Dear Steve,

> all(!is.na(subset(fData(exonCounts), testable)$pvalue))
[1] TRUE

> all(is.na(fData(exonCounts)$pvalue) == is.na(fData(exonCounts)$padjust))
[1] TRUE

Do you see large fold changes that are getting small pvalues that look like
they should be "real"?
No I don't

Thanks

About the point 3, I am not quite sure if I understand well. Anyway, I
asked to a collegue and he said that

On 5 February 2013 14:12, Steve Lianoglou <mailinglist.honeypot@gmail.com>wrote:

> Hi,
>
> Your code seems correct -- although I think the vignette has you you
> estimate log2foldChanges  after differential exon usage testing, but
> that shouldn't matter.
>
> It also looks as if you have plenty of testable exons (almost 200k),
> so the question is:
>
> (1) For every exon that is testable, do you get a value in the
> `pvalue` column for it? That is to say, the only NA's in the pvalue
> column should be for rows that are not `testable`, eg:
>
> R> all(!is.na(subset(fData(exonCounts), testable)$pvalue))
>
> (2) Are the NA's in your padjust column only present because the
> pvalue column is also NA?
>
> R> all(is.na(fData(exonCounts)$pvalue) == is.na
> (fData(exonCounts)$padjust))
>
> (3) If 1 and 2 are kosher -- we might be running out of things to
> debug, and perhaps the high pvalues are what we're stuck with here.
> What if you plot the log2fold(what/ever) aginst the -log10(pvalue)? Do
> you see large fold changes that are getting small pvalues that look
> like they should be "real"? That is to say, large fold changes might
> come from a region of extreme low expression (although the vst xform I
> think does try to mitigate that), so while the fold change is "high",
> the pvalue won't be.
>
> HTH,
> -steve
>
> On Tue, Feb 5, 2013 at 6:50 AM, Vincenzo Capece <vivo0304@gmail.com>
> wrote:
> > Thanks a lot Steve.
> > This is the last part of my pipeline(the first part is just the
> > concatenation of strings to build the paths to input files, but they are
> > right):
> >
> >> samples
> >       condition replicate
> > 28M_1        28         1
> > 28M_2        28         2
> > 28M_3        28         3
> > 3M_1          3         1
> > 3M_2          3         2
> > 3M_3          3         3
> >
> > exonCounts=read.HTSeqCounts(countfiles = fullFileNames,design =
> > samples,flattenedfile = annotationFile)
> >
> > exonCounts=estimateSizeFactors(exonCounts)
> >
> > #sizeFactors(exonCounts)
> >
> > exonCounts=estimateDispersions(exonCounts,nCores=32)
> >
> > exonCounts=fitDispersionFunction(exonCounts)
> >
> > exonCounts=estimatelog2FoldChanges(exonCounts,nCores=32)
> >
> > exonCounts=testForDEU(exonCounts,nCores=32)
> >
> > res=DEUresultTable(exonCounts)
> >
> > DEXSeqHTML(exonCounts, path=pathToExonFiles ,color = c("#DDDDDD",
> > "#CCFFFF"))
> >
> > plotOut=paste(pathToExonFiles,"res.csv",sep="/")
> > write.csv(res,file=plotOut)
> >
> > This is the command that you suggested me to use:
> >
> >> table(fData(exonCounts)$testable)
> >
> >  FALSE   TRUE
> > 151884 199146
> >
> > If you think that my pipeline is right, I can send you the file
> "res.csv" in
> > private way.
> >
> > Thanks a lot
> >
> >
> > On 5 February 2013 12:17, Steve Lianoglou <
> mailinglist.honeypot@gmail.com>
> > wrote:
> >>
> >> Hi Vincenzo,
> >>
> >> On Tue, Feb 5, 2013 at 5:11 AM, Vincenzo Capece <vivo0304@gmail.com>
> >> wrote:
> >> > Dear bioconductorers,
> >> > I am using DEXseq for the splicing analysis.
> >> > The pipeline works perfectly, but I have some doubts about the output:
> >> > on
> >> > 351000 exons I have just 10 padjust significant ones.
> >> > Googling I found this thread, in which the user developed my same
> >> > pipeline
> >> > and in which she has the same problem:
> >> > https://stat.ethz.ch/pipermail/bioconductor/2012-February/043476.html
> >> > This thread finished in a "private" way, but I need to know if there
> are
> >> > any bugs(I think fixed, because It past one year);
> >>
> >> The thread actually finished off "publicly." Perhaps you thing it went
> >> private because you see messages like "embedded and
> >> charset-unspecified text was scrubbed" in the emails of the thread
> >> that follow the one you posted?
> >>
> >> If you click on the URL below, it's there:
> >>
> >>
> https://stat.ethz.ch/pipermail/bioconductor/attachments/20120213/5f13a499/attachment.pl
> >>
> >> That having been said, it's hard to say what (if anything) is going
> >> wrong with your analysis without you posting any source code, but you
> >> can calculate the adjust pvalues yourself.
> >>
> >> After running the testForDEU step, the fData(ecs)$pvalue and
> >> fData(ecs)$padjust columns should be filled. One could calculate the
> >> padjust column manually by doing:
> >>
> >> adjusted <- p.adjust(fData(ecs)$pvalue, 'BH')
> >>
> >> but if the values in the pvalue column are NA, so too will be the
> >> adjust pvalues.
> >>
> >> If you have many NA pvalues after your testForDEU, then there might be
> >> several reasons why they are there:
> >>
> >> (1) These exons have been marked as "untestable" (fData(ecs)$testable)
> >> during the estimateDispersions step, which might be due to:
> >>   (a) read counts for the exon below the "minCount" required per exon
> >>   (b) the exon is in a gene with more than "maxExon" exons
> >>   (c) the gene only has one exon?
> >> (2) The calculation of the pvalue errored and didn't return anything
> >> -- not sure why this would happen so frequently, though.
> >> (3) ... ? ...
> >>
> >> > moreover I am interested
> >> > knowing if the problem was at "user level" or "source code level" and,
> >> > in
> >> > the first case, how she fixed the bug.
> >>
> >> I'm not sure if this can be answered without your data, but you will
> >> first need to post the code yo used.
> >>
> >> As for the original poster that you mentioned -- you'll see how her
> >> problem was fixed when you click on the URL's in the messages that
> >> look "hidden" from view.
> >>
> >> > Thanks in advance and congratulation for your package: it is awesome.
> >>
> >> Hear, hear!
> >>
> >> HTH,
> >> -steve
> >>
> >> --
> >> Steve Lianoglou
> >> Graduate Student: Computational Systems Biology
> >>  | Memorial Sloan-Kettering Cancer Center
> >>  | Weill Medical College of Cornell University
> >> Contact Info: http://cbio.mskcc.org/~lianos/contact
> >
> >
> >
> >
> > --
> > Regards,
> >             Capece Vincenzo
>
>
>
> --
> Steve Lianoglou
> Graduate Student: Computational Systems Biology
>  | Memorial Sloan-Kettering Cancer Center
>  | Weill Medical College of Cornell University
> Contact Info: http://cbio.mskcc.org/~lianos/contact
>



-- 
Regards,
            Capece Vincenzo

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