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 ?probeNames? for >signature ?"ExpressionTimeSeries"? > >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]] >>> >>> _______________________________________________ >>> Bioconductor mailing list >>> Bioconductor at r-project.org >>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>> Search the archives: >>>http://news.gmane.org/gmane.science.biology.informatics.conductor >>> >> >>-- >>Antti Honkela >>antti.honkela at hiit.fi - http://www.hiit.fi/u/ahonkela/ >> >

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 ?probeNames? for >> signature ?"ExpressionTimeSeries"? 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/