Hi Alejandro, Thank you for the quick reply. I've already done what you've suggested but thought that there might be a way of using the experimental condition "total" as a mask (control). Meaning that the DEXSeq output (hits) would be the exons that show changes upon knockdown *only* in the fraction. Since that is not possible I'll have to stick with the direct comparison of FC. Cheers, Ant?nio On 04.07.2013 11:34, Alejandro Reyes wrote: > Dear Antonio Domingues, > > It is not possible to modify the DEXSeq formulas in order to test for > 'not changes in exon usage'. An option would be to subset your > ExonCountSet object leaving only the subcellular fractions or the > totals and do the vanilla DEXSeq analysis in both subsets separately. > Afterwards you could compare the results by plotting the fold changes > and try to identify abrupt changes in the knockdown effect differences > between the cellular fractions and in the total. > > Alejandro > >> ** I have sent this message before, but somethign must have gone wrong >> because it seems like it never reached the mailing-list. If it did and >> did his a duplicate, my apologies ** >> >> Dear Bioconductors, >> >> I would like to ask for some advice/suggestions on the set-up of DEXSeq >> with multiple condictions. At the moment, I am using DEXSeq in a >> "vanilla" fashion: >> - 2 conditions, knockdown and control >> - 2 biological replicates per condition >> - output exons that change upon knockdown. >> >> So far this is working fine. But I also have another experimental >> variable: sub-cellular fractions (total vs fraction). The goal is obtain >> exons whose expression is changed in the knockdown but only in the >> fraction, that is a combined effect of knockdown and sub-cellular >> localization. Following the vignette, I was thinking of an experimental >> design like this: >> condition type >> sample1_a control total >> sample1_b control total >> sample2_a knockdown total >> sample2_b knockdown total >> sample3_a control fraction >> sample3_b control fraction >> sample4_a knockdown fraction >> sample4_b knockdown fraction >> >> and the code would be: >> formuladispersion <- count ~ sample + ( condition + type ) * exon >> ecs <- estimateDispersions( ecs, formula = formuladispersion ) >> ecs <- fitDispersionFunction(ecs) >> formula0 <- count ~ sample + type * exon + condition >> formula1 <- count ~ sample + type * exon + condition * I(exon == exonID) >> ecs <- testForDEU( ecs, formula0=formula0, formula1=formula1 ) >> res2 <- DEUresultTable( ecs ) >> >> would this work and is this design correct? >> >> Thank you, >> Ant?nio >> >> >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at r-project.org >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor >> > -- -- Ant?nio Miguel de Jesus Domingues, PhD Neugebauer group Max Planck Institute of Molecular Cell Biology and Genetics, Dresden Pfotenhauerstrasse 108 01307 Dresden Germany e-mail: domingue at mpi-cbg.de tel. +49 351 210 2481 The Unbearable Lightness of Molecular Biology