Indeed I have been somewhat criptic. Section 7.2 shows no matrices, though section 7.4 has a few sample design matrices for two color arrays. I can suppose those -1 there refer to dye swaps. I am going to play with what you suggested and see what do I get out of it. > design <- model.matrix(~0 + targets$Culture) > design targets$Culture6moA targets$Culture6moC targets$Culturet0 1 0 0 1 2 0 0 1 3 0 0 1 4 0 0 1 5 1 0 0 6 1 0 0 7 1 0 0 8 1 0 0 9 0 1 0 10 0 1 0 11 0 1 0 12 0 1 0 attr(,"assign") [1] 1 1 1 attr(,"contrasts") attr(,"contrasts")$`targets$Culture` [1] "contr.treatment" Cheers Massimo On Tue, May 19, 2009 at 3:48 PM, James W. MacDonald <jmacdon@med.umich.edu>wrote: > Hi Massimo, > > Massimo Pinto wrote: > >> I am trying to analyze a microarray experiment that was completed on an >> Agilent single color array platform. >> Using Agi4x44Preprocess, I happily went through the early steps of >> reading, >> normalization, filtration. Ended up on impressive heatmaps etc. >> >> Time to move to limma, now. I've been through limma's userguide >> (limma_2.18.0), chapter 7.2 (Affymetrix and other single channel designs) >> and I am trying to make sense of the construction of a design matrix. I >> know my basics matrix multiplications, but I don't understand the sense of >> those "-1" in my case of single color arrays. More generally, I had made >> some specifications in my targets file, with regards to what each >> population >> was, and I am missing how to feed that info into design and contrast >> matrices to operate with limma. >> Is there any reading that you could recommend? >> > > The limma User's Guide is probably the best thing to read, although the > BioC monograph has some information as well: > http://www.bioconductor.org/pub/docs/mogr/ > > However, I don't know what -1s you are talking about. Section 7.2 doesn't > even show the design matrix, and there aren't any -1s in the design matrix > that is constructed therein: > > > design <- model.matrix(~0 + factor(c(1,1,1,2,2,2,3,3,3))) > > colnames(design) <- paste("group", 1:3, sep="") > > design > group1 group2 group3 > 1 1 0 0 > 2 1 0 0 > 3 1 0 0 > 4 0 1 0 > 5 0 1 0 > 6 0 1 0 > 7 0 0 1 > 8 0 0 1 > 9 0 0 1 > attr(,"assign") > [1] 1 1 1 > attr(,"contrasts") > attr(,"contrasts")$`factor(c(1, 1, 1, 2, 2, 2, 3, 3, 3))` > [1] "contr.treatment" > > And I think using your targets file is covered in some detail as well. In > your case something like > > design <- model.matrix(~0 + targets$Culture) > > should get you pretty close to where you want to be. > > Best, > > Jim > > > Thank you in advance. >> Massimo P. >> >> a sketch of a sample "targets" file follows >> >> X FileName Treatment GErep Biorep Culture >> Array >> 41745_1_1_5 t0_1Gy1 41745_1_1_5.txt 1Gy 1 1 t0 >> 41745 >> 41844_1_4_6 t0_1Gy2 41844_1_4_6.txt 1Gy 1 2 t0 >> 41844 >> 41744_1_1_7 t0_1Gy3 41744_1_1_7.txt 1Gy 1 3 t0 >> 41744 >> 41742_1_4_8 t0_1Gy4 41742_1_4_8.txt 1Gy 1 4 t0 >> 41742 >> 41745_1_2_13 6moA_1Gy1 41745_1_2_13.txt 1Gy 2 1 6moA >> 41745 >> 41742_1_3_14 6moA_1Gy2 41742_1_3_14.txt 1Gy 2 2 6moA >> 41742 >> 41741_1_3_15 6moA_1Gy3 41741_1_3_15.txt 1Gy 2 3 6moA >> 41741 >> 41844_1_3_16 6moA_1Gy4 41844_1_3_16.txt 1Gy 2 4 6moA >> 41844 >> 41745_1_3_21 6moC_1Gy1 41745_1_3_21.txt 1Gy 3 1 6moC >> 41745 >> 41844_1_2_22 6moC_1Gy2 41844_1_2_22.txt 1Gy 3 2 6moC >> 41844 >> 41744_1_2_23 6moC_1Gy3 41744_1_2_23.txt 1Gy 3 3 6moC >> 41744 >> 41743_1_3_24 6moC_1Gy4 41743_1_3_24.txt 1Gy 3 4 6moC >> 41743 >> >> > -- > James W. MacDonald, M.S. > Biostatistician > Douglas Lab > University of Michigan > Department of Human Genetics > 5912 Buhl > 1241 E. Catherine St. > Ann Arbor MI 48109-5618 > 734-615-7826 > -- Massimo Pinto Post Doctoral Research Fellow Enrico Fermi Centre and Italian Public Health Research Institute (ISS), Rome http://claimid.com/massimopinto [[alternative HTML version deleted]]