[BioC] Positive correlation between dye-swap technical replicates

[Claus Mayer]

Dear Michal!

You should include dye effect in your linear model (cf the limma guide 8.1.2
Dye Swaps). The normalization only removers an overall dye-effect but
typically that effect is slightly different from gene to gene. Including the
dye effect in the model should remove this remaining gene-specific bias.

That effect is likely to be the reason for the positive correlation you
observe.

Claus

> -----Original Message-----
> From: bioconductor-bounces at stat.math.ethz.ch [mailto:bioconductor-
> bounces at stat.math.ethz.ch] On Behalf Of Michal Góralski
> Sent: 14 January 2010 11:59
> To: bioconductor at stat.math.ethz.ch
> Subject: [BioC] Positive correlation between dye-swap technical replicates
> 
> 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.
> 
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