Dear Hayssam,

Thanks for your reply. I have used trinity and following the instructions
on their website:
1- I generated a matrix of counts from all 12 samples.
2- "Using the counts.matrix file created above, perform TMM normalization
and generate the FPKM values per transcript and sample as follows:

$TRINITY_HOME/Analysis/DifferentialExpression/run_TMM_normalization_write_FPKM_matrix.pl
--matrix counts.matrix --transcript_lengths feature_lengths.txt" (from
Trinity website).

3- Then I followed the codes from edgeR manual and ended up in high values
which I had posted.

BUT I also tried the original count.matrix (raw counts data) without
correction by using aforesaid script and received the same dispersion and
BCV values.
Here is the header of my count.matrix:
        ERR162262       ERR162225       ERR162226       ERR162215
ERR162243       ERR162235       ERR162224       ERR162219       ERR162218
    ERR1
62266       ERR162239       ERR162263
Contig8320 43.00 71.00 44.21 39.00 35.00 25.00 18.00 19.92 28.00 28.00 7.00
37.00
comp28560_c2_seq1-len=504       239.00  231.00  239.00  214.00  223.00
 155.00  211.00  203.00  212.00  225.00  11.00   294.00
comp36723_c0_seq1-len=635       83.67   79.02   38.28   52.13   72.07
27.00   46.88   55.23   51.12   46.00   24.50   63.12
comp24326_c0_seq2-len=1093      18.00   16.00   9.00    23.00   30.00
18.00   30.00   28.00   12.00   17.00   70.00   42.00

You also asked about the warnings:
Warning messages:
1: In dnbinom(y, size = 1/dispersion, mu = mu, log = TRUE) :
  non-integer x = 0.940000
2: In dnbinom(y, size = 1/dispersion, mu = mu, log = TRUE) :
  non-integer x = 0.500000
3: In dnbinom(y, size = 1/dispersion, mu = mu, log = TRUE) :
  non-integer x = 0.010000
4: In dnbinom(y, size = 1/dispersion, mu = mu, log = TRUE) :
  non-integer x = 0.410000
5: In dnbinom(y, size = 1/dispersion, mu = mu, log = TRUE) :
  non-integer x = 1.740000

I appreciate your help,
Regards,
Nima

On Tue, Feb 26, 2013 at 11:01 PM, Hayssam Soueidan <h.soueidan@nki.nl>wrote:

> Hi Nima,
>
>
> I never had such high value for the BCV. In my analysis (mouse and human
> RNA-Seq), the BCV is usually way below 1. From the name of your data file,
> it looks like you have normalized FPKM data. EdgeR expect raw counts data
> (integers). That might be causing problems. Could you provide a head of
> your data.TMM data.frame?
> Further could you 1) provide a session.info and 2) provide some of the
> warnings?
>
> Regards,
> Sam.
>
> On Feb 26, 2013, at 4:39 PM, Nima Rafati <nimarafati@gmail.com> wrote:
>
> > Dear all,
> >
> > I have RNA-seq libraries of 12 individuals in two groups (6 replicates
> > each). I would like to do differential expression analyses using a GLM
> with
> > effect of group and sex on the transcripts. I followed the manual and in
> > last step for calculation of dispersion (estimateGLMCommonDisp)  I
> received
> > a high value with a warning. Here comes all commands that I have used:
> >
> >
> data.TMM<-read.table("Mod-H-transcripts.ount.matrix.TMM_normalized.FPKM",row.names=1,header=T)
> > sex<-factor(c("M","F","M","F","M","F","M","F","M","F","F","M"))
> > grp<-factor(c("W","W","W","W","W","W","D","D","D","D","D","D"))
> > y.TMM<-DGEList(count=data.TMM.new,group=group.D.W)
> > data.frame(Sample=colnames(y.TMM),grp,sex)
> > design<-model.matrix(~grp+sex)
> > rownames(design)<-colnames(y.TMM)
> > y.TMM <- estimateGLMCommonDisp(y.TMM, design, verbose=TRUE)
> >
> > Disp = 3.99994 , BCV = 2
> > There were 50 or more warnings (use warnings() to see the first 50)
> >
> > Despite of error, is the generated dispersion reliable? can I continue
> with
> > analyses?
> > Best regards,
> > Nima
> >
> >       [[alternative HTML version deleted]]
> >
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>

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