Microarrays are high-throughput methods that measure the expression levels of thousands of genes simultaneously. Each sample receives different conditions. A small difference in RNA quantities or/and experimental errors may cause the intensity level to vary from one replicate to the other. This can be irrespective of the biological expression of genes. Handling this inherent problem requires the normalization of data. This minimizes the technical effects, rendering the data comparable. This recipe will explore a few of the many normalization methods developed for data normalization in R.
To start with the normalization recipe, we need to define our data as an
AffyBatch object. Here, we will use the ...