[BioC] Limma - gls.series - design matrix?

[Jakob Hedegaard]

Hi

We are studying the impact of different traits on the expression in pigs using cDNA microarray. We have some problems using Limma when taking within-array replicated spots into acount. Analysing the effects of a single trait (like sick-healthy) works fine, but combing traits (like far6syg-far2syg, far=father, syg=sick) results in the presence of NA's in the output from gls.series. We are using R ver 1.9.0 and Bioconductor 1.4.

What we do:

> targets <- readTargets("targets_farXsyg.txt")
> targets
   SlideNumber Cy3      Cy5
1     12755473 mix far6rask
2     12755474 mix  far6syg
3     12755475 mix far2rask
4     12755476 mix  far2syg
5     12755477 mix far6rask
.......
25    12759971 mix  far2syg
26    12760017 mix  far6syg
27    12760018 mix far6rask
28    12760019 mix  far6syg
> 
> model <- modelMatrix(targets, ref="mix")
Found unique target names:
 far2rask far2syg far6rask far6syg mix 
> model 
   far2rask far2syg far6rask far6syg
1         0       0        1       0
2         0       0        0       1
3         1       0        0       0
4         0       1        0       0
5         0       0        1       0
...........
25        0       1        0       0
26        0       0        0       1
27        0       0        1       0
28        0       0        0       1
> 
> contrast.matrix <- makeContrasts(far6rask-far6syg, far2rask-far2syg, far6rask-far2syg, far6syg-far2rask, far6rask-far2rask, far6syg-far2syg, levels=model)
> contrast.matrix                                          
         far6rask - far6syg far2rask - far2syg far6rask - far2syg
far2rask                  0                  1                  0
far2syg                   0                 -1                 -1
far6rask                  1                  0                  1
far6syg                  -1                  0                  0
         far6syg - far2rask far6rask - far2rask far6syg - far2syg
far2rask                 -1                  -1                 0
far2syg                   0                   0                -1
far6rask                  0                   1                 0


> contrast.matrix                                          
         far6rask - far6syg far2rask - far2syg far6rask - far2syg
far2rask                  0                  1                  0
far2syg                   0                 -1                 -1
far6rask                  1                  0                  1
far6syg                  -1                  0                  0
         far6syg - far2rask far6rask - far2rask far6syg - far2syg
far2rask                 -1                  -1                 0
far2syg                   0                   0                -1
far6rask                  0                   1                 0
far6syg                   1                   0                 1
> 
> design <- model %*% contrast.matrix
> design
    
     far6rask - far6syg far2rask - far2syg far6rask - far2syg
  1                   1                  0                  1
  2                  -1                  0                  0
  3                   0                  1                  0
  4                   0                 -1                 -1
  5                   1                  0                  1
 ............
  25                  0                 -1                 -1
  26                 -1                  0                  0
  27                  1                  0                  1
  28                 -1                  0                  0
    
     far6syg - far2rask far6rask - far2rask far6syg - far2syg
  1                   0                   1                 0
  2                   1                   0                 1
  3                  -1                  -1                 0
  4                   0                   0                -1
  5                   0                   1                 0
...........
  25                  0                   0                -1
  26                  1                   0                 1
  27                  0                   1                 0
  28                  1                   0                 1
> cor <- duplicateCorrelation(MArep$M,design,ndups=4)
> fitcor <- gls.series(MArep$M, design,ndups=4,correlation=cor$cor)
> fitcor$coefficients[1:5,]
     far6rask - far6syg far2rask - far2syg far6rask - far2syg
[1,]        0.019897478       -0.058434294        -0.06046312
[2,]       -0.006362523        0.061185620        -0.11697534
[3,]       -0.003859953       -0.050241979        -0.02122334
[4,]        0.013675174       -0.003196691        -0.02909511
[5,]        0.005154980        0.043414444        -0.04125395
     far6syg - far2rask far6rask - far2rask far6syg - far2syg
[1,]                 NA                  NA                NA
[2,]                 NA                  NA                NA
[3,]                 NA                  NA                NA
[4,]                 NA                  NA                NA
[5,]                 NA                  NA                NA
> 

For some reason (?) it is always the final three contrasts that results in NA´s - chancing the design matrix by flipping the final three contrast with the first three contrasts, still results in NA's in the final three contrast (which were the the first three before flipping......). Some technical problem? The design matrix ("design") are of dim 28x6 as it it should be......
Any suggestions?

------------------------------------------------------
Jakob Hedegaard

Danish Institute of Agricultural Sciences
Department of Animal Breeding and Genetics
Research Centre Foulum
P.O. Box 50
DK-8830 Tjele, Denmark

Tel: (+45) 8999 1363
Fax: (+45) 8999 1300