[BioC] Testing for no difference

[Gustavo Fernández Bayón]

Hi Wolfgang.  


---------------------------
Enviado con Sparrow (http://www.sparrowmailapp.com/?sig)


El lunes 23 de julio de 2012 a las 17:09, Wolfgang Huber escribió:

> 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

I have just had a look at it. Thanks for the link. I did not even know that cluster ensembles existed. Sometimes, it is difficult to just stay up to date with all the methods that are available. That's why I find these conversations in the  BioC list so enriching. At the beginning, that was one of the ideas I was looking for, i.e., to prove that there was not a clear way of separating the 5 samples in a general way.   
> 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.

That seems very appropriate for me. That is  related to the TOST method, that Albyn Jones has pointed in one of the other answers, isn't it? I know is kind of double testing with two one-tail tests, and that it takes a parameter stating the amount of difference we are willing to admit before we say that the individuals are not equivalent.  

I have to admit this is one of the ideas I think I am more comfortable with. Maybe I can give it a try on my data, and then write it in a professional way, that is, just like if I knew what I was talking about. ;)
>  
> Best wishes
> Wolfgang

Thanks for your answer.

Regards,
Gus

  
>  
> 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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