I am using DESeq to determine DE of an RNA-Seq project that has
multiple
genotypes
exposed to different treatments. I would like to use the GLM
functionality to partition
the variance in the count data between genotype and treatment.
However,
when I follow
the suggestions of Simon (Bioconductor Digest, Vol 96, Issue 9),
neither
model
(fit0 or fit1) is reaching convergence, and I was wondering if it was
possible for me to
increase the number of iterations to see if this would help out.
Otherwise I would appreciate
any advice on how to proceed.
Many thanks for your time,
Seanna
--
Seanna McTaggart
Centre for Immunity, Infection and Evolution
School of Biological Sciences
Ashworth Laboratories
University of Edinburgh
Edinburgh EH9 3JT
Scotland, UK
Tel +44 131 650 8682
Fax +44 131 650 6564
E-mail: smctagga at staffmail.ed.ac.uk
<http: www.biology.ed.ac.uk="" research="" groups="" tlittle="" people.html="">
<http: ciie.bio.ed.ac.uk="" centre="">
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.
Hi Seanna
On 03/08/2011 05:13 PM, Seanna McTaggart wrote:
> I am using DESeq to determine DE of an RNA-Seq project that has
> multiple genotypes exposed to different treatments. I would like to
> use the GLM functionality to partition the variance in the count
data
> between genotype and treatment. However, when I follow the
> suggestions of Simon (Bioconductor Digest, Vol 96, Issue 9), neither
> model (fit0 or fit1) is reaching convergence, and I was wondering if
> it was possible for me to increase the number of iterations to see
if
> this would help out. Otherwise I would appreciate any advice on how
> to proceed.
I've just added a new argument to the function 'nbinomTestGLM', called
'glmControl', which is a list of GLM control parameters as described
in
the 'glm.control' help page.
So, you could try something like
fit0 <- nbinomTestGLM( cds, count ~ whatever,
glmControl = list( maxit=75 ) )
This should increase the maximum number of iterations from 25 to 75.
However, it is well possible that this does not help much. If your
model
fails to converge for any gene, it might be a deeper problem, and if
it
fails to converge only for a few genes, these might be somehow
"stubborn" and won't converge at all, no matter how many iterations
you
wait. If necessary, ask again with more details.
Simon
