[BioC] Positive correlation between dye-swap technical replicates

[Michał Góralski]

Dear All,

I have some doubts concerning linear model used in my data analysis. I 
was searching for the answer on the mail list but I didn't find the 
similar case.
I analyse tobacco roots treated with 2 types of stress: NaCl and CdCl2. 
I have pooled common reference and I have 3 biological replicates of 
treated plants. I also did dye swaps as technical replicate.
This is my targets file:
SlideNumber    Name    FileName    Cy3    Cy5
13244317    21    13244317.gpr    Control    NaCl
13244318    22    13244318.gpr    Control    NaCl
13244315    23    13244315.gpr    Control    NaCl
13244319    31    13244319.gpr    Control    CdCl2
13244337    32    13244337.gpr    Control    CdCl2
13244316    33    13244316.gpr    Control    CdCl2
13244330    21    13244330.gpr    NaCl    Control
13244329    22    13244329.gpr    NaCl    Control
13244331    23    13244331.gpr    NaCl    Control
13244333    31    13244333.gpr    CdCl2    Control
13244335    32    13244335.gpr    CdCl2    Control
13244336    33    13244336.gpr    CdCl2    Control

I did background subtraction with method "normexp" and normalization 
"pronttip loess", without normalization between arrays.

Now I have the vector indicating biological and technical replicates.

 >biolrep=c(1,2,3,4,5,6,1,2,3,4,5,6)

and create model matrix:

 >design=modelMatrix(targets, ref="Control")
 > design
      CdCl2 NaCl
 [1,]     0    1
 [2,]     0    1
 [3,]     0    1
 [4,]     1    0
 [5,]     1    0
 [6,]     1    0
 [7,]     0   -1
 [8,]     0   -1
 [9,]     0   -1
[10,]    -1    0
[11,]    -1    0
[12,]    -1    0

I'm interested in such contrasts:

 >cmatrix=makeContrasts(NaCl, CdCl2, NaCl-CdCl2,levels=design)
 > cmatrix
       Contrasts
Levels  NaCl CdCl2 NaCl - CdCl2
  CdCl2    0     1           -1
  NaCl     1     0            1

Object for duplicate correlation with dye-swaps:

 >corfit=duplicateCorrelation(MA, design=design, ndups=1, block=biolrep)

and the first problem is:
 > corfit$consensus
[1] 0.3926545

In limma manual it is written that correlation should be negative for 
dye swaps- why is it positive?- is it a question of wrong model matrix 
or is it something wrong with my samples?

but

When I do simple hierarchical clustering of log-ratios:
 >dist.matrix=dist(t(MA$M))
 >hc=hclust(dist.matrix)
 >par(mfrow=c(1,1)
 >plot(hc)

The plot divides my arrays in two groups that exactly reflects dye 
swaps. So maybe the model is correct?
 
I was thinking also about checking dye effect so I tried with such model:

 > design2=cbind(Dye=1, design)
 > design2
      Dye CdCl2 NaCl
 [1,]   1     0    1
 [2,]   1     0    1
 [3,]   1     0    1
 [4,]   1     1    0
 [5,]   1     1    0
 [6,]   1     1    0
 [7,]   1     0   -1
 [8,]   1     0   -1
 [9,]   1     0   -1
[10,]   1    -1    0
[11,]   1    -1    0
[12,]   1    -1    0

 I'm not sure if  I can use such model.
if  I  use it:

 > corfit=duplicateCorrelation(MA, design=design2, ndups=1, block=blockrep)
 > corfit$consensus
[1] -0.04530506

The second problem is that each probe on my array is duplicated so in 
the final top table I have each gene doubled- I read it is not possible 
in Limma to analyse both technical duplicates and gene replicas on the 
array. Could you give me any hint how to solve this problem?
 
I will be glad for any help in this cases

Best regards,

Michal Goralski, PhD student,
Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, 
Poland.