Hello, I'm new to limma and have been plowing through the excellent users guide, where I've reached a sticking point. Would someone have the saintly patience to explain to a small mind how the design matrix for the direct design example works? I think I'm missing something quite fundamental in that I was under the impression that the numbers 1 and -1 represented the red and green dyes, respectively, but if that is true, I have no idea what these numbers mean in columns headed "CD8-CD4" and "DN-CD4". In fact, I don't really understand what these "subtracted" column headers mean at all, either in the design matrix or the contrast matrix. I'm planning a loop design experiment and this appears to be an essential point to grasp. Thanks in advance of your answer. Matt Scholz Research Specialist Department of Plant Science University of Arizona scholz at ag.arizona.edu --------------------------------------------- College of Agriculture and Life Sciences Web Mail. http://ag.arizona.edu

Hello, This short debate encouraged me to ask another question regarding direct microarray design and limma's design matrix. We compare response of plants to viral infection an several varieties of the sam plant species (potato). We don't use a common reference but we hybridise on each array infected (V) and healthy plant (M - mock infected) - for lets say 2 cultivars ( indexes "s", "i"). There are 3 biological repetitions including dyswaps - as seen in the targets file (below). My question is if the design matrix we created (down below) is OK? In the "CD8 vs CD4 and DN vs CD4" case described in Limma User's Guide and discussed below the CD4 was used as a virtual common reference. Do you have any suggestions what could be used as a "virtual common reference" in our case or it doesn't matter what we choose for it? Could we also include additional microarrays with the same experimental layout but done on two additional varietis of the plants (lets say indexes "c" and "n")in the experiment? And would then the logics of the design matrix construction still stay the same - see below (pick one virtual common reference)? Or would it be better if we used ANOVA to compare varieties? > targets<-readTargets("C:/Uporabniki/RRR/phenoDataIS.txt") > targets fileName slide.number variety Cy5 Cy3 1 I_30m_1_1042.txt 12901042 I Mi Vi 2 I_30m_2_1109.txt 12901109 I Vi Mi 3 I_30m_3_1040.txt 12901040 I Mi Vi 4 S_30m_1_1103.txt 12901103 S Ms Vs 5 S_30m_2_1039.txt 12901039 S Vs Ms 6 S_30m_3_1106.txt 12901106 S Vs Ms > design <- modelMatrix(targets, ref="Mi") Found unique target names: Mi Ms Vi Vs > design Ms Vi Vs 1 0 -1 0 2 0 1 0 3 0 -1 0 4 1 0 -1 5 -1 0 1 6 -1 0 1 > And now for the four varieties (reference would be "Mi"): > targets<-readTargets("C:/Uporabniki/RRR/phenoDataISCN.txt") > design <- modelMatrix(targets, ref="Mi") Found unique target names: Mc Mi Mn Ms Vc Vi Vn Vs > design Mc Mn Ms Vc Vi Vn Vs 1 0 0 0 0 -1 0 0 2 0 0 0 0 1 0 0 3 0 0 0 0 -1 0 0 4 0 0 1 0 0 0 -1 5 0 0 -1 0 0 0 1 6 0 0 -1 0 0 0 1 7 1 0 0 -1 0 0 0 8 -1 0 0 1 0 0 0 9 1 0 0 -1 0 0 0 10 0 1 0 0 0 -1 0 11 0 -1 0 0 0 1 0 12 0 -1 0 0 0 1 0 Thank you for any replies, Matjaz P.S. I Use RGUI 2.1.1 on WindowsXP and limma package. ---------------------------------------------------------------------- ---------- Matjaz Hren National Institute of Biology Dept. of Plant Physiology and Biotechnology Vecna pot 111 1000 Ljubljana SLOVENIA www.nib.si phone: +386 1 423 33 88 fax: +386 1 257 38 50 ---------------------------------------------------------------------- ---------- -----Original Message----- From: bioconductor-bounces@stat.math.ethz.ch [mailto:bioconductor- bounces@stat.math.ethz.ch] On Behalf Of Gordon K Smyth Sent: Saturday, August 06, 2005 4:37 PM To: scholz at Ag.arizona.edu Cc: bioconductor at stat.math.ethz.ch Subject: [BioC] loop design question > Date: Wed, 3 Aug 2005 08:27:13 US/Arizona > From: scholz at Ag.arizona.edu > Subject: [BioC] loop design question > To: bioconductor at stat.math.ethz.ch > > Hello, > > I'm new to limma and have been plowing through the excellent users > guide, where I've reached a sticking point. Would someone have the > saintly patience to explain to a small mind how the design matrix for > the direct design example works? I think I'm missing something quite > fundamental in that I was under the impression that the numbers 1 and > -1 represented the red and green dyes, respectively, but if that is > true, I have no idea what these numbers mean in columns headed > "CD8-CD4" and "DN-CD4". In fact, I don't really understand what these > "subtracted" column headers mean at all, either in the design matrix > or the contrast matrix. I'm planning a loop design experiment and this > appears to be an essential point to grasp. Thanks in advance of your > answer. The headers mean that the coefficients represent the comparisons CD8 vs CD4 and DN vs CD4 respectively. As the text explains, the other two treatments are compared back to CD4. The easiest way to analysis a direct design is to choose one of the treatments to compare back to in this way, i.e., to stand in as a virtual common reference. The design matrix for the CD example could have been computed using design < modelMatrix(targets, ref="CD4") Just use the modelMatrix() function, look at the 1's and -1's, just understand what the interpretatation of the columns is in terms of treatment comparisons. Gordon > Matt Scholz > Research Specialist > Department of Plant Science > University of Arizona > scholz at ag.arizona.edu > > --------------------------------------------- > College of Agriculture and Life Sciences Web Mail. > http://ag.arizona.edu _______________________________________________ Bioconductor mailing list Bioconductor at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/bioconductor

Hi, Extending the lesson of chapter 9.4. It says that "There are many valid choices for a design matrix for such an experiment and no single correct choice." Would this will be a valid design: CD8 CD4 DN ml12med 1 -1 0 ml13med -1 1 0 ml14med 1 0 -1 ml15med -1 0 1 ml16med 0 -1 1 ml17med 0 1 -1 and then fit <- lmFit(MA, design) contrast.matrix CD8-CD4 DN-CD4 CD8-DN CD8 1 0 1 CD4 -1 -1 0 DN 0 1 -1 fit2 <- contrasts.fit(fit, contrasts.matrix) fit2 <- eBayes(fit2) or because of the restriction that "With direct-design two-color data you will need one fewer coefficient than you have distinct RNA sources..." the design should be (created by design < modelMatrix(targets, ref="CD4")) CD8 DN ml12med 1 0 ml13med -1 0 ml14med 1 -1 ml15med -1 1 ml16med 0 1 ml17med 0 -1 and then the only contrast possible is CD8-DN CD8 1 DN -1 Does the first design is over parameterized? Thanks, Ron >>> "Gordon K Smyth" <smyth at="" wehi.edu.au=""> 08/06/05 5:37 PM >>> > Date: Wed, 3 Aug 2005 08:27:13 US/Arizona > From: scholz at Ag.arizona.edu > Subject: [BioC] loop design question > To: bioconductor at stat.math.ethz.ch > > Hello, > > I'm new to limma and have been plowing through the excellent users guide, where > I've reached a sticking point. Would someone have the saintly patience to > explain to a small mind how the design matrix for the direct design example > works? I think I'm missing something quite fundamental in that I was under the > impression that the numbers 1 and -1 represented the red and green dyes, > respectively, but if that is true, I have no idea what these numbers mean in > columns headed "CD8-CD4" and "DN-CD4". In fact, I don't really understand what > these "subtracted" column headers mean at all, either in the design matrix or > the contrast matrix. I'm planning a loop design experiment and this appears to > be an essential point to grasp. Thanks in advance of your answer. The headers mean that the coefficients represent the comparisons CD8 vs CD4 and DN vs CD4 respectively. As the text explains, the other two treatments are compared back to CD4. The easiest way to analysis a direct design is to choose one of the treatments to compare back to in this way, i.e., to stand in as a virtual common reference. The design matrix for the CD example could have been computed using design < modelMatrix(targets, ref="CD4") Just use the modelMatrix() function, look at the 1's and -1's, just understand what the interpretatation of the columns is in terms of treatment comparisons. Gordon > Matt Scholz > Research Specialist > Department of Plant Science > University of Arizona > scholz at ag.arizona.edu > > --------------------------------------------- > College of Agriculture and Life Sciences Web Mail. > http://ag.arizona.edu _______________________________________________ Bioconductor mailing list Bioconductor at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/bioconductor