Hi,

I've got some 450K data and I've been looking at the various packages around....There's a lot of normalisation methods.

I realise normalisation as a method is quite subjective to the data, and it's probably best to "shop around", and see which method best fits my data. However, are there any methods that in principle should be better than others?

Thanks,
Hi Andrew,

you are corrected, depending on your dataset characteristics, some normalization methods are more appropriate than others. If the goal of your analysis is to compare different tissues, cell types, or cancers versus normals (or any other dataset with global methylation differences), you might want to take a look at our recent paper comparing normalization methods for such datasets: <a href="http://www.genomebiology.com/2014/15/12/503">http://www.genomebiology.com/2014/15/12/503</a>. We showed that Functional normalization performs really well at conserving global methylation differences (`preprocessFunnorm()` function in the minfi package)

Otherwise, if you are doing a EWAS-type of study, you may want to use `preprocessNoob()` + `preprocessQuantile()`, both implemented in the minfi packages as well. The former performs a background correction based on the negative control probes (implementation of the noob method described in <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3627582/">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3627582/</a>) that is always appropriate to use (and btw is called internally by `preprocessFunnorm` as well). The `preprocessQuantile()` function is an adaptation of quantile normalization to the 450k array, very appropriate when no global differences are expected between your samples (for instance whole-blood samples).

Finally, if you have a large dataset and think you could have batch effects, there is a recent paper that applies a batch effect correction method (RUVm) in top of Funnorm: <a href="http://nar.oxfordjournals.org/content/early/2015/05/18/nar.gkv526.full">http://nar.oxfordjournals.org/content/early/2015/05/18/nar.gkv526.full</a>

Hope this helps,

Jean-Philippe