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This question comes because reading a paper about normalization of gene-expression data, is not clear if the method for normalize the data is just for RNA-Seq data or could be applied also for microarrays.

For RNA-Seq data, there are methods of normalization which adjust for GC-content effect or other gene-level effects. It make sense consider these effects in the normalization of microarray gene-expression data?

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    $\begingroup$ These methods correct for the biases during the sequencing experiment. Since microarray is a different technique, the biases it suffers from would be different. There are bias correction techniques for microarrays as well, but they are different from the ones used for RNAseq. They cannot be used interchangeably. $\endgroup$ – WYSIWYG May 5 '16 at 18:27
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Normalization of expression data is a big topic with new methods being published regularly. When approaching something like this you generally want look at people who have done similar things to what you've done, and then once you understand why they did what they did, you can ask what you need to do to answer your questions. Always keep your biological question in mind. For instance if you're measuring QTLs, you'll need to be a lot more careful than if you're just looking for genes effected by a knockout mutation.

In general you want to use quite different methods for RNAseq and Microarray data. The two data types follow completely different distributions (RNAseq gives you count data, microarray data gives you continous signals) and have different types of technical noise effecting them (GC content will effect both, but in a different way). Some methods can be used on both, but usually involve coercing the data into a different form (e.g. mapping counts to a normal distribution). The limma package for R can handle both, using different distributions, and is a good start. Newer, purportedly better methods exist for RNAseq, which I haven't personally used.

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Generally speaking for RNA-seq data, you don't want to correct for GC content or other gene level effects (e.g. length) because you compare expression values between conditions WITHIN a gene. For this reason, it is recommended to use raw counts and not normalized values such as FPKM. See Section 2.7 of the edgeR user manual.

This recent benchmark comparing RNA-seq quantification methods may be worth a look.

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