[BioC] Testing for no difference

[Gustavo Fernández Bayón]

Hi Wolfgang.  

I had already seen that paper, thanks to @yokofakun in twitter. How could that happen? Is it a manuscript? Although I have seen strange things in the past, this has really hit me. A fellow here at the lab showed me yesterday a manuscript where the chromatin inmunoprecipitation step was described with a figure drawn at hand with a pencil. At least I should say that the guy knew how to draw a histone. ;)

Regards,
Gus



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El lunes 23 de julio de 2012 a las 17:31, Wolfgang Huber escribió:

>  
> Btw, a less complex way to approach such an analysis is highlighted here:
>  
> http://nsaunders.wordpress.com/2012/07/23/we-really-dont-care-what-statistical-method-you-used/
>  
> Best wishes
> Wolfgang
>  
> Jul/23/12 5:09 PM, Wolfgang Huber scripsit::
> > Gustavo,
> >  
> > it seems that your question can be rephrased as 'there is no evidence
> > for these 5 samples forming any (nontrivial, i.e. different from size 1
> > or 5) clusters'. If so, have a look at the package 'clue':
> > http://cran.r-project.org/web/packages/clue/vignettes/clue.pdf
> >  
> > Of course, proving the absence of something (e.g., a systematic
> > difference) is very difficult, and in your case as in most it's probably
> > better to aim for saying that any difference that may exist is smaller
> > than some (more or less arbitrary) measure.
> >  
> > Best wishes
> > Wolfgang
> >  
> > Jul/23/12 9:52 AM, Gustavo Fernández Bayón scripsit::
> > > Hi everybody.
> > >  
> > > I have a set of only 5 samples of Illumina27k methylation data. We
> > > have extracted some info from the probes, but now the researcher in
> > > charge of the project wants something that could support his idea of
> > > the five samples to be practically equivalent wrt to their methylation
> > > levels.
> > >  
> > > I know that the sample is quite small. Intuitively, if you plot
> > > densities from the 5 samples, they are almost equal. Problem is, I do
> > > not know a way in which I could give a statistical significance about
> > > this fact (yes, as always, there is the "I need a p-value" problem).
> > >  
> > > 1) I did PCA on both beta values and m-values, and found that the
> > > first principal component accounts for between 90 and 91% of the total
> > > variance. In the biplot, the five samples appear to be very close.
> > >  
> > > 2) I asked for advice to a statistician friend, and we tried to do the
> > > following: probe by probe, we tried a Leave-One-Out approach, by
> > > calculating a confidence interval for 4 of the samples and seeing if
> > > the remaining probe falls within the interval. Then, for each probe, I
> > > summed the number of times a methylation value fell out of the
> > > confInt, only to find out that nearly 53% of the probes contain -in
> > > this sense- 'outliers'.
> > >  
> > > At first it surprised me, but then I noticed -by plotting the outliers
> > > against the samples- that these 'outliers' were uniformly distributed
> > > among samples, which I think is again intuitive, i.e., there are
> > > indeed differences (statistical differences, maybe not biological)
> > > among samples, but there is no global difference of one of the samples
> > > w.r.t. the others.
> > >  
> > > These differences might be related to technical noise, so I was
> > > thinking of imposing a minimum difference in order to test again for
> > > outliers. Would this be ok?
> > >  
> > > Is there any method I can use to try to show there is no difference
> > > among the samples? Or should I stay with the graphs and the intuitive
> > > description on the text?
> > >  
> > > Thanks. Any help or hint would be much appreciated.
> > >  
> > > Regards,
> > > Gustavo
> > >  
> > > ---------------------------
> > > Enviado con Sparrow (http://www.sparrowmailapp.com/?sig)
> > >  
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> >  
>  
>  
>  
>  
> --  
> Best wishes
> Wolfgang
>  
> Wolfgang Huber
> EMBL
> http://www.embl.de/research/units/genome_biology/huber
>  
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