[BioC] RE: quantile normalization

[Reimers, Mark (NIH/NCI)]

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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