[BioC] Tigre Package question 2

Solanki, Anisha a.solanki.12 at ucl.ac.uk
Sun Feb 9 17:49:26 CET 2014


Dear Antii,

I have now solved the previous error by adding variances independently to
the expression Dataset. I just had another quick question. The targets are
ranked by the log-likelihood. Does this mean that the higher the
log-likelihood the greater the probability of the gene being a target or
vice versa? Also what does null log likelihood stand for?

Thanks

Anisha 






On 09/02/2014 13:32, "Solanki, Anisha" <a.solanki.12 at ucl.ac.uk> wrote:

>Dear Antti,
>
>Thanks for your reply to the Earlier question. I have managed to run the
>GPLearn command and obtained some interesting results.
>
>However, when I try to run the GPRanktargets command I get this error
>"Error in yvar + sigma : non-conformable arrays".
>
>I think this means that my Data lacks calculated variances. As I
>understand from your User guide you process affymetrix Datasets using the
>mmgmos command from the PUMA package which automatically calculates the
>variances for you. However, when I try to run my expression value matrix
>through this mmgmos command it doesn't work and gives me this error
>"unable to find an inherited method for function ŒprobeNames¹ for
>signature Œ"ExpressionTimeSeries"¹
>
>Please advise on whether I should use an independent method to calculate
>the variances so the GPRanktargets works or whether the error lies
>somewhere else.
>
>Thanks
>Anisha
>
>
>
>
>
>
>
>On 06/02/2014 06:32, "Antti Honkela" <antti.honkela at hiit.fi> wrote:
>
>>Dear Anisha,
>>
>>GPLearn function expects the data (your MyExpressionSet) to be an
>>ExpressionTimeSeries object that you can create using functions
>>processData() or processRawData(). Can you please make sure you are
>>passing it the correct kind of object, as a wrong kind of object could
>>cause an error like you describe?
>>
>>Furthermore, in your example you specify a model with a regulating TF
>>but no targets. You should add some targets to get a sensible model. For
>>screening candidate targets GPRankTargets() provides an easier option
>>than GPLearn().
>>
>>Thanks a lot for your report, I will update the package to make the
>>error message more informative!
>>
>>
>>Antti
>>
>>
>>On 2014-02-05 21:47 , Solanki, Anisha wrote:
>>>
>>> When I tried to run the GPLearn command with my expression set it gives
>>>me an error
>>>
>>> model <- GPLearn(MyExpressionSet, TF="ENSMUSG00000001300",
>>>useGpdisim=TRUE, quiet=TRUE)
>>>
>>> "Error in yvar[[1]] :subscript out of bounds".
>>>
>>> Please advise
>>>
>>> 	[[alternative HTML version deleted]]
>>>
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>>
>>-- 
>>Antti Honkela
>>antti.honkela at hiit.fi   -   http://www.hiit.fi/u/ahonkela/
>>
>



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