Dear Gordon, Thank you for the tip. I think I am making some progress. I should have phrased my last question better. I meant what code do I use to get genes differentially expressed in one tissue under control and stress conditions. I have one more question. Is it possible to specify the random effect in the design matrix in edgeR? I am thinking of something similar to the RANDOM statement in SAS that will allow me to treat "block" as a random effect? Cheers. Tilahun ---------------------------- > Message: 5 > Date: Tue, 31 Jan 2012 11:18:59 +1100 (AUS Eastern Daylight Time) > From: Gordon K Smyth <smyth@wehi.edu.au> > To: Tilahun Abebe <tilahun.abebe@uni.edu> > Cc: Bioconductor mailing list <bioconductor@r-project.org> > Subject: [BioC] Please help! How to specify factors for a RCBD in > edgeR > Message-ID: <pine.wnt.4.64.1201311107280.1360@pc765.wehi.edu.au> > Content-Type: TEXT/PLAIN; charset=US-ASCII; format=flowed > > Dear Tilahun, > > The first step is that you need to create a data frame containing the > experimental factors, just as you would for a SAS analysis. So you need > to create three factors: > > Tissue > Treatment > Block > > each containing 24 values, one for each RNA sample. Then the design marix > is formed by: > > design <- model.matrix(~Tissue*Treatment+Block) > > Type colnames(design) to see how the coefficients are defined. You will > see that the interaction coefficients are coefficients 6 to 8. > > After fitting your linear model, you could find genes that show > significant Tissue*Treatment interaction (on 3df) by > > lrt <- glmLRT(d, fit, coef=6:8) > > and so on. > > I don't understand your question "How do I obtain differentially expressed > genes in each Tissue*Stress combination?", so I can't give specific advice > on that. Differentially expressed compared to what? > > Best wishes > Gordon > > ---------------- original message ------------------- > [BioC] Please help! How to specify factors for a RCBD in edgeR > Tilahun Abebe tilahun.abebe at uni.edu > Wed Jan 25 22:16:41 CET 2012 > > Hi, > > I am learning how to use edgeR to analyze RNA-seq data generated from > Illumina GAII. The experimental design is fairly complex. > > I have a mixed 4x2 factorial randomized complete block design (RCBD) > consisting of: > > 4 tissues: A, B, C, D > 2 treatments: control, stressed > 3 blocks: Block1, Block2, Block3 > > Tissue and treatment are fixed effects and block is a random effect. > > Here is the code I tried to use in edgeR: > > > counts <- read.delim( file = "Mycounts.txt", header = TRUE) > > rownames <-counts [ , 1 ] > > d <- counts [, 2:25] # counts are in columns 2-25 > > d > > group <- c(rep("Control", 3), rep("Stress", 3), rep("Control", 3), > rep("Stress", 3), rep("Control", 3), rep("Stress", 3), rep("Control", 3), > rep("Stress", 3)) > > d <- DGEList(counts = d, group = group) > > design <- model.matrix(~group) > > d <- calcNormFactors(d) > > d$samples > > d <- estimateGLMCommonDisp(d, design) > > d <- estimateGLMTagwiseDisp(d, design) > > d$common.dispersion > > fit <- glmFit(d, design) > > lrt <- glmLRT(d, fit, coef=2) > > topTags(lrt, n=4) > > I am interested to know genes differentially expressed in each of the four > tissues under stress. However, I feel like I am not specifying the factors > correctly in the design statement. My questions are: > > 1) How do I specify the fixed effects Tissue, Stress, and Tissue*Stress > interaction in the model? > 2) How do I tell edgeR to use block as a random effect? > 3) How do I obtain differentially expressed genes in each Tissue*Stress > combination? > > I appreciate your help. > > Cheers. > > Tilahun Abebe, Ph.D. > University of northern Iowa > Cedar Falls, IA > > [[alternative HTML version deleted]]