Dear all,

I am a biostatistics student and working in a molecular biology lab. I have
the similar problem as Markis has. But the biology may be totally different.
My affy array samples come from pooled plasmid library(extract from yeast).
They are not from RNA.
So I did not expect it has same character as normal RNA profile.

I have used the same chip,  yeast strain for RNA profile, I got hundreds
gene express differentially; but the pooled plasmid library samples tole me
more than 3000  genes changed (mostly are from the library) although 9000
gene on the chip and my library has 6000 genes only. Using Affy MAS5 or RMA
followed by limma gave me similar number result : more than 3000. And the
data reproducibility is good, means high percent of the genes changes are
real.

I am very curious if the way to normalize the chips are reasonable and if I
should trust the results. I am thinking, if the genelist can be trust, what
I got from the chip is the relative ratio of each gene in the pooled
changed; which is related to what Naomi said: I know I gave the similar
amount of samples and the facility put on similar amount of tRNA on chips.
It's the assumption when we nomalize chips and it is also true in my case.

Just want to descripe what problem I met, what related results I got and
would like to hear suggestions or related ideas.

Thanks,

Di
Division of Biostatistics
Department of Epidemiology and Biostatistics
School of Medicine
Case Western Reserve University
Cleveland, OH


On 5/26/06, E Motakis, Mathematics <E.Motakis@bristol.ac.uk> wrote:
>
> Dear all,
>
> I am working on two colours microarray experiments and, from a set of
> 42000
> genes, I would like to identify the differentially expressed ones. I have
> read several articles on this issue and most of them imply that the number
> of differential expressed genes in such experiments should be a small
> number (compared to the whole set).
>
> Could anyone tell me why this is correct? What if I find half of the genes
> to be differentially expressed according to the t-test p-value?
>
> I am not discussing the issue of p-values and q-values yet. I am asking
> only about why most of the papers imply a low number of differentially
> expressed genes.
>
> Thank you,
> Makis
>
>
> ----------------------
> E Motakis, Mathematics
> E.Motakis@bristol.ac.uk
>
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