Hi Alejandro,

Thanks for your help. That solved the problem. I noticed a strange results
when running DEXSeq using intronic features. All my log2FoldChange values
are NaN.  I am getting the following warning() messages:

Warning messages:
1: In glmgam.fit(mm, disps[good], coef.start = coefs) :
  Too much damping - convergence tolerance not achievable
2: In fitDispersionFunction(exonCountSet) :
  Negative intercept value in the dispersion function, it will be set to 0.
Check fit diagnostics plot section from the vignette.

I checked the fit diagnostic plot section from the vignette, but I am not
sure as to what could be problem. If it means anything, I was testing the
code on a subset of the data which was 2000 introns. There are no NAs in my
exonCountSet. Any ideas as to what is happening?

Fong


On Wed, Feb 6, 2013 at 2:39 PM, Alejandro Reyes <alejandro.reyes@embl.de>wrote:

> Dear Fong Chun Chan,
>
> Yes the IDs from the introns are causing some problems in the functions.
>
> It is because DEXSeq uses feature names from the exonCountSet object the
> concatenation of the gene ID and the exon ID separated by a ":" character,
> so introducing additional ":" causes some parsing errors.  Could you
> replace the ":" in your exon IDs for another character?
> I think that should solve this problem!
>
> Alejandro
>
>
>
> Thanks for the reply. I've tried to use DEXSeq for intronic regions just
> like how I did it with exons and I am running in this the problem when I am
> trying to estimatelog2FoldChanges. This is the error:
>
> exonCountSet <- estimatelog2FoldChanges( exonCountSet )
> Error in do.call(`[[<-`, c(quote(coefIndices), as.list(lvlTbl[i, ]),
> coefNames[i])) :
>   [[ ]] subscript out of bounds
>
> My code to get to this point is (ignore the fact that the variables are
> called exon. I am just copying the code from my exonic DEXSeq run):
> -----------------------------
>
> print('Building ExonCountSet ...')
> exonCountSet <- newExonCountSet( countData = sumExonsDf[selectedExons,
> samples], design = designMat, geneIDs = sumExonsDf[selectedExons,
> 'geneID'], exonID = sumExonsDf[selectedExons, 3], exonIntervals =
> exonAnnotDf[selectedExons, c('chr', 'start', 'end', 'strand')] )
> print('... Done')
>
> print('Estimating size factors ...')
> exonCountSet <- estimateSizeFactors(**exonCountSet)
> print('... Done')
>
> print('Estimating dispersions ...')
> if ( opt$trt ){
>         print('Using the TRT functions ...')
>         exonCountSet <- estimateDispersionsTRT( exonCountSet, nCores =
> opt$nCores )
> } else{
>         exonCountSet <- estimateDispersions(**exonCountSet, nCores =
> opt$nCores)
> }
> print('... Done')
>
> print('Fitting dispersions ...')
> exonCountSet <- fitDispersionFunction( exonCountSet )
> print('... Done')
>
> print('Testing for differential exon usage ...')
> if ( opt$trt ){
>         print('Using the TRT functions ...')
>         exonCountSet <- testForDEUTRT( exonCountSet, nCores = opt$nCores )
> } else{
>         exonCountSet <- testForDEU( exonCountSet, nCores = opt$nCores )
> }
> print('... Done')
>
> exonCountSet <- estimatelog2FoldChanges( exonCountSet )
> ----------------------
>
> Any idea what is happening here? Does it have something to do with exonIDs
> actually? Because I don't actually have proper intronIDs so what I am
> actually doing is using the genomic coordinates as the intronID. So an
> intronID will look like:
>
> chr1:861181-861301
>
> I wondering if this is causing a problem. Any help would be greatly
> appreciated. I am using DEXSeq 1.5.6 the version that I got from the svn
> repository so that I have access to the TRT functions.
>
> Fong
>
>
>
>>
>> On Wed, Feb 6, 2013 at 1:06 AM, Alejandro Reyes <alejandro.reyes@embl.de<mailto:
>> alejandro.reyes@embl.**de <alejandro.reyes@embl.de>>> wrote:
>>
>>     Dear Fong Chun Chan,
>>
>>     Recently I have tried this, and works nicely you just need to
>>     count the reads falling in the introns and them as "exonic bins"
>>     in DEXSeq.
>>
>>     However, I think is recommendable to have strand specific data for
>>     this, because sometimes intronic regions contain antisense
>>     transcripts that if they are differentially expressed between your
>>     conditions, they can look like intron retention differences in
>>     your transcripts. Also, when adding the introns as "exonic parts"
>>     in DEXSeq, the models become big and difficult to compute, so it
>>     is necessary to use DEXSeq in the TRT context
>>     (estimateDispersionsTRT and testForDEUTRT).
>>
>>     Best wishes,
>>     Alejandro Reyes
>>
>>
>>
>>         Hi,
>>
>>         I am interested in trying to detect the intron retention in
>>         RNA-Seq I was
>>         wondering if anyone how ever tried to apply DEXSeq to looking for
>>         differential intron usage between two groups of samples. It
>>         seems like an
>>         ideal software that could detect for this given that the
>>         features are just
>>         introns now instead of exons. Or does anyone else recommend
>>         any other
>>         Bioconductor package that looks for differential intron retention?
>>
>>         Thanks,
>>
>>         Fong
>>
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>>
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>>
>>
>

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