Question: DEXSeq and DRIMSeq on paired samples?
gravatar for zoe.ward
23 days ago by
zoe.ward0 wrote:

I have 85 paired samples i.e. before and after ischemia so my question is how would I run the DEXSeq and DRIMSeq on paired samples? I’ve had a look in the relevant vignettes but cannot find any example of paired analysis.

I have a design matrix like so:

sample_id cond pair
100V_post post 1
100V_pre pre 1
102V_post post 2
102V_pre pre 2
103V_post post 3
103V_pre pre 3
104V_post post 4
104V_pre pre 4
105V_post post 5
105V_pre pre 5



ADD COMMENTlink modified 23 days ago by gosia.nowicka30 • written 23 days ago by zoe.ward0

Dear Zoe,

One note about the rnaseqDTU workflow is that I initially made an error and included nbinomLRT() in the DEXSeq code. This should be testForDEU(). The workflow code on Bioconductor and on F1000Research has now been fixed to show testForDEU().

ADD REPLYlink written 16 days ago by Michael Love19k
gravatar for Alejandro Reyes
23 days ago by
Alejandro Reyes1.6k
Dana-Farber Cancer Institute, Boston, USA
Alejandro Reyes1.6k wrote:

Hi zoe.ward, 

For DEXSeq's vignette, this is documented in the section "4 Additional technical or experimental variables". Please let us know if something is not clear. Shortly, you need to pass the following formulae to both estimateDispersions and testForDEU:

formulaFullModel = ~ sample + exon + pair:exon + cond:exon
formulaReducedModel = ~ sample + exon + pair:exon


ADD COMMENTlink modified 23 days ago • written 23 days ago by Alejandro Reyes1.6k
gravatar for gosia.nowicka
23 days ago by
gosia.nowicka30 wrote:

In DRIMSeq, based on the design matrix, you would set the full model as

design_full <- model.matrix(~ cond + pair, data = samples(d))

and use it in dmPrecision() and dmFit().

In dmTest(), you can specify which coefficient you would like to test with coef = "condpre" or coef = "condpost", depending on which of the two conditions is as a reference. Or you could define the null model design matrix which would be

design_null <- model.matrix(~ pair, data = samples(d))

This is similar to the case with batches presented in the DRIMSeq vignette section 5.3 Differential transcript usage analysis between two conditions with accounting for the batch effects. In your case, patients are batches sort of.

ADD COMMENTlink written 23 days ago by gosia.nowicka30
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