Hi Zhihao, On 1/29/2014 12:07 PM, Zhihao Tan wrote: > Hi Jim, > > Thanks for your reply. Alright, I'm guessing that contrast really > doesn't make sense, and actually going back to look at the additional > genes that the (Day2.Fluffy - Day2.Smooth) + (Day5.Fluffy - > Day5.Smooth) brought up, it seems like it might mostly be noise, or > day specific effects. > > (Day2.Fluffy - Day2.Smooth) - (Day5.Fluffy - Day5.Smooth) would indeed > be an interesting set of genes to look at, and I will definitely try that. > > I do want to try and understand the best (whatever best means...) way > to get at the genes that are DE between fluffy and smooth though. I > can briefly think of three ways: > - Lump all the samples, build a model with 1 effect (Phenotype), and > get a set of DE genes (Fluffy - Smooth). But that seems to be > averaging out the values, and not supplying information to the model > about a source of biological variation (Time) that we know about. > - Lump all the samples, build a model with 2 effects (Phenotype and > Time), test (Day2.Fluffy - Day2.Smooth) and (Day5.Fluffy - > Day5.Smooth), get the intersection of the DE genes. This should > account for biological variation, though normalization of Day2 and > Day5 samples together would add a little noise? > - Separate samples by day, test (Fluffy - Smooth) for each, get the > intersect. And only when I want to test for interaction between > effects do I build the 2 effect interaction model. This to me seems > the cleanest, but I'm not sure if that makes sense in the world of > biostatistics... The conventional way to do this would be to fit a model like design <- model.matrix(~phenotype*time) where phenotype is a factor with the two levels (fluffy and smooth) and time is a factor with the two levels (2 and 5). This will result in a design matrix like this: > model.matrix(~phenotype*time) (Intercept) phenotypeSmooth time5 phenotypeSmooth:time5 1 1 0 0 0 2 1 0 0 0 Where phenotypeSmooth is inherently a contrast comparing Smooth - Fluffy after controlling for time, and time5 is a contrast of day5 - day2 after controlling for phenotype, and phenotypeSmooth:time5 tests the interaction. Note that the second and third coefficient are not interpretable for any genes that have a significant interaction, so you should first look for genes with an interaction, and then look for genes that are different in Smooth - Fluffy only in the set of genes that do not have a significant interaction term. Does that make sense? Best, Jim > > Hope to get your advice on this... and thanks once again! > > Cheers, > Zhihao > > > > > On Wed, Jan 29, 2014 at 6:17 AM, James W. MacDonald <jmacdon at="" uw.edu=""> <mailto:jmacdon at="" uw.edu="">> wrote: > > Hi Zhihao, > > > On Tuesday, January 28, 2014 6:56:20 PM, Zhihao Tan wrote: > > Hi there, > > I have a question on whether some of the contrasts I am making > in a > multifactorial experiment should actually be made. I don't > have a strong > grasp of GLMs, so I might be missing something conceptually, > and am hoping > someone can advise. > > I am basically looking for genes that are differentially > expressed in a > certain phenotypic state (e.g. fluffy vs. smooth), but have > set it up with > 2 time-points (Day 2 and Day 5). I have trouble setting up the > design using > an equation (columns seem to disappear) so have gone ahead and > created the > design matrix using the method in 3.3.1 of the manual (pasting > factors > together). The design looks like this (I have removed > replicates and many > samples to simplify): > > Day2.Fluffy Day2.Smooth Day5.Fluffy Day5.Smooth > 1 0 1 0 0 > 7 1 0 0 0 > 13 0 0 0 1 > 16 0 0 1 0 > 19 0 0 0 1 > 35 0 0 1 0 > 36 0 0 1 0 > > >From what I understand, the above design is set up for 2 main > effects > (phenotype and time), and if I reduce it to 1 main effect > (phenotype), I > get the design below. > > Fluffy Smooth > 1 0 1 > 7 1 0 > 13 0 1 > 16 1 0 > 19 0 1 > 35 1 0 > 36 1 0 > > The contrast I make in the latter case is basically (Fluffy - > Smooth). The > contrast that I did for the former case, and this is what I'm > unsure of, is > ((Day2.Fluffy - Day2.Smooth) + (Day5.Fluffy - Day5.Smooth)). > These tests > are definitely not equivalent, and I get different number of > sig. DE genes > for both (more for the 2 effect design). In my mind, it makes > sense, > because the experiment *is *set up with 2 effects, and > accounting for the > > biological variation in your model should allow you to be more > powered to > detect DE genes. However, I've never seen a contrast like that > before. Does > it even make sense to have an addition sign in the equation? > What does that > actually mean? Should I instead make contrasts of (Day2.Fluffy - > Day2.Smooth) and (Day5.Fluffy - Day5.Smooth) and get the union > or intersect > of them? > > > The contrast you are using doesn't really make sense, because a > contrast is usually testing the difference between groups, so you > subtract rather than sum. If you were to use > > (Day2.Fluffy - Day2.Smooth) - (Day5.Fluffy - Day5.Smooth) > > then you would be testing the interaction of time and phenotype. > In other words the interaction looks for genes that are different > between fluffy and smooth, depending on the day. So if you think > the fluffiness of your samples is dependent on time, that is what > you would likely want to test. > > Best, > > Jim > > > > Hope someone can help on this, and thanks in advance! > > Regards, > Zhihao > Graduate Student > University of Washington > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org <mailto:bioconductor at="" r-project.org=""> > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > > > -- > James W. MacDonald, M.S. > Biostatistician > University of Washington > Environmental and Occupational Health Sciences > 4225 Roosevelt Way NE, # 100 > Seattle WA 98105-6099 > > -- James W. MacDonald, M.S. Biostatistician University of Washington Environmental and Occupational Health Sciences 4225 Roosevelt Way NE, # 100 Seattle WA 98105-6099