[BioC] FW: Tigre Package question 3

[Solanki, Anisha]

>Dear Antti,
>
>Thanks for your reply. The information you have given me has been very
>useful. I had another quick question regarding the mmgmos command. I
>understand that the command accepts data as an AffyObject. However, I have
>data from RNA-Seq and not from affymetrix microarrays. Hence I cannot
>create an Affyobject from my data as the object requires CEL files to
>convert the data into an AffyObject. Is there any other alternative other
>than using an Affyobject. I have tried to run a matrix with the expression
>values of every sample from my data. However the command mmgmos doesn't
>seem to accept this as a valid object.
>
>Please advise.
>
>Thanks
>
>Anisha
>
>
>On 10/02/2014 09:38, "Antti Honkela" <antti.honkela at hiit.fi> wrote:
>
>>On 2014-02-09 18:49 , Solanki, Anisha wrote:
>>
>>Dear Anisha,
>>
>>> I have now solved the previous error by adding variances independently
>>>to
>>> the expression Dataset.
>>
>>The error variances are critical to the accuracy of the method, so you
>>should never just impute any values there without careful consideration.
>>More about how you could fix this better below.
>>
>>> 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?
>>
>>Our method is based on comparing log-likelihoods over different data
>>sets (time series for different genes), which is slightly trickier than
>>usual comparison of log-likelihoods over the same data.
>>
>>The log-likelihood measures how well the data fit a model assuming
>>regulation, therefore higher log-likelihood should be counted as
>>evidence for being a target.
>>
>>That said, some time series are easy to fit, and get a high likelihood
>>over practically any model. To catch these, we fit the baseline or null
>>model (which is just a time-independent Gaussian). We can then filter
>>out genes that fit the null model equally well or better than the true
>>model.
>>
>>Finally, even though one might consider the likelihood ratio of real vs.
>>null a useful statistic, it is actually not good for ranking. This is
>>because the range of null model likelihoods is much larger, and
>>therefore the ranking will be determined by how badly the null model
>>fits instead of how well the real model fits, and tell nothing about the
>>regulation.
>>
>>In summary, you should:
>>1. *Filter* by likelihood ratio real/null: only keep genes where
>>  log-likelihood > null-log-likelihood
>>2. *Rank* remaining genes by log-likelihood
>>
>>>> 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
>>
>>You should run mmgmos on the original AffyBatch object, not on an
>>ExpressionTimeSeries object.
>>
>>
>>Hope this helps,
>>
>>Antti
>>
>>-- 
>>Antti Honkela
>>antti.honkela at hiit.fi   -   http://www.hiit.fi/u/ahonkela/
>>
>