[BioC] correlation of dye swap experiments

Sheetal Bhan sbhan at biochem.umsmed.edu
Mon Jun 20 22:26:45 CEST 2005

I have done a few dye swap experiments. And strangely the dye swap
experiments which should show an inverse co-rrelation show a high
positive correlation (about 0.7)
I would appreciate any comments or suggestions regarding this.

Sheetal Bhan
Graduate Student
Donald B. Sittman Lab
Dept.of Biochemistry
Univ.of Mississippi Medical Center
2500 North State Street
Jackson, MS-39216
E-mail: sbhan at biochem.umsmed.edu

>>> Wolfgang Huber <huber at ebi.ac.uk> 06/20/05 1:36 PM >>>
Hi Claus,

thanks for pointing this out. This has slipped through since the 
standard deviation of log(x) is approximately equal to the CV of x, if 
the latter is not too large (this is seen from a first order expansion),

so when I talked about "CV of replicates" I meant the standard deviation

of their log-ratios.

However, in his mail Jakob refered to "CV of log-ratios", and you are 
absolutely right - these are not appropriate.

	Best wishes

Claus Mayer wrote:
> Hi Wolfgang and Jakob
> I think there is some confusion here. The CV is (at least as far as I 
> know) standard deviation divided by mean, so it is scale-invariant,
> dividing all log-ratios by 2 shouldn't make a difference. It is not 
> location-invariant though, which could be the explanation for the 
> increased CV. The normalisation centers the log-ratio distribution, so

> for most genes the mean should be closer to 0 than before, which will 
> result in an increased CV.
> For that reason the CV is not an appropriate tool here to assess the 
> effect of the normalisation. As Wolfgang points out, the distribution 
> of  F- or t-statistics (or the corresponding p-values)  should be a 
> reasonable (and scale-invariant!) exploratory tool to assess the
> of the normalisation.
> Best Wishes
> Claus
> Wolfgang Huber wrote:
>> Hi Jakob,
>> it can be misleading to look solely at the CV of replicates to assess

>> normalization. Because if you did that, a normalization method that 
>> simply divided all your log-ratios by 2 would be twice as good, and 
>> one that sets everything to zero would be even better.
>> What I usually do is look at the distribution of F- or t-statistics 
>> per gene across arrays for some meaningful biological grouping of the

>> samples. There need to be enough replicate arrays within each group 
>> for this.
>> Still, if you used a "reasonable" normalization method, it sounds it 
>> didn't work well on your data. It is hard to say more without more 
>> details on what you did and diagnostic plots etc.
>> Best regards
>>  Wolfgang
>> Jakob Hedegaard wrote:
>>> Hi list
>>> I am working on a data set from 24 arrays, where each array consist
>>> 6.912 spots replicated pair wise at two different spatial locations.
>>> For quality evaluation, I have calculated the CV of "raw" log-ratios
>>> each pair wise replicated spot (13.824 points per array) and have
>>> observed the expected tendency of decreasing CV by increasing
>>> spot intensity.
>>> When calculating the CV for normalized data, I have observed that
the CV
>>> has increased compared to CV for raw data. This essentially means
>>> normalization is making data worse in terms of variance among
>>> spots!
>>> Has anybody observed something similar?
>>> Is this what should be expected or does it indicate that the
>>> normalization is not optimally performed?
>>> Looking forward hearing from you!
>>> Jakob

Best regards

Wolfgang Huber
European Bioinformatics Institute
European Molecular Biology Laboratory
Cambridge CB10 1SD
Phone: +44 1223 494642
Fax:   +44 1223 494486
Http:  www.ebi.ac.uk/huber

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