[BioC] Pooling in microarray studies

James MacDonald jmacdon at med.umich.edu
Mon Oct 6 10:42:43 MEST 2003

You might consider trying to run the chips with 8 ug mRNA anyway. At our
facility we routinely run chips with as little as 5 ug.


James W. MacDonald
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109

>>> Wiesner Vos <vos at stats.ox.ac.uk> 10/06/03 09:27AM >>>

I have question arising to the pooling of mRNA
samples. Someone approached me about the
following problem:

The study wants to use Affymetrix chips to study
changes in expression between a group of treated
mice and a group untreated mice. There are 10 mice
in each group. It is only possible to extract
8 ug of RNA from each mouse, not enough for one chip.
(According to the experimenters they require 10 ug per
chip)  So it is not possible to use biological
replicate chips for each individual mice. Now the issue
is whether to perhaps pool the RNA in each group
and carry out analysis on technical replicates from the
pooled samples.

As I understand it pooling may reduce the precision, with
the risk that one or few samples can dominate the outcome, and
that averaging over single sample hybridisations is perhaps
safer than using pooled samples. However in this case you cannot
do single sample hybridisations.

I was wondering if the following approach is an acceptable
compromise to retain at least some information on the between
sample variation in each group:

Mix the RNA from 2 different mice on a single chip to get 5
hybridisations, where the hybridisation on each chip is from the
mix of the RNA samples of two mice? I though that this may
enable you to some extend if all the mice are behaving
similarly. Ofcourse one would not be able to distinguish
between the behaviour of the two mice relating to the same
chip. Or is it better to accept that you do not have enough
RNA to hybridize the sample for each individual to a separate
chip and pool the samples and accept the risk that
one sample may dominate the outcome? The best
solution did not seem obvious (to me at least!)

Any comments will be much appreciated.


Wiesner J. Vos
Department of Statistics
University of Oxford
1 South Parks Road
United Kingdom

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