[BioC] RE: RMA normalization

[Dalphin, Mark]

Our results match what you say Mark: normalization across cell types by
quantiles, etc is problematic (at best) due t the different distributions of
the RNA concentrations. In our hands, simulations suggest that when more
than ~10% of the RNA species (randomly selected) change substantially in
concentration, _all_ of our normalization methods go out of whack.

We _believe_ that externally spiked-in standards would permit normalization
across multiple cell types. While we haven't had the chance to test external
standards for a variety of technical reasons (mostly finding a set that
really shows no cross-hybridization, then preparing that set in sufficient
quantity and then convincing all the people who do the benchwork that the
extra work is worth while), I have read that this too is no panacea. The
major complaint I have read is that a mis-matching of the amount of external
standard will really damage the ability to normalize the experiment and
leave no trace of that systematic error. It is apparently very tough to add
the external standard in a reproducable manner (disclaimer: I have no bench
experience with microarray).

Mark Dalphin

-----Original Message-----
From: bioconductor-bounces at stat.math.ethz.ch
[mailto:bioconductor-bounces at stat.math.ethz.ch]On Behalf Of Reimers,
Mark (NIH/NCI)
Sent: Monday, September 13, 2004 3:13 PM
To: 'bioconductor at stat.math.ethz.ch'
Subject: [BioC] RE: RMA normalization


Hello Hairong, Adai,
That suggestion was mine a few weeks ago. 
My thinking currently is that we may reasonably expect different cell types
to have different distributions of RNA abundances; as an extreme example,
some cells specialize in making one protein for export. Then it seems to me
our best shot is to make the raw data comparable within each cell type, and
to make the different cell types comparable per identical weight of RNA
(ideally we'd like to find some way to normalize by the number of cells).
Normalization within cell types might be done by quantiles; normalization
across cell types by the simpler (robust) mean until we can normalize by
cells. Is there a better way?
In practice I find substantial differences when normalizing across different
cell types, as opposed to normalizing within cell types separately. 
Does anyone else have experience with this?

Regards

Mark Reimers

Date: Fri, 10 Sep 2004 15:56:00 +0100
From: Adaikalavan Ramasamy <ramasamy at cancer.org.uk>
Subject: RE: [BioC] RMA normalization
To: Hairong Wei <HWei at ms.soph.uab.edu>
Cc: BioConductor mailing list <bioconductor at stat.math.ethz.ch>
Message-ID: <1094828160.3055.29.camel at ndmpc126.ihs.ox.ac.uk>
Content-Type: text/plain

I was under the impression getting a sufficient mRNA from a single sample
was difficult enough.

Sorry, I do not think I can be of much help as I never encountered this sort
of problem, perhaps due to my own inability to distinguish the terms mRNA,
sample, tissue. But there are many other people on the list who have better
appreciation of biology and hopefully one of them could advise you.

Could you give us the link to this message you are talking about.



On Fri, 2004-09-10 at 15:26, Hairong Wei wrote:
> Dear Adai:
> 
> Thanks for asking.  I got this phrase from the messages stored in the 
> archive yesterday.  My understand is that, suppose you have 100 
> arrays, and 10 mRNA samples from 10 tissues.  Each 10 arrays are 
> hybridized with mRNAs from the same tissue.  When you run RMA 
> algoritm, you run those arrays (10 each time) that hybridized with 
> mRNA from same tissue together rathan than running 100 arrays 
> together.  After running RMA for each tissue, the scaling is applied to
arrays form different tissues.
> 
> The reason for doing this is that it is not reasonable to assume that 
> the arrays from different have the same distribution.
> 
> What is you idea to do background.correction and normalization of 100 
> arrays across 10 tissues?
> 
> Thank you very much in advance
> 
> Hairong Wei, Ph.D.
> Department of Biostatisitics
> University of Alabama at Birmingham
> Phone:  205-975-7762


Mark Reimers,
senior research fellow, 
National Cancer Inst., and SRA,
9000 Rockville Pike, bldg 37, room 5068
Bethesda MD 20892


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