Yes, exactly. See the section 'Multi-factor designs' in the vignette of
DESeq or DESeq2.

Note that for DESeq2, we recommend to put the variable of interest, say
'condition' taking values {normal, tumor} at the end of the design formula,
because the results() and plotMA() functions by default will pull from the
last variable of the design formula:

~ patient + condition

Mike




On Fri, Aug 30, 2013 at 8:39 AM, Devon Ryan <dpryan@dpryan.com> wrote:

> Hi Prabhakar,
>
> The general method you're looking for is to add "patient" (or whatever
> term you prefer) as a factor in your design. So, if you denote tumor/normal
> by a factor called "status":
>
> counts ~ status + patient
>
> would likely be the formula you want. You could also allow an interaction
> if it makes sense for your dataset, by swapping an asterisk for the plus
> sign. You'll need to specify the individual patients in your dataframe, of
> course.
>
> Best,
> Devon
>
> ____________________________________________
> Devon Ryan, Ph.D.
> Email: dpryan@dpryan.com
> Molecular and Cellular Cognition Lab
> German Centre for Neurodegenerative Diseases (DZNE)
> Ludwig-Erhard-Allee 2
> 53175 Bonn, Germany
>
> On Aug 30, 2013, at 12:12 AM, Prabhakar Chalise wrote:
>
> > Hi,
> >
> > I was looking for a method to identify differentially expressed genes
> using the paired tumor/normal samples. Is there a method under DEseq to
> test for such correlated data?
> >
> > Thanks,
> > -Prabhakar Chalise
> >
> >       [[alternative HTML version deleted]]
> >
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