Dear Gordon, thank you so much for your prompt and helpful answer. You're right I was thinking too complicated:-) Best, Daniel On 09.05.2014 06:12, Gordon K Smyth wrote: > Dear Daniel, > > I don't see any need for a gene-specific factor. > > Simply give all the count rows (for all genes and all splicing events) > to edgeR. The design matrix is: > > genotype <- factor(c("mutant1","mutant1","mutant2","mutant2","wt","wt")) > genotype <- relevel(genotype,ref="wt") > design <- model.matrix(~genotype) > > If you want to find differentially abundant events between the mutants > and wt, you can run glmLRT() with coef=2 to examine mutant1, coef=3 to > examine mutant2, and contrast=c(0,0.5,0.5) to average the two mutant lines. > > Best wishes > Gordon > > >> Date: Thu, 8 May 2014 00:33:05 -0700 (PDT) >> From: "Daniel Lang [guest]" <guest at="" bioconductor.org=""> >> To: bioconductor at r-project.org, daniel.lang at biologie.uni- freiburg.de >> Subject: [BioC] edgeR GLM using factor that varies for each gene >> >> Hi, >> >> after going over the user guide and searching this mailing list I'm >> not quite clear on how to best address my specific situation: >> >> I'd like to test differential "expression" of specific splicing events >> between a mutant and the wild type in a replicated design. To do so, >> I've specifically counted reads that are specific to a certain >> splicing event for each gene. >> >> e.g. >> event AS.type mutant.line1.rep1 mutant.line1.rep2 >> mutant.line2.rep1 mutant.line2.rep2 wt.rep1 wt.rep2 >> S102-F_10.883 alt_donor 4 7 4 7 0 1 >> S102-F_12.884 alt_donor 0 1 0 1 0 2 >> S102-F_10.887 alt_donor 0 0 0 0 30 33 >> S102-F_10.886 alt_acceptor 0 0 0 0 22 21 >> S102-F_11.890 alt_donor 0 0 0 0 0 0 >> S102-F_11.889 alt_acceptor 0 0 0 0 0 0 >> S102-F_10.891 alt_acceptor 0 0 0 0 0 0 >> S103-R_3.901 alt_acceptor 4 5 4 5 10 11 >> S103-R_2.904 skipped_exon 2 4 2 4 33 28 >> S103-R_2.902 alt_acceptor 4 5 4 5 0 0 >> S103-R_1.906 alt_acceptor 0 1 0 1 1 0 >> >> It's not clear from this example, but overall there is a difference >> between abundances and noise levels of specific types of alternative >> splicing I'd like to correct for, but also assess using GLM. Thus, >> ideally I'd like to find differentially abundant splicing events >> between the mutant and the wild type irrespective of line and >> biological replicate. >> >> As far as I understood the UserGuide and the ReferenceManual design >> always refers to factors for describing the libraries/experiments the >> counts are derived from. >> >> If I'd be using "normal" GLM, what I want to do would look like >> glm(count ~ AS.type + genotype + line + biological.replicate). >> >> Can I accomplish this with edgeR without splitting up the events into >> different data sets per splice type? >> >> Any advise on this would be greatly appreciated. >> >> Best, >> Daniel >> >> -- output of sessionInfo(): >> >> R version 3.0.1 (2013-05-16) >> Platform: x86_64-pc-linux-gnu (64-bit) >> >> locale: >> [1] LC_CTYPE=en_US.utf8 LC_NUMERIC=C >> [3] LC_TIME=de_DE.utf8 LC_COLLATE=en_US.utf8 >> [5] LC_MONETARY=de_DE.utf8 LC_MESSAGES=en_US.utf8 >> [7] LC_PAPER=C LC_NAME=C >> [9] LC_ADDRESS=C LC_TELEPHONE=C >> [11] LC_MEASUREMENT=de_DE.utf8 LC_IDENTIFICATION=C >> >> attached base packages: >> [1] stats graphics grDevices utils datasets methods base > > ______________________________________________________________________ > The information in this email is confidential and inte...{{dropped:23}}