[BioC] normalisation assumptions (violation of)

[J.delasHeras at ed.ac.uk]

Hi Henrik,

> Roughly what fraction of DEs do you except/see by visual inspection?
> BTW, it is not clear if your plots in scatterplots.gif are on the
> intensity or log scale, but looking at the noise structure I guess on
> the log scale.

Yes, it's log scale. I did mention it in teh other thread but forgot to 
say it here.
What fraction? That's hard to say. Visually I'd say easily 20 or 30%. 
But that's a rough estimate. I thought this was probably a lot higher 
than most experiments.

> loess(), not lowess(), can be tuned to be very robust against outliers
> including non-symmetric ones.  I know Gordon Smyth has done some
> examples/slides on this, but I'm not sure if they're in limma or not.
> In addition, in the aroma.light package you can assign weights to the
> datapoints for some of the normalization methods.  Assigning a smaller
> weight to a datapoint will make that datapoint have less of a say in
> the estimation of the normalization function, but when it comes to
> normalize/transform the datapoints, all are transformed equally much.
> So with weights you may be able to tune your robustness against
> outliers further.

that's on my "to do" list... I can use weights in limma.

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
Institute for Cell & Molecular Biology        Fax:   +44 (0)131 6507360
Swann Building, Mayfield Road
University of Edinburgh
Edinburgh EH9 3JR
UK