Question

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 6.7 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 • 1.2k views

ADD COMMENT • link updated 20 months ago by yr542 • 0 • written 6.7 years ago by znl207 • 0

score 0 · Answer 1 · 2018-03-21

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Günter Klambauer ▴ 540

@gunter-klambauer-5426

Last seen 3.8 years 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

ADD COMMENT • link 6.7 years ago Günter Klambauer ▴ 540

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Does the exomecn.mops do the same normalization?

Lets say I did the following:

# "Calculation of read counts from BAM files" (page 15, reference manual)

bamDataRanges <- getReadCountsFromBAM(BAMFiles, sampleNames=NULL)

# The core function (page 6, of manual)

resCNMOPS <- exomecn.mops(bamDataRanges)

Will the exomecn.mops also normalize it endogenously or is that only for the standard cn.mops function?

ADD REPLY • link 20 months ago yr542 • 0