[BioC] normalisation assumptions (violation of)

[J.delasHeras at ed.ac.uk]

Quoting Sean Davis <sdavis2 at mail.nih.gov>:

[...]
> 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.
>
> Sean

Thanks Sean.

I already tried loess, and this is the MA plot for the first set of 
data looks like this:

http://mcnach.com/MISC/MAplots2.png

which looks okay to me. You see the ascending diagonal is denser, which 
contains all those newly activated spots. I knew a few genes that were 
expected to be there (from RT data) and they line up nicely on that 
diagonal.

This was without substracting background.
When I attempted to correct for background I run into problems. Mainly 
because some slides have a higher bkg than usual, and the signal is 
lower than the local bkg for a good number of spots. When I use 
"subtract" as a bkg correction method, it results in many negative 
intensities, and those spots are removed. I then tried "half" to 
overcome this, so that negative values are turned into an arbitrary 
0.5... and this totally flattened the MA plot, and nothing was 
statistically DE. I showed this on a previous thread:

http://mcnach.com/MISC/MAplots1.png

It's very striking. It leaves me no other choice but not removing 
background (which is increasingly looking like the best option in 
general, in my still short experience...)

Jose

-- 
Dr. Jose I. de las Heras                      Email: J.delasHeras at ed.ac.uk
The Wellcome Trust Centre for Cell Biology    Phone: +44 (0)131 6513374
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