[BioC] limma design question

[Jenny Drnevich]

Hi Jim,

I've seen you suggest this way for account for blocks by fitting 
extra columns in the design matrix before. I'm just wondering how 
this differs from the suggestion in the limma vignette (Section 8.2 
Technical Replication) to use duplicateCorrelation() to determine the 
average correlation between blocks. I know they are not 
mathematically equivalent; the coefficients for the treatment groups 
are slightly different, they use different DF, and the p-values tend 
to be larger using the duplicateCorrelation() method (at least for 
the one experiment I'm using). So, is one more "correct" than the 
other? Or are blocks of technical replicates different somehow than 
blocks of patients or cell lines, etc.?

Thanks,
Jenny

At 08:05 AM 11/25/2008, James W. MacDonald wrote:
>Hi Adrian,
>
>Adrian Johnson wrote:
>>dear group,
>>I am sorry to ask again design related question. the data is from SMD.
>>three or two different samples have been obtained from single patient.
>>Say :
>>from patient 1 -  (A). a normal tissue, (B). inflamed tissue and (C).
>>cancer tissue was extracted
>>from Patient 2 -  (A). a normal tissue (B). cancer tissue was only
>>extracted and like wise.
>>A universal reference sample was used to hybridize on Green channel.
>>This is a paired design and a reference design. Limma manual describes
>>examples unique to one specific design.
>
>Yes, but the 'limma User's Guide' also notes that the reference 
>design is pretty much the same as a one-color analysis, but that you 
>have to account for dye-swaps. Since you don't have dye-swaps, then 
>it _is_ the same as a one-color analysis. The only wrinkle here is 
>that you have blocked data (which is also covered in the limma User's Guide).
>
>If you had doubts, you could have approached this iteratively. First 
>let's see what limma thinks you should be using:
>
> > modelMatrix(targets, ref="Ref")
>Found unique target names:
>  ACA B N Ref
>       ACA B N
>  [1,]   0 1 0
>  [2,]   1 0 0
>  [3,]   0 0 1
>  [4,]   1 0 0
>  [5,]   0 0 1
>  [6,]   1 0 0
>  [7,]   0 0 1
>  [8,]   0 1 0
>  [9,]   1 0 0
>
>So this is a pretty simple model matrix, but it doesn't account for 
>the blocks.
>
> > Cy5=factor(c("B","ACA","N","ACA","N","ACA","N","B","ACA"))
> > sibship=factor(rep(c(12,15,16,17), c(2,2,2,3)))
> > model.matrix(~0 + Cy5 + sibship)
>   Cy5ACA Cy5B Cy5N sibship15 sibship16 sibship17
>1      0    1    0         0         0         0
>2      1    0    0         0         0         0
>3      0    0    1         1         0         0
>4      1    0    0         1         0         0
>5      0    0    1         0         1         0
>6      1    0    0         0         1         0
>7      0    0    1         0         0         1
>8      0    1    0         0         0         1
>9      1    0    0         0         0         1
>
>Now this is identical to the above, but with three extra columns to 
>capture the sib-specific means. Note that you could have simply 
>added the three extra columns for the sibs to the previous model matrix.
>
>Also note that your contrast matrix will have to have 6 rows (with 
>the last three being all zeros).
>
>Best,
>
>Jim
>
>
>>I do not know how to combine two different designs.
>>My targets file:
>>FileName        Cy3     Cy5     SibShip (patient)
>>61453.xls       Ref     B       12
>>61454.xls       Ref     ACA     12
>>61459.xls       Ref     N       15
>>61460.xls       Ref     ACA     15
>>61461.xls       Ref     N       16
>>61462.xls       Ref     ACA     16
>>61463.xls       Ref     N       17
>>61464.xls       Ref     B       17
>>61465.xls       Ref     ACA     17
>>
>>I want to identify BvsN, ACAvsN, ACAvsB.
>>how could I get design matrix for this type of design.
>>This is one of those studies where rare cancers have been studied (in 2003).
>>Unfortunately, this is public dataset (Published in Oncogene) where
>>experiments have been done using stanford microarray database.
>>thank you in advance.
>>Adrian.
>>_______________________________________________
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>
>--
>James W. MacDonald, M.S.
>Biostatistician
>Hildebrandt Lab
>8220D MSRB III
>1150 W. Medical Center Drive
>Ann Arbor MI 48109-0646
>734-936-8662
>
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Jenny Drnevich, Ph.D.

Functional Genomics Bioinformatics Specialist
W.M. Keck Center for Comparative and Functional Genomics
Roy J. Carver Biotechnology Center
University of Illinois, Urbana-Champaign

330 ERML
1201 W. Gregory Dr.
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e-mail: drnevich at illinois.edu