Hi,

I was wondering which counts are appropriate to use for plotting the expression of the differential transcripts? `plotDEXSeq`

is giving me this error, even when using the pre-constructed matrix of counts from the workflow "**Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification**":

```
plotDEXSeq( dxr2, "ENSG00000000457.13", legend=FALSE, cex.axis=1.2, cex=1.3,
lwd=2 )
#legend is false because we have not imported everything from the txdf.
```

Error in plot.window(xlim = c(0, 1), ylim = c(0, max(matr))) :
need finite 'ylim' values
In addition: Warning messages:
1: In max(coeff, na.rm = TRUE) :
no non-missing arguments to max; returning -Inf
2: In max(matr) : no non-missing arguments to max; returning -Inf

I am interested in outputting all normalized transcripts that I found to be to differentially expressed.

Further, I have looked at the function `plotDEXseq`

and it uses `featureCounts`

, which is just like requesting in Deseq2, `counts(object, normalized = TRUE)`

of the specific groupID. I can't help but wondering if these counts are OK to graphically represent a *transcript* that was found to be differential expressed following `DEXseq`

and `StageR`

?

Thank You

Hi Mike,

I agree with you regarding the plotting function of DRIMSeq, especially the plotProportions function; however, when I try to run dmPercision on my entire dmDSdata and not a subset I receive the error below:

`d <- DRIMSeq::dmPrecision(d, design=design_full) ! Using a subset of 0.1 genes to estimate common precision ! Error in optimHess(par = par, fn = dm_lik_regG, gr = dm_score_regG, x = x, : non-finite value supplied by optim In addition: There were 50 or more warnings (use warnings() to see the first 50)`

My recommendation for now is to make simple plots on your own, and not to run DRIMSeq all over again if you've used DEXSeq.

OK. So that takes us back to my initial question: Which counts are appropriate to use for plotting the expression of the differential transcripts? Would this be correct to use, following DE of a transcript(s) after running DEXSeq and StageR

count <- featureCounts(dxd, normalized = TRUE)[idx, ]

I’d recommend using scaledTPM from tximport for the reasons described in the workflow.