Hi Gordon,
Thanks for the reply. That's good to know I shouldn't be worried about the
results, but then again I'm not really sure, as you say, if I should even go
down this road. Using the standard edgeR normalization would also be
meaningless in my case unfortunately. I have two data sets of counts each
belonging to different organisms. One data set runs smoothly and the other
has all these problems yet the experiments done on both were exactly the
same. Kind of puzzling and unfortunate but I will investigate more into what
you're saying about the offsets and see if I can't adjust my
strategy. Thanks for the insights!
Best,
Sean
On Thu, Jul 21, 2011 at 4:29 PM, Gordon K Smyth <sm...@wehi.edu.au> wrote:
> Dear Sean,
>
> The fact that you get a warning message does not mean that the function
> hasn't worked satisfactorily. It is mainly to alert you to an unusual
> situation. The warnings affect only 15 tags out of thousands, so are
> unlikely to have any influence on your results.
>
> The main issue is that there is no biological dispersion in your data. My
> guess is that you have computed offsets in such as way that they soak up all
> the biological variation in your data, and there is no
> Poisson-overdispersion left to estimate. Your data could even now be
> underdispersed relative to Poisson, once the offsets are accounted for. This
> suggests to me that your offsets are over-fitted in some sense, and that
> statistical analysis using these offsets might not be meaningful.
>
> Have you tried a more standard analysis of your data, using the built-in
> normalization procedures in edgeR?
>
> Best wishes
> Gordon
>
> Date: Wed, 20 Jul 2011 18:17:42 -0700
>> From: Sean Ruddy <srudd...@gmail.com>
>> To: bioc-sig-sequencing@r-project.**org<bioc-sig-sequencing@r-project.org>
>> Subject: [Bioc-sig-seq] edgeR Warning Messages from
>> maximizeInterpolant called from estimateGLMTagwiseDisp
>>
>> Hi,
>>
>> I'm trying to get tagwise estimates from estimateGLMTagwiseDsip with the
>> argument "method = 'common' " and 15 warning messages each of which are
>> one
>> of 2 flavors:
>>
>> 9: In maximizeInterpolant(spline.**pts, apl.smooth[j, ]) : Divergence
>> 10: In maximizeInterpolant(spline.**pts, apl.smooth[j, ]) : max
>> iterations
>> exceeded
>>
>> After looking at the functions "**dispCoxReidInterpolateTagwise" and
>> "maximizeInterpolant", I believe that each of these warnings correspond to
>> a
>> specific tag; correct me if I'm wrong. I'd like to find out which tags are
>> creating this warning so I can get an idea of what type of data is causing
>> this. I've tried debug() but that information isn't passed to the
>> functions.
>> In particular, the part of the "dispCoxReid..." function below
>>
>> for (j in 1:ntags) d[j] <- maximizeInterpolant(spline.**pts,
>> apl.smooth[j, ])
>>
>> I tried changing the functions internally to pass that information and
>> output it but it doesn't seem to work. The changes don't have any effect.
>> Is
>> there an feasible way of figuring out the tags that cause the warnings?
>> I'm
>> out of ideas...
>>
>> Also, the common dispersion estimate is very low -- 1e-08 neighborhood --
>> which I'm guessing is one main reason why maximizeInterpolant is having
>> trouble. On that note, I have no idea why the common dispersion value is
>> so
>> low. I do not have technical replicates and the replicates I do have are
>> hardly biological. I would expect to get a higher dispersion estimate, but
>> it's somewhat difficult to tell since I also have an offset value for each
>> tag and sample and thousands of tags.
>>
>> Any help is appreciated!
>>
>> Thanks!
>> Sean
>>
>
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