adding covariates

[jackhump]

Hi, this package looks like fun. I have 3 datasets where the same biological condition is compared to a set of controls. I was hoping to analyse the 3 datasets jointly adding a covariate for dataset. Is it possible to pass this to DEXSeq via the support file? So with DEXSeq I'd fit:

formulaFullModel = ~ sample + exon + dataset:exon + condition:exon
formulaReducedModel = ~ sample + exon + dataset:exon

What do you suggest?

[qqwang-berkeley]

Hi Jack, I just want to make sure that I understand your request correctly. Do you mean that you want to run JUM in a way that analyzes three datasets from one biological condition jointly? Or do you mean you want to run DEXSeq instead? Qingqing

…

On Sat, Oct 13, 2018 at 8:55 AM Jack Humphrey ***@***.***> wrote: Hi, this package looks like fun. I have 3 datasets where the same biological condition is compared to a set of controls. I was hoping to analyse the 3 datasets jointly adding a covariate for dataset. Is it possible to pass this to DEXSeq via the support file? So with DEXSeq I'd fit: ''' formulaFullModel = ~ sample + exon + dataset:exon + condition:exon formulaReducedModel = ~ sample + exon + dataset:exon ''' What do you suggest? — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#17>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AZPn2-wghXtr9okiaqq1H1OA3_tCKIJ8ks5ukgz1gaJpZM4XasUT> .

[jackhump]

Sorry Qingqing, I wasn't clear.
I was under the impression that JUM uses DEXSeq for the downstream statistical testing for differential junction usage?
If so, could I run JUM on all 3 datasets together to find a joint set of splicing events and then run the statistical testing part with a dataset-specific covariate?

[qqwang-berkeley]

Hi Jack, In step 2 (the Rscript step in the manual) JUM does utilize functions from the DEXSeq package to calculate differential junction usage in each AS structure. This step is then followed by two JUM-unique steps (JUM_B.sh and JUM_C.sh) that reconstitute the tissue-specific AS graph *ab initio*, assign splicing patterns for each AS event, perform rigorous intron retention analysis, and calculate deltaPSI values. If you have your own preference for running the statistical testing step, you can modify the R_script_JUM.R so that it fits your needs. Take a look at the R script. It should be straightforward. Let me know if you have any questions. Qingqing

…

On Tue, Oct 16, 2018 at 11:06 AM Jack Humphrey ***@***.***> wrote: Sorry Qingqing, I wasn't clear. I was under the impression that JUM uses DEXSeq for the downstream statistical testing for differential junction usage? If so, could I run JUM on all 3 datasets together to find a joint set of splicing events and then run the statistical testing part with a dataset-specific covariate? — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#17 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AZPn20Y2obySl-yz6BsMn82sop9J5Eclks5uliBCgaJpZM4XasUT> .