Hi Gordon,
I wasn't able to get your suggestion to work. estimateGLMCommonDisp() seems
to want explicit values for the design. If I leave the design argument empty
I get the error,
Error in as.matrix(design) :
argument "design" is missing, with no default
I have release 2.8 installed. My code is
y <- DGEList( countMat )
y$offset <- log( totals )
y <- estimateGLMCommonDisp( y , offset = y$offset )
Sorry if I'm missing something obvious.
Thanks,
Sean
On Fri, Jul 15, 2011 at 7:26 PM, Gordon K Smyth <sm...@wehi.edu.au> wrote:
> Hi Sean,
>
> On Fri, 15 Jul 2011, Sean Ruddy wrote:
>
> Hi Gordon,
>>
>> Thanks for the response. One of my data sets has 8 conditions and no
>> replicates and so I wanted to emulate DESeq's way of pooling the samples and
>> also use an offset matrix. I was hoping to avoid doing it manually so that I
>> don't mess it up. I could do this all in edgeR and pool the samples but I'm
>> not sure how well this would work under edgeR vs. DESeq.
>>
>
> edgeR has a very flexible interface, so there was no need to explicitly
> introduce a "pooled" method. Instead, this sort of thing can be handled by
> the usual functions in the usual way. Suppose you have a data object y,
> which includes an offset matrix:
>
> y$offset <- your matrix
>
> Then you can estimate the "pooled" dispersion simply by:
>
> y <- estimateGLMCommonDisp(y)
>
> The fact that you don't supply a design matrix means that the samples are
> automatically treated as one group, i.e., pooled. You can estimate a
> trended or tagwise dispersions in the same way. Then
>
> fit <- glmFit(y,design) etc
>
> will do any analysis you want using dispersions estimated when the samples
> were pooled.
>
> I and the other edgeR authors are anxious to get feedback, so write again
> if this doesn't turn out to be clear.
>
> I am curious though what sounds off to you in my previous email. I don't
>> feel entirely comfortable doing this manually but hopefully it's just
>> because I left out some details. I was trying to follow the DESeq method and
>> the only difference I saw was in the size factor calculations which I
>> changed for my own needs by using the offset values for each tag and sample.
>>
>
> Even if you could estimate the variances yourself, I don't see any manual
> way that you could perform valid statistical tests, while correctly
> accounting for the offsets. The whole negative binomial methodology
> requires genuine counts rather than adjusted counts. So handling the
> offsets needs to be built-in.
>
> Best wishes
> Gordon
>
> I appreciate the help!
>>
>> Best,
>> Sean
>>
>> On Fri, Jul 15, 2011 at 12:02 AM, Gordon K Smyth <sm...@wehi.edu.au>
>> wrote:
>>
>> Hi Sean,
>>>
>>> I'm curious to know why not use edgeR, since edgeR does what you want and
>>> DESeq doesn't?
>>>
>>> I might be wrong, but the manual analysis that you describe doesn't sound
>>> right.
>>>
>>> Best wishes
>>> Gordon
>>>
>>> Date: Thu, 14 Jul 2011 12:54:49 -0700
>>>
>>>> From: Sean Ruddy <srudd...@gmail.com>
>>>> To: bioc-sig-sequencing@r-project.****org<bioc-sig-sequencing@r-**
>>>> project.org <bioc-sig-sequencing@r-project.org>>
>>>> Subject: [Bioc-sig-seq] Supplying own variance functions and adjusted
>>>> counts to a DESeq dataset
>>>>
>>>> Hi,
>>>>
>>>> I have a RNA-Seq count data set that requires separate offset values for
>>>> each tag and sample. DESeq does not appear to take a matrix of offset
>>>> values
>>>> (unlike edgeR) in any of its functions so I've carried out the analysis
>>>> manually, ie. calculating a size factor for each tag of each sample,
>>>> adjusting the counts, then proceeding to calculate means and variances of
>>>> the adjusted counts, and finally fitting a curve for each condition to the
>>>> mean-var plot using locfit().
>>>>
>>>> Essentially, I'd like to put these variance functions (or at least all
>>>> the predicted variances) and adjusted counts inside a DESeq object so that
>>>> I
>>>> can take advantage of the other functions DESeq offers, tests, plots,
>>>> etc...
>>>>
>>>> Thanks for the help!
>>>>
>>>> Sean
>>>>
>>>
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