[BioC] Testing for no difference

[Gustavo Fernández Bayón]

Hi Albyn.  

As I have already answered to W. Huber in this thread, I think that TOST could be a good choice here. I think I am going to give it a try. Problem is, I am comfortable with the two sample description of TOST but, what about multiple groups? Should I do a similar procedure to the one I was doing before? That is, leave-one-out methods for each of the probes, but doing TOST's instead of common tests…

Thanks for your answer.

Regards,
Gus



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


El lunes 23 de julio de 2012 a las 17:29, jones escribió:

> You might look into "tests of equivalence", one common procedure
> involves defining an interval (-a,a) and doing two one sided
> tests ("TOST") for H_0: delta > a and delta < -a, which is equivalent
> to checking that the CI for the difference is contained in
> the specified interval.
>  
> albyn
>  
> On 7/23/12 12:52 AM, Gustavo Fernández Bayón wrote:
> > 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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