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Mark Reimers
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70
@mark-reimers-658
Last seen 10.3 years ago
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@brown.edu>
Subject: [BioC] quantile normalization
To: <bioconductor@stat.math.ethz.ch>
Message-ID: <00bb01c48e13$99faa680$48c49480@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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