Hi James, Great, I am convinced. Thanks for the explanation, although my brain hurts following it.... Cheers, Milena James W. MacDonald wrote: > Hi Milena, > > Milena Gongora wrote: >> Hello James, >> >> Thanks a lot for your insight! I made that contrast matrix as you >> suggested and got the results fine. >> >> Can I add another question? >> >> You mention that in the result of topTable(fit2_paired_RMAbg_Qnorm), >> the baseline is OB1/SurfaceA and everything is compared to that... >> >> So are the results under coefficients SurfaceB and SurfaceC taking >> into consideration all patients? or just OB1? (I imagine is all >> patients, but just checking how it works) > > Yep. All patients. You can convince yourself of this by noting that > the design matrix has all of these rows: > > Int OB2 OB3 OB4 OB5 B C > 2 1 0 0 0 0 1 0 > 5 1 1 0 0 0 1 0 > 8 1 0 1 0 0 1 0 > 11 1 0 0 1 0 1 0 > 14 1 0 0 0 1 1 0 > > A simple way to think about this is to go back to first principles. > The design matrix is used to do the linear algebra required to > estimate these coefficients. Linear algebra is just a compact way to > solve for multiple unknowns (remember in algebra how you can solve for > x with one equation, but to solve for x and y you need two, and to > solve for x, y, and z you need three, and on and on?). > > So the above design matrix specifies the following equations: > > <val1> = (OB1 + A) + (B - A) > <val2> = (OB1 + A) + (OB2 - OB1) + (B - A) > <val3> = (OB1 + A) + (OB3 - OB1) + (B - A) > . > . > . > > Which we solve simultaneously to get the coefficients, based on all > the patients. > > Does that help? > > Best, > > Jim > > > >> >> Thanks again! >> >> Milena >> >> James W. MacDonald wrote: >> >>> Hi Milena, >>> >>> Milena Gongora wrote: >>> >>> >>>> Hello Everyone, >>>> >>>> I am wondering if anyone has scaled a paired t-test to do multiple >>>> pairwise comparisons and can enlighten me in how to interpret the >>>> outcome. I read the limma guide back and forth but seem to be >>>> missing on understanding a few things. >>>> >>>> Essentially I am doing a paired t-test, but have 3 treatments and >>>> wish to make pairwise comparisons of all combinations. >>>> >>>> I have single channel data (Illumina) that I imported using >>>> BeadExplorer, this creates an exprSet. Following that I >>>> RMA-bg-corrected and then normalized using Quantile normalization >>>> from the BeadExplorer package, which essentially invokes limma >>>> Quantile normalization. As a result of this I had an exprSet of >>>> normalized values which I then log2 transformed. >>>> >>>> So my experimental design is as follows, 5 patients that were >>>> biopsied (OB1 to OB5) and their biopsy split into 3 cultures of >>>> cells that underwent each a different treatment (surfaces A, B, C). >>>> Therefore I have 3 treatments, each with 5 replicates but they are >>>> of the same origin, which to my logic seems like I should analyse >>>> as paired samples. >>>> >>>> My challenge was to scale the paired t-test to 3 sets of comparisons. >>>> >>>> So first I read a targets file that specifies all the pairs and >>>> treatments >>>> >>>> > targets <- readTargets("samples.txt") >>>> > targets >>>> FileName Patient Surface >>>> 1 1519138023_A OB1 A >>>> 2 1488802050_A OB1 B >>>> 3 1488802050_D OB1 C >>>> 4 1519138023_B OB2 A >>>> 5 1488802050_B OB2 B >>>> 6 1488802050_E OB2 C >>>> 7 1519138023_C OB3 A >>>> 8 1488802050_C OB3 B >>>> 9 1488802050_F OB3 C >>>> 10 1519138023_D OB4 A >>>> 11 1519138023_E OB4 B >>>> 12 1519138023_F OB4 C >>>> 13 1519138034_A OB5 A >>>> 14 1519138034_B OB5 B >>>> 15 1519138034_C OB5 C >>>> >>>> Then make the design matrix >>>> > Patients <- factor(targets$Patient) >>>> > Surfaces <- factor(targets$Surface, levels=c("A", "B", "C") ) >>>> > paired_design <- model.matrix(~Patients+Surfaces) >>>> >>>> And then fit a linear model and do eBayes >>>> > fit_paired_RMAbg_Qnorm <- lmFit(data_log2_RMAbg_Qnorm, >>>> paired_design) >>>> > fit2_paired_RMAbg_Qnorm <- eBayes(fit_paired_RMAbg_Qnorm) >>>> >>>> > topTable(fit2_paired_RMAbg_Qnorm, number=2) >>>> ID X.Intercept. PatientsOB2 PatientsOB3 PatientsOB4 >>>> 13720 GI_34304116-S 15.29244 1.431159e-15 0.1152188 0.14177094 >>>> 11757 GI_31543813-S 15.14338 -1.090994e-01 0.1038085 0.08840763 >>>> >>>> PatientsOB5 SurfacesSLA SurfacesSLAa AveExpr F >>>> 13720 -0.03689951 0.006326441 0.01046853 15.34205 30967.96 >>>> 11757 0.01106040 0.080210742 -0.06714165 15.16657 29657.53 >>>> >>>> P.Value adj.P.Val >>>> 13720 1.549603e-24 1.816823e-20 >>>> 11757 2.007728e-24 1.816823e-20 >>>> >>>> >>>> My Questions are: >>>> I am a bit confused by the fact that in the resulting table (shown >>>> by topTable) I am getting a column for the intercept of surface A >>>> with all patients as well as other surfaces. What do the values >>>> under patients mean? Does the fact that they are being considered >>>> reduces the power of the comparison to the other surfaces? >>>> >>> >>> >>> For starters, you _have_ fit a paired design, and it is simple to >>> get your results out. Unfortunately it is difficult to explain this >>> via email (and if you were taking a linear modeling class there >>> would probably be several lectures devoted to design matrices, so it >>> isn't a trivial thing to learn). >>> >>> In short, the model you are fitting uses patient OB1/Surface A as a >>> baseline, to which all other samples are compared (looking at the >>> design matrix may help). The SurfacesB coefficient compares the B >>> and A surfaces (B-A), and the SurfacesC coefficient compares the C >>> and A surfaces (C-A). If you want the other comparison, you need to >>> set up a contrasts matrix like this: >>> >>> > matrix(c(rep(0,5), 1, -1), dimnames=list(colnames(paired_design), >>> "B-C")) >>> B-C >>> (Intercept) 0 >>> PatientsOB2 0 >>> PatientsOB3 0 >>> PatientsOB4 0 >>> PatientsOB5 0 >>> SurfacesB 1 >>> SurfacesC -1 >>> >>> Because this will compute (B-A) - (C-A) = B-C. >>> >>> So topTable(fit2_paired_RMAbg_Qnorm, coef=6) will give you the genes >>> different between A and B, coef=7 will give you the genes different >>> between A and C, and fitting the contrast will give you the genes >>> different between B and C. >>> >>> Best, >>> >>> Jim >>> >>> >>> >>> >>> >>> >>>> As I am not interested in the differential expression amongst >>>> patients, how do I avoid these being considered? >>>> >>>> How can I know about the differences amongst surfaces B and C? >>>> >>>> Do I need to or can I make a contrast matrix to specify which are >>>> the comparisons I want to get information for? (only surfaces, and >>>> not amongst patients) >>>> >>>> If I can make a contrast matrix, can you give me an example of how >>>> to do it with 3 treatments? >>>> >>>> Many Thanks! >>>> >>>> Milena >>>> >>>> _______________________________________________ >>>> Bioconductor mailing list >>>> Bioconductor at stat.math.ethz.ch >>>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>>> Search the archives: >>>> http://news.gmane.org/gmane.science.biology.informatics.conductor >>>> >>> >>> >>> >>> >> >> >> > > -- Milena Gongora Bioinformatician SRC Computational Chemistry and Biology Unit Institute for Molecular Biosciences The University of Queensland Phone: +61 7 3346 2609 Fax: +61 7 3346 2101 email: m.gongora at imb.uq.edu.au