best way to normalize for differences in sample read depth: normalize genome vs cn.MOPs
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znl207 • 0
@znl207-14983
Last seen 3.5 years ago

I would like your advice about normalization with cn.MOPs - as I understand cn.MOPs algorithm includes normalization to compare between different loci and across different samples. cn.MOPs also contains the "normalize genome" function to compare across samples. What is the difference between these two options? I am working with a dataset of variable read depth samples ranging from 7x to 51x with an average of 16x. What is your recommendation for normalizing across these different samples? Should the "normalize genome" option be used?

Thank you for your help.

cn.mops normalization • 403 views
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@gunter-klambauer-5426
Last seen 8 months ago
Austria

Hi,

Yes, the function "cn.mops" internally also applies normalization. I just added the function "normalizeGenome" and "normalizeChromosome" for Users who want to normalize by hand. So, there is no difference between first applying the normalization function and then running cn.mops with "norm=0" (no normalization). 
Yes, you are right the normalization should correct for different coverages. Even if your dataset contains vastly differing coverages, you can just run the standard cn.mops functions with the default options (which include normalization).

Regards,
Günter

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