[BioC] RE: quantile normalization

[H. Han]

Hi, Mark and Jim:

thanks for answering my post. In my case, the samples are not too
different - they are from different dosage levels of the same treatment. i'm
expecting some differences between two normalization methods. Though the
actual differences are somewhat larger than expected (dozen vs a few
thousand significant genes ). One reason of course, as Jim pointed out,
"replicate only" method would pick out more false sig genes. i now think
that a particular reason that makes our difference large is number of
replicates. i have 4 replicates each condition. when i am doing "replicate
only" normalization, there is a big chance that all four have similar
quantile ranks, and hence the same expression values. this reduces st.
devation, and increases t values in general. i assume when # of replicates
become larger, the outcomes of both methods would be closer.

regards,
hillary


----- Original Message ----- 
From: "Reimers, Mark (NIH/NCI)" <reimersm at mail.nih.gov>
To: <bioconductor at stat.math.ethz.ch>
Sent: Monday, August 30, 2004 12:01 PM
Subject: [BioC] RE: quantile normalization


> How different are your 'biologically different' samples? In our experience
> quantile-normalizing across very different samples makes a noticeable
> difference in relative expression within fairly similar samples.
>
> Our main data is cancer cell lines from 9 different tissues, and we find
> considerable differences (ie 2% of genes greater than factor of 2
> different), comparing normalization within tissue-of-origin to
normalization
> across all samples. My opinion now is that we should normalize within
> tissue-of-origin, and then standardize raw data across tissues by scaling
to
> constant median. However I find that RMA (1.3) gives different numbers
when
> I separate out the normalize( cel.data ) process from estimation ( rma(
> normed.data , normalize=F)), compared with rma( cel.data).  Has anyone
else
> observed this?
>
> Regards
>
> Mark
>
> Message: 1
> Date: Sun, 29 Aug 2004 18:00:30 -0400
> From: "H. Han" <hihan at brown.edu>
> Subject: [BioC] quantile normalization
> To: <bioconductor at stat.math.ethz.ch>
> Message-ID: <00bb01c48e13$99faa680$48c49480 at micron10>
> Content-Type: text/plain
>
> Hi:
>
> Does anyone has input on compatibility of "replicate-only" vs.
"all-sample"
> quantile normalizations? I'd assume that "true significant" genes would be
> picked up by either "replicate-only" or "all-sample" method, though the
> latter is surely more conservative (by forcing the same distribution
across
> all samples, replicates or not).  My analysis though seem to select two
> distinct lists of genes by two methods. e.g. If I pick top few hundreds
from
> both lists, there'd be little overlap. Would it because my initial pool of
> genes are large (10,000 or so), or inherently these two methods are two
> assumptions, and not to be compared?
>
> thanks in advance,
>
> Hillary
>
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