Hi Gordon Thanks for the response. I worked through my two examples and found out they are equivalent. >Do you know how to multiply a vector by a matrix? If you do, then I think >the best way to figure out what the design matrix is doing for you is just >to sit down with a piece of paper and a pencil for a few minutes, and >multiply the design matrix by the coefficient vector. I did actually do this previous to sending the mail but it didn't easy my confusion. Eg: > apoai.design ApoAI Control 1 0 1 2 0 1 3 0 1 4 0 1 5 0 1 6 0 1 7 0 1 8 0 1 9 1 0 10 1 0 11 1 0 12 1 0 13 1 0 14 1 0 15 1 0 16 1 0 > apoai.contrasts ApoAI - Control ApoAI 1 Control -1 > apoai.design %*% apoai.contrasts ApoAI - Control 1 -1 2 -1 3 -1 4 -1 5 -1 6 -1 7 -1 8 -1 9 1 10 1 11 1 12 1 13 1 14 1 15 1 16 1 So I still don't see how multiplying those two matrices relates to the ApoAI design matrix given in the documentation, which is: Control-Ref KO-Control c1 1 0 c2 1 0 c3 1 0 c4 1 0 c5 1 0 c6 1 0 c7 1 0 c8 1 0 k1 1 1 k2 1 1 k3 1 1 k4 1 1 k5 1 1 k6 1 1 k7 1 1 k8 1 1 I have read the updated limma documentation and it is clearer now, but it is still unclear why the above design matrix is used for the ApoAI data - there is no explanation as to how it relates to the design matrix and contrasts matrix I calculated above using modelMatrix() and makeContrasts(). Mick -----Original Message----- From: Gordon Smyth [mailto:smyth@wehi.edu.au] Sent: 12 November 2004 12:23 To: michael watson (IAH-C) Cc: bioconductor@stat.math.ethz.ch Subject: Re: Bioconductor Digest, Vol 21, Issue 12 You are reading the Limma User's Guide written for Bioconductor Release 1.4. Could I encourage you to consider the moving onto Bioconductor 1.5? Even without upgrading your software, you could read a more recent User's Guide at http://bioinf.wehi.edu.au/limma/usersguide.pdf. Section 10 might help, especially section 10.5. >Date: Thu, 11 Nov 2004 12:11:34 -0000 >From: "michael watson (IAH-C)" <michael.watson@bbsrc.ac.uk> >Subject: [BioC] Confusion over limma documentation and design/contrast > matrices >To: <bioconductor@stat.math.ethz.ch> > >Hi > >The confusion comes from the examples given in usersguide.pdf. >Sections 7.3 and 8.2 both deal with two colour microarray data where a >common reference has been used. > >Section 7.3 advocates the use of designMatrix() and makeContrasts(), >where lmFit() is first used with the design matrix, and then >contrasts.fit() is used with the contrasts matrix, and then eBayes() >applied to the resulting linear model fit. > >Section 8.2 gives us the design matrix directly, and does not use a >contrasts matrix at all. > >So my first question is this: with two colour cDNA microarrays which >use a common reference, do we need both a design matrix and a contrasts >matrix, or just a design matrix? (I'm assuming it depends on how many >samples there are in addition to the reference, but I'm not sure). > >Looking at section 8.2, the ApoAI data, we have three samples - control >mice, ApoAI mice and the reference sample. 8 control mice were >compared to the reference and 8 ApoAI mice were compared to the >reference, and in all cases the reference was Cy3. Therefore, a >targets file to describe this experiment can reasonably be expected to >look like this: > > SlideNumber Cy5 Cy3 >1 c1 Control Ref >2 c2 Control Ref >3 c3 Control Ref >4 c4 Control Ref >5 c5 Control Ref >6 c6 Control Ref >7 c7 Control Ref >8 c8 Control Ref >9 k1 ApoAI Ref >10 k2 ApoAI Ref >11 k3 ApoAI Ref >12 k4 ApoAI Ref >13 k5 ApoAI Ref >14 k6 ApoAI Ref >15 k7 ApoAI Ref >16 k8 ApoAI Ref > >If we run modelMatrix() on this, we get this: > > ApoAI Control >1 0 1 >2 0 1 >3 0 1 >4 0 1 >5 0 1 >6 0 1 >7 0 1 >8 0 1 >9 1 0 >10 1 0 >11 1 0 >12 1 0 >13 1 0 >14 1 0 >15 1 0 >16 1 0 > >If we then carry on the example in 7.3, and create a contrasts matrix, >then I assume we are interested in the comparison of ApoAI knockout >mice to Control mice, so I run: > > > makeContrasts(ApoAI-Control, levels=apoai.design) > >And get this > > ApoAI - Control >ApoAI 1 >Control -1 > >So, just to take a breath here, I am using the documentation from >section 7.3 to run against the data in section 8.2. I now have a >design matrix and a contrasts matrix for the ApoAI data. The only >problem is that they look completely different to the example design >matrix given in section 8.2. The design matrix there is: > > Control-Ref KO-Control >c1 1 0 >c2 1 0 >c3 1 0 >c4 1 0 >c5 1 0 >c6 1 0 >c7 1 0 >c8 1 0 >k1 1 1 >k2 1 1 >k3 1 1 >k4 1 1 >k5 1 1 >k6 1 1 >k7 1 1 >k8 1 1 > >And there is no contrasts matrix. > >So, my question 2: is the design matrix given in section 8.2 the >equivalent of using the design and contrasts matrices that I calculated >above? Yes it is. This has been said many, many times on this list. Do you know how to multiply a vector by a matrix? If you do, then I think the best way to figure out what the design matrix is doing for you is just to sit down with a piece of paper and a pencil for a few minutes, and multiply the design matrix by the coefficient vector. Gordon >Question 3: does the fact that the ApoAI data only has one contrast of >interest mean that I don't need a contrasts matrix, I only need a >design matrix? (albeit a DIFFERENT design matrix to one that would be >given by modelMatrix()). > >Question 4: can anyone recommend a good book which I can go and read >and learn all about design and contrast matrices and their relation to >linear models? Everything I have read so far just says something like >"... The design matrix is therefore this... and the contrast matrix is >therefore this..." and doesn't actually explain how exactly the >structure of the matrices was arrived at... > >Thanks and sorry for the long mail! > >Mick