I have three different treatment times (0h (untreated), 3h and 6h), and three samples for each time point, 9 samples in total. I want to perform a single test to identify exons where the level of expression is dependent of the 'dose' of time; 0h<3h<6h OR 0h>3h>6h .

I have read the vignette, and know I have to set up the reducedModel and fullModel:

fullModel = ~ sample + exon + time:exon

reducedModel = ~ sample + exon

I am not sure I have this right.  Would someone be able to suggest these model arguments please? I have no additional technical or biological covariates I need to correct for.

Hi MedwayC,

The models seem correct. From the question you want to answer, it seems more appropriate to treat the time variable as a quantitative variable instead of a categorical (qualitative) one. You would just need to specify the time variable in your colData object as a numerical value and the models can remain the same.

As for the effect sizes (estimateExonFoldChanges), the current implementation of this function can't handle numerical variables, but  you could specify the categorical variables and maybe use as denominator the 0h time point. 

Alejandro