Entering edit mode
Dear Miguel,
In principle, constructing a likelihood ratio test of equality of
dispersions for two groups would not be difficult. I'd have to sit
down
and work out some code, however, and I don't have time to do that just
now. It is unclear to me whether it could be done reliably for just
two
libraries in each group. This is first time I've been asked for it.
Best wishes
Gordon
Professor Gordon K Smyth,
Bioinformatics Division,
Walter and Eliza Hall Institute of Medical Research,
1G Royal Parade, Parkville, Vic 3052, Australia.
Tel: (03) 9345 2326, Fax (03) 9347 0852,
smyth at wehi.edu.au
http://www.wehi.edu.au
http://www.statsci.org/smyth
> On Thu, Feb 23, 2012 at 1:19 AM, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote:
>
>> Dear Miguel,
>>
>> Well, you could obviously subset your data, and compute the tagwise
>> dispersions separately for each subset, and compare the two. Do a
formal
>> test requires a more work, and would be genewise anyway.
>>
>> I find it hard to imagine in what context it would make sense to
test
>> whether two libraries (R4.Hot and R5.Hot) are more different than
two other
>> libraries. For a particular gene? Averaged over genes?
>>
>> Could you not just make an MDS plot and look to see how far apart
the
>> samples are from each other?
>>
>> What are you trying to achieve here from a biological point of
view? What
>> biological question are you trying to answer?
>>
>> Best wishes
>> Gordon
>>
>> Date: Tue, 21 Feb 2012 15:16:53 +0100
>>> From: Miguel Gallach <miguel.gallach at="" univie.ac.at="">
>>> To: Bioconductor mailing list <bioconductor at="" r-project.org="">
>>> Subject: [BioC] Testing for differential expression variability
with
>>> edgeR
>>>
>>> Dear Bioconductor list,
>>>
>>> I am using edgeR to test for differential expression but I also I
would
>>> like to know how to test whether expression variability (per gene)
is
>>> significantly different between biological groups or treatments.
>>>
>>> For instance, the next is my experimental design, according to
which I
>>> have
>>> two treatments (Hot and Cold) and two biological groups (Hot
Adapted and
>>> Cold adapted; two replicates each).
>>>
>>> $samples
>>> group lib.size norm.factors
>>> R4.Hot HotAdaptedHot 17409289 0.9881635
>>> R5.Hot HotAdaptedHot 17642552 1.0818144
>>> R9.Hot ColdAdaptedHot 20010974 0.8621807
>>> R10.Hot ColdAdaptedHot 14064143 0.8932791
>>> R4.Cold HotAdaptedCold 11968317 1.0061084
>>> R5.Cold HotAdaptedCold 11072832 1.0523857
>>> R9.Cold ColdAdaptedCold 22386103 1.0520949
>>> R10.Cold ColdAdaptedCold 17408532 1.0903311
>>>
>>>
>>> I would like to detect those genes for which the expression
variability
>>> between R4.Hot and R5.Hot is significantly different to that
obtained for
>>> R4.Cold and R5.Cold. Can I use the tagwise dispersion values
provided by
>>> edgeR, and how??
>>>
>>> Many thanks!
>>> Miguel
> --
> Miguel Gallach
> Center for Integrative Bioinformatics Vienna (CIBIV)
> Max F. Perutz Laboratories(MFPL)
> Telf: +43 1 4277 24029
>
> Postal Address:
> Ebene 1
> Campus Vienna Biocenter 5
> CIBIV, MFPL
> 1030 Vienna
> Austria
>
> e-mail:
> miguel.gallach at univie.ac.at
> migaca2001 at gmail.com
>
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