[BioC] Factorial design with LIMMA

[cmprobst]

Hi,

After using SAM for a long time, I have started an "struggle" with the exceptional LIMMA package.

After working with the example datasets and looking at the mailing list, I have begun to analyse our own data.

In the simpler experiments, I have not found any trouble and the package did very well.

But now I am trying to analyse a 2x2x2 factorial design, and I think I have run into problems, with my biologist background.

We are using Affymetrix Genechip, studying an infection process in two different cell types and in two time points. There is two replicates for each point.

The phenoData slot is:

> pData(resRMA)
      Strain Infected Time
Hyb01      A        1   6h
Hyb02      A        1   6h
Hyb03      A        1  24h
Hyb04      A        1  24h
Hyb05      A        0   6h
Hyb06      A        0   6h
Hyb07      A        0  24h
Hyb08      A        0  24h
Hyb09      B        1   6h
Hyb10      B        1   6h
Hyb11      B        1  24h
Hyb12      B        1  24h
Hyb13      B        0   6h
Hyb14      B        0   6h
Hyb15      B        0  24h
Hyb16      B        0  24h

I tried to create the following design matrix:

> design<-model.matrix(~Strain*Infected*Time, data=pData(resRMA))
> design
      (Intercept) StrainB Infected1 Time6h StrainB:Infected1 StrainB:Time6h Infected1:Time6h StrainB:Infected1:Time6h
Hyb01           1       0         1      1                 0              0                1                        0
Hyb02           1       0         1      1                 0              0                1                        0
Hyb03           1       0         1      0                 0              0                0                        0
Hyb04           1       0         1      0                 0              0                0                        0
Hyb05           1       0         0      1                 0              0                0                        0
Hyb06           1       0         0      1                 0              0                0                        0
Hyb07           1       0         0      0                 0              0                0                        0
Hyb08           1       0         0      0                 0              0                0                        0
Hyb09           1       1         1      1                 1              1                1                        1
Hyb10           1       1         1      1                 1              1                1                        1
Hyb11           1       1         1      0                 1              0                0                        0
Hyb12           1       1         1      0                 1              0                0                        0
Hyb13           1       1         0      1                 0              1                0                        0
Hyb14           1       1         0      1                 0              1                0                        0
Hyb15           1       1         0      0                 0              0                0                        0
Hyb16           1       1         0      0                 0              0                0                        0
attr(,"assign")
[1] 0 1 2 3 4 5 6 7
attr(,"contrasts")
attr(,"contrasts")$Strain
[1] "contr.treatment"
attr(,"contrasts")$Infected
[1] "contr.treatment"
attr(,"contrasts")$Time
[1] "contr.treatment"


Whick looked very logical for me, but very complicated (well, I was expecting something complex, anyway).

So, before going into contrast analysis that could be meaningless, I decide to ask for some advice from Bioconductor´s list.

First, is this model correct?

Second, I am interested in several aspects (contrasts), which I can address if asked:

For instance, differences between cell types without infection, and differences between cell types with infection (time excluded or included).

Which contrasts can answer these questions? How many constrasts I can analyse? All of them? Is there sufficient degree of freedom?

Thanks in advance for your assistance.

Christian Probst
Bioinformatics - IBMP
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