[BioC] Limma: doing multiple paired t-tests in one go...

[James W. MacDonald]

Hi Philip,

Does this help?

 > sib
[1] 113 113 114 114 101 101 103 103
Levels: 101 103 113 114
 > rep
[1] Control_Diet_1   Treatment_Diet_1
[3] Control_Diet_1   Treatment_Diet_1
[5] Control_Diet_2   Treatment_Diet_2
[7] Control_Diet_2   Treatment_Diet_2
4 Levels: Control_Diet_1 ... Treatment_Diet_2
 > design <- model.matrix(~0+rep+sib)
 > design
   repControl_Diet_1 repControl_Diet_2
1                 1                 0
2                 0                 0
3                 1                 0
4                 0                 0
5                 0                 1
6                 0                 0
7                 0                 1
8                 0                 0
   repTreatment_Diet_1 repTreatment_Diet_2 sib103
1                   0                   0      0
2                   1                   0      0
3                   0                   0      0
4                   1                   0      0
5                   0                   0      0
6                   0                   1      0
7                   0                   0      1
8                   0                   1      1
   sib113 sib114
1      1      0
2      1      0
3      0      1
4      0      1
5      0      0
6      0      0
7      0      0
8      0      0
attr(,"assign")
[1] 1 1 1 1 2 2 2
attr(,"contrasts")
attr(,"contrasts")$rep
[1] "contr.treatment"

attr(,"contrasts")$sib
[1] "contr.treatment"

Best,

Jim




Groot, Philip de wrote:
> Hello All,
>  
> I encountered a problem that I cannot easily solve, most probably because my knowledge of linear models is too restricted. The problem is that I want to do a paired t-test using limma, but that I want to fit multiple comparisons (using different patients!) simultanuously. The reason for this is that all .CEL-files in my experiment are fitted and this fit is used for the eBayes() command to maximize the advantage of using the eBayes approach.
>  
> I found in the bioconductor mailing list a somewhat related topic:
>  
> https://stat.ethz.ch/pipermail/bioconductor/2007-February/016123.html <https://stat.ethz.ch/pipermail/bioconductor/2007-February/016123.html> 
>  
> However, my problem is different. Instead of having multiple treatments over the same patients, I have multiple treatments over multiple patients (but still can do a paired t-test because before and after a single treatment is done on the same person).
>  
> For simplicity, let's assume that I have 2 diets and 2 patients for each diet. My pData(x.norm) looks like this:
>  
>                           sample       replicates sibship
> A96_hA_09_113_1_base.CEL       1   Control_Diet_1     113
> A96_hA_40_113_1_final.CEL      2 Treatment_Diet_1     113
> A96_hA_10_114_1_base.CEL       3   Control_Diet_1     114
> A96_hA_41_114_1_final.CEL      4 Treatment_Diet_1     114
> A96_hA_01_101_2_base.CEL       5   Control_Diet_2     101
> A96_hA_32_101_2_final.CEL      6 Treatment_Diet_2     101
> A96_hA_02_103_2_base.CEL       7   Control_Diet_2     103
> A96_hA_33_103_2_final.CEL      8 Treatment_Diet_2     103
> 
>  My design matrix (for a paired t-test) is calculated as follows (from the Limma user guide):
>     Replicates <- factor(pData(x.norm)$replicates)
>     SibShip <- factor(pData(x.norm)$sibship)
>     design <- model.matrix(~SibShip+Replicates)
> 
> And the design matrix looks like this:
>   (Intercept) SibShip103 SibShip113 SibShip114 ReplicatesControl_Diet_2
> 1           1          0          1          0                        0
> 2           1          0          1          0                        0
> 3           1          0          0          1                        0
> 4           1          0          0          1                        0
> 5           1          0          0          0                        1
> 6           1          0          0          0                        0
> 7           1          1          0          0                        1
> 8           1          1          0          0                        0
>   ReplicatesTreatment_Diet_1 ReplicatesTreatment_Diet_2
> 1                          0                          0
> 2                          1                          0
> 3                          0                          0
> 4                          1                          0
> 5                          0                          0
> 6                          0                          1
> 7                          0                          0
> 8                          0                          1
> attr(,"assign")
> [1] 0 1 1 1 2 2 2
> attr(,"contrasts")
> attr(,"contrasts")$SibShip
> [1] "contr.treatment"
> attr(,"contrasts")$Replicates
> [1] "contr.treatment"
>  
>  
> As you can image, the comparisons I am interested in are Control_Diet_1-Treatment_Diet_1 and Control_Diet_2-Treatment_Diet_2. I might also be interested in Control_Diet_1-Control_Diet_2 and Treatment_Diet_1-Treatment_Diet_2, and so forth. My problem is that the current design matrix is rather complicated and that multiple interaction effects are somehow included, i.e. I cannot get individual effects by simply subtracting two factors in the design matrix (as I understand it). My question is: how can I create a contrast matrix that gives me the comparisons I am interested in? I am really looking forward to an answer!
>  
> Kind Regards,
>  
> Dr. Philip de Groot
> Wageningen University
>  
> 
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-- 
James W. MacDonald, M.S.
Biostatistician
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109
734-647-5623