I am not an expert of statistic. Why not use ANOVA?

Jack

2012/7/23 Wolfgang Huber <whuber@embl.de>

>
> 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/<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<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<http://www.sparrowmailapp.com/?sig>
>>> )
>>>
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>>>
>>>
>>
>>
>
> --
> Best wishes
>         Wolfgang
>
> Wolfgang Huber
> EMBL
> http://www.embl.de/research/**units/genome_biology/huber<http://www.embl.de/research/units/genome_biology/huber>
>
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