Dear EdgeR community, I am new to edgeR and still in the phase of reading the vignette in details to be able to use it for my data. I have a question in understanding the model.matrix. On page 27 (paragraph 3.3.2 "Nested interaction formulas"), the design is defined as: > targets Sample Treat Time 1 Sample1 Placebo 0h 2 Sample2 Placebo 0h 3 Sample3 Placebo 1h 4 Sample4 Placebo 1h 5 Sample5 Placebo 2h 6 Sample6 Placebo 2h 7 Sample1 Drug 0h 8 Sample2 Drug 0h 9 Sample3 Drug 1h 10 Sample4 Drug 1h 11 Sample5 Drug 2h 12 Sample6 Drug 2h targets$Treat <- relevel(targets$Treat, ref="Placebo") design <- model.matrix(~Treat + Treat:Time, data=targets) #and the coefficient names are: > colnames(design) [1] "(Intercept)" "TreatDrug" [3] "TreatPlacebo:Time1h" "TreatDrug:Time1h" [5] "TreatPlacebo:Time2h" "TreatDrug:Time2h" Whereas on page 28 (paragraph 3.3.4 "Interaction at any time") the design formula looks like this: #I added "2" in "design2" compared to original text for easier following: > design2 <- model.matrix(~Treat + Time + Treat:Time, data=targets) > colnames(design2) [1] "(Intercept)" "TreatDrug" [3] "Time1h" "Time2h" [5] "TreatDrug:Time1h" "TreatDrug:Time2h" It is explained that for the design2 (page 29 top): "The last two coefficients give the DrugvsPlacebo.1h and DrugvsPlacebo.2h contrasts, so that > lrt <- glmLRT(fit, coef=5:6) is useful because it detects genes that respond differently to the drug, relative to the placebo, at either of the times." My question is, if I understood it well, in design2, why there are no coefficients "TreatPlacebo:Time1h" and "TreatPlacebo:Time2h"? And should't "Time1h" and "Time2h" be effects of time, no matter of the Treat(ment), and not: "> lrt <- glmLRT(fit, coef=3) and > lrt <- glmLRT(fit, coef=4) are the e ffects of the reference drug, i.e., the effects of the placebo at 1 hour and 2 hours" as it is written in the vignette text? Thank you! ------------------------------------ Why I need edgeR: I have an RNASeq experiment (~30 samples), where I need to explore the influence of 3 factors with 2 levels each: 1. sex: f/m 2. disease_state:healthy/cancer 3. localization: blood/bones. Question I want to answer: which genes are differentially expressed between 2 localisations in 2 disease states (i.e. are bones more severely affected by cancer than blood) taking into account different sex? I assume that my design formula should look like: design=~sex+disease+localization+disease:localization Could anyone please tell me if the formula is correct? And, what should be the output? How could I know if the disease has different effects depending on the localization? By number of genes affected (=differentially expressed)? I would appreciate very much if someone has some time to help me with any of the questions. Best, Mike [[alternative HTML version deleted]]

Dear Mike, You are asking basic questions about interaction formula in R. Many non-statisticians find model formulas in R a bit confusing. It would be simpler and just as effective to take the alternative approach described in the section "Defining each treatment combination as a group" of the edgeR User's Guide. For your real experiment, you might combine disease state and localization into one factor (dis.loc) and use model.matrix(~sex+dis.loc) Best wishes Gordon > Date: Fri, 7 Feb 2014 14:34:04 +0100 > From: Mike Miller <mike.bioc32 at="" gmail.com=""> > To: <bioconductor at="" stat.math.ethz.ch=""> > Subject: [BioC] edgeR, multifactorial design > > Dear EdgeR community, > > > I am new to edgeR and still in the phase of reading the vignette in details > to be able to use it for my data. > I have a question in understanding the model.matrix. > On page 27 (paragraph 3.3.2 "Nested interaction formulas"), the design is > defined as: >> targets > Sample Treat Time > 1 Sample1 Placebo 0h > 2 Sample2 Placebo 0h > 3 Sample3 Placebo 1h > 4 Sample4 Placebo 1h > 5 Sample5 Placebo 2h > 6 Sample6 Placebo 2h > 7 Sample1 Drug 0h > 8 Sample2 Drug 0h > 9 Sample3 Drug 1h > 10 Sample4 Drug 1h > 11 Sample5 Drug 2h > 12 Sample6 Drug 2h > > targets$Treat <- relevel(targets$Treat, ref="Placebo") > > design <- model.matrix(~Treat + Treat:Time, data=targets) > > > #and the coefficient names are: >> colnames(design) > [1] "(Intercept)" "TreatDrug" > [3] "TreatPlacebo:Time1h" "TreatDrug:Time1h" > [5] "TreatPlacebo:Time2h" "TreatDrug:Time2h" > > Whereas on page 28 (paragraph 3.3.4 "Interaction at any time") the design > formula looks like this: > #I added "2" in "design2" compared to original text for easier following: >> design2 <- model.matrix(~Treat + Time + Treat:Time, data=targets) >> colnames(design2) > [1] "(Intercept)" "TreatDrug" > [3] "Time1h" "Time2h" > [5] "TreatDrug:Time1h" "TreatDrug:Time2h" > > It is explained that for the design2 (page 29 top): > "The last two coefficients give the DrugvsPlacebo.1h and DrugvsPlacebo.2h > contrasts, so that >> lrt <- glmLRT(fit, coef=5:6) > is useful because it detects genes that respond differently to the drug, > relative to the placebo, > at either of the times." > My question is, if I understood it well, in design2, why there are no > coefficients "TreatPlacebo:Time1h" and "TreatPlacebo:Time2h"? And should't > "Time1h" and "Time2h" be effects of time, no matter of the Treat(ment), and > not: > "> lrt <- glmLRT(fit, coef=3) > and >> lrt <- glmLRT(fit, coef=4) > are the e ffects of the reference drug, i.e., the effects of the placebo at > 1 hour and 2 hours" as it is written in the vignette text? > > Thank you! > ------------------------------------ > Why I need edgeR: I have an RNASeq experiment (~30 samples), where I need > to explore the influence of 3 factors with 2 levels each: > 1. sex: f/m > 2. disease_state:healthy/cancer > 3. localization: blood/bones. > Question I want to answer: which genes are differentially expressed between > 2 localisations in 2 disease states (i.e. are bones more severely affected > by cancer than blood) taking into account different sex? > I assume that my design formula should look like: > design=~sex+disease+localization+disease:localization > > Could anyone please tell me if the formula is correct? And, what should be > the output? How could I know if the disease has different effects depending > on the localization? By number of genes affected (=differentially > expressed)? > > I would appreciate very much if someone has some time to help me with any > of the questions. > Best, > Mike ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}