Dear Iain, I think you might be after an interaction plot, see ?interaction.plot I've never seen anyone do this for count data. However I guess you could make such a plot approximately in edgeR by computing lcpm <- log2(cpm(y)) Then using interaction.plot(x.factor, trace.factor, response=lcpm[i,]) where 'i' is the gene of interest. Best wishes Gordon > Date: Mon, 31 Oct 2011 10:26:13 +0000 (GMT) > From: Iain Gallagher <iaingallagher at="" btopenworld.com=""> > To: Bioconductor mailing list <bioconductor at="" r-project.org=""> > Subject: Re: [BioC] visualise model fit in edgeR > > Dear Gordon > > Thanks for your reply. There's nothing like someone else's question to > make one focus on what exactly one wants. This was certainly the case > here! > > I have given this some thought from my statisically naive > point of view and I have attached a mock-up picture of the kind of thing > I envisaged (although I appreciate the real life situation is more > complicated). > > The experimental design is as follows: > > Cells > were collected from 6 animals and infected with one of 4 strains of > bacteria or left uninfected. RNA was sampled at 2, 6, 24 & 48 hours > post infection. There are thus 120 data points across the whole > experiment. > > > I have used edgeR to analyse the infected v > control data at each timepoint using the GLM approach? - effectively a > paired samples analysis for each timepoint? as per the edgeR manual > (section 11). Perhaps there's something more sophisticated I could do > here though. If you had any advice that would be great! > > > design <- model.matrix(~ cow + infection) > #dispersion estimate > d <- estimateGLMCommonDisp(d, design) > #fit the NB GLM for each gene > fitFiltered <- glmFit(d, design, dispersion = d$common.dispersion) > #carry out the likliehood ratio test > lrtFiltered <- glmLRT(d, fitFiltered, coef = 7) > > For > my audience I simply wanted to illustrate the fitting of the two models > and how likelihood ratio tests are used rather than a t-test approach. > In the attached pdf each black line represents the H1 model (with > infection) and each red line represents the null model (cows only) for > one gene only. The points are the 'raw data' (but not real data); C = > control, I = infected. I realise this illustration is showing > essentially a linear fit but I'm trying to aim for simplicity for the > audience (a conceptual rather than entirely accurate approach). I would > be happy to get my hands dirty coding something more lifelike as I think > that would aid my understanding as well. > > > I was going to > describe this in terms of the 'fit' of each line to the data i.e. for > the regulated gene the black line is the more 'likely' model whereas in > the non-regulated gene there is little to separate the models. > > > Hope this is somewhat useful. > > Best > > Iain ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}