Thanks for your email Henrik.

> When you say heterogeneous, do you mean that they contain a lot of CN
> aberrations or do you mean that they have different noise levels?

Different noise levels.

>
> If "not too many" things go on in your reference samples, then you
> should expect to get an improvement when you calculate the reference
> channel as the average over a larger and larger pool of samples.  A
> few years ago I checked this on 500K data and I found a dramatic drop
> in SNRs when increasing from 5 to 10 to 20 reference samples and then
> i flattened out.
>
> However, if you look Nannya et al (2005), their CNAG method tries to
> identify a subset of reference samples that gives best SNRs.  This is
> to say that you believe there is a set of reference samples that are
> more "normal" than others.  An alternative argument, which may make
> even more sense is that there will always be some systematic effects
> remaining in the estimates and if you can locate a set of reference
> samples that have similar remaining effects as you test sample, they
> will cancel out better than if other reference samples where used.
> Note that this strategy uses different pools of references for each
> test sample.   I know that the Broad Institute (Gaddy Getz, Scott
> Carter et al.) are doing something similar in the TCGA project and
> they say they get better SNRs.

thank you very much for these pointers, I'll look into them.
I'll try and do something a bit more systematic and post the results.

cheers

Lavinia.
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