# [BioC] limma design question

James W. MacDonald jmacdon at med.umich.edu
Tue Nov 25 15:05:25 CET 2008

```Hi Adrian,

> 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.
>
>
>
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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

```