I am using Limma for an experimental design with three categories, two biological replicates each and two technical replicates of each biological replicate for a total of 12 arrays. Since this goes just a little bit beyond the Limma user guide, I want to make sure I am setting this up correctly. Also, figure out why I am getting the error message below. The categories are: Cyt1 Cyt2 Mock (which is control for both of the other groups) The biological replicates are indicated with A and B. The technical replicates are indicated with .1 and .2. The variable expr has the array data and the sample columns are: MockA.1 MockA.2 Cyt1A.1 Cyt1A.2 Cyt2A.1 Cyt2A.2 MockB.1 MockB.2 Cyt1B.1 Cyt1B.2 Cyt2B.1 Cyt2B.2 > treatment [1] MockA MockA Cyt1A Cyt1A Cyt2A Cyt2A MockB MockB Cyt1B Cyt1B Cyt2B Cyt2B Levels: Cyt1A Cyt1B Cyt2A Cyt2B MockA MockB > design = model.matrix(~ 0 + treatment) > design Cyt1A Cyt1B Cyt2A Cyt2B MockA MockB 1 0 0 0 0 1 0 2 0 0 0 0 1 0 3 1 0 0 0 0 0 4 1 0 0 0 0 0 5 0 0 1 0 0 0 6 0 0 1 0 0 0 7 0 0 0 0 0 1 8 0 0 0 0 0 1 9 0 1 0 0 0 0 10 0 1 0 0 0 0 11 0 0 0 1 0 0 12 0 0 0 1 0 0 attr(,"assign") [1] 1 1 1 1 1 1 attr(,"contrasts") attr(,"contrasts")$treatment [1] "contr.treatment" > block [1] "MockA" "MockA" "Cyt1A" "Cyt1A" "Cyt2A" "Cyt2A" "MockB" "MockB" "Cyt1B" "Cyt1B" "Cyt2B" "Cyt2B" > dupcor = duplicateCorrelation(expr, design, block=block) > fit.raw = lmFit(expr, design, block=block, + correlation=dupcor$consensus) Error in chol.default(V) : the leading minor of order 2 is not positive definite [[alternative HTML version deleted]]

You need to have a variable "biorep" with biorep number e.g. c(1,1,2,2,3,3,...6,6) This is a blocking variable. The treatments are just Cyt1, Cyt2 and Mock. You will need to follow the instructions for blocking variables. --Naomi At 05:15 PM 7/2/2013, James Platt wrote: >I am using Limma for an experimental design with three categories, >two biological replicates each and two technical replicates of each >biological replicate for a total of 12 arrays. Since this goes >just a little bit beyond the Limma user guide, I want to make sure I >am setting this up correctly. Also, figure out why I am getting the >error message below. > >The categories are: >Cyt1 >Cyt2 >Mock (which is control for both of the other groups) > >The biological replicates are indicated with A and B. The technical >replicates are indicated with .1 and .2. > >The variable expr has the array data and the sample columns are: >MockA.1 MockA.2 Cyt1A.1 Cyt1A.2 Cyt2A.1 Cyt2A.2 MockB.1 MockB.2 >Cyt1B.1 Cyt1B.2 Cyt2B.1 Cyt2B.2 > > > treatment > [1] MockA MockA Cyt1A Cyt1A Cyt2A Cyt2A MockB MockB Cyt1B Cyt1B Cyt2B Cyt2B >Levels: Cyt1A Cyt1B Cyt2A Cyt2B MockA MockB > > design = model.matrix(~ 0 + treatment) > > design > Cyt1A Cyt1B Cyt2A Cyt2B MockA MockB >1 0 0 0 0 1 0 >2 0 0 0 0 1 0 >3 1 0 0 0 0 0 >4 1 0 0 0 0 0 >5 0 0 1 0 0 0 >6 0 0 1 0 0 0 >7 0 0 0 0 0 1 >8 0 0 0 0 0 1 >9 0 1 0 0 0 0 >10 0 1 0 0 0 0 >11 0 0 0 1 0 0 >12 0 0 0 1 0 0 >attr(,"assign") >[1] 1 1 1 1 1 1 >attr(,"contrasts") >attr(,"contrasts")$treatment >[1] "contr.treatment" > > block > [1] "MockA" "MockA" "Cyt1A" "Cyt1A" "Cyt2A" "Cyt2A" "MockB" > "MockB" "Cyt1B" "Cyt1B" "Cyt2B" "Cyt2B" > > dupcor = duplicateCorrelation(expr, design, block=block) > > fit.raw = lmFit(expr, design, block=block, >+ correlation=dupcor$consensus) >Error in chol.default(V) : > the leading minor of order 2 is not positive definite > [[alternative HTML version deleted]] > >_______________________________________________ >Bioconductor mailing list >Bioconductor at r-project.org >https://stat.ethz.ch/mailman/listinfo/bioconductor >Search the archives: >http://news.gmane.org/gmane.science.biology.informatics.conductor

On Jul 3, 2013, at 1:32 PM, Naomi Altman wrote: > You need to have a variable "biorep" with biorep number e.g. c(1,1,2,2,3,3,...6,6) > > This is a blocking variable. So I have to use numbers for the blocking variable? I can't use text? Otherwise, it's the same as the variable which I called "block." > The treatments are just Cyt1, Cyt2 and Mock. My design matrix should have just those three columns? Then, my variable "treatment" should actually be the following: treatment <- c("Mock", "Mock", "Cyt1", "Cyt1", "Cyt2", "Cyt2", "Mock", "Mock", "Cyt1", "Cyt1", "Cyt2", "Cyt2") > You will need to follow the instructions for blocking variables. If I understand the user guide correctly then it's the same as what I did before except for having the corrected "biorep" and "treatment" variables: design = model.matrix(~ 0 + treatment) dupcor = duplicateCorrelation(expr, design, block=biorep) fit.raw = lmFit(expr, design, block=biorep, correlation=dupcor$consensus) fit = eBayes(fit.raw) This runs without error but I just want to make sure that it's correct. The results do make sense. In other words, the top ranked genes include many that were expected. Thanks, James