[BioC] normalisation assumptions (violation of)
sdavis2 at mail.nih.gov
Mon Aug 7 13:52:45 CEST 2006
On 8/7/06 7:29 AM, "J.delasHeras at ed.ac.uk" <J.delasHeras at ed.ac.uk> wrote:
> Quoting Sean Davis <sdavis2 at mail.nih.gov>:
>> On 8/7/06 6:59 AM, "J.delasHeras at ed.ac.uk" <J.delasHeras at ed.ac.uk> wrote:
>>> I have a set of data from an experiment where there appears to be an
>>> effect of the treatment on a large number of genes. I put scatterplots
>>> for 6 of the slides here:
>>> these are Cy3 vs Cy5, in log scale.
>>> These show that many genes are differentially expressed, and they are
>>> mostly one one side only (upregulated; some of those slides are dye
>>> Would this appear to violate (too much) any of the assumptions made by
>>> loess normalisation? Should I investigate other normalisation
>> First, I would start by doing a VERY thorough evalutation of the slide
>> quality for these slides, as these are very distorted scatterplots. IF the
>> slide quality looks OK, then I would probably stay away from a non-linear
>> normalization method, as these will tend to make your
>> differentially-expressed genes look less differentially-expressed.
> Hi Sean,
> thanks for your reply. The slides are good, I checked them well. The
> strong effect is not so unexpected, as it involves transfection of
> cells with a DNA-binding protein fused to a strong transactivator, so
> in theory the fusion protein could be responsible of the expression of
> a very large number of genes. There is some specificity to the binding,
> but there should be many target sites, often at promoters... So the
> effects are more or less what we expected, I suppose, and the quality
> of the slides is good. The second spike going either almost vertical or
> almost horizontal should correspond to those genes that are not
> expressed on the particular cell line, but expressed after transfection.
> Do you have any suggestions of what sort of methods to use, for the
> normalisation of such experiments? Until now I used loess for
> everything, but I wasn't sure it would be okay for this experiment when
> I saw these plots.
You can certainly try loess and see how the result looks, as scatterplots
are notorious for "hiding" where the data are most dense. Alternatively,
you could try "rotating" the scatterplot until the body of the data is where
you think it should be--I don't know if there is a method in Bioconductor
that does this, though.
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