-Seq)

##Data Matrix Before any meaningful analysis can be done using your genomic data, they will need to go under a thorough cleaning process. Data normalization is the paradigm of such data reshaping processes, but not the only one. Mathematical transformations of your data like taking logarithms or missing data imputation are among them. In general the purpose of this step is to reshape your data into a distribution which will be suitable in further steps of the analysis. Babelomics preprocessing tools allow you doing such data transformations.

• [More information](Preprocessing for data matrix)

References