Hi Ryan,

Thanks for your reply!

It was my belief that the experimental setup would imply that the dye
effect would be confounded with the biological effect - thanks for
clarifying that this is indeed the case. However, I'm still slightly
confused about the dye effect term; specifically, shouldn't the loess
normalisation (performed by *normalizeWithinArray*s*()* function) correct
for the dye effect? If this is the case, why is a dye effect term required?

Also, with a view to identifying differentially expressed genes, is the
sample code provided in my previous mail otherwise correct? Are there any
alterations that I should consider?

Joseph

On Sat, Jan 4, 2014 at 2:57 PM, Ryan <rct@thompsonclan.org> wrote:

> Hi Joseph,
>
> You cannot include a dye effect term in this design, because the
> biological effect and dye effect are completely confounded due to the lack
> of dye swaps. Hence, I believe this design is incapable of distinguishing
> between dye effects and biological effects. The only way to proceed would
> be to make an arbitrary assumption about the dye effects (e.g. assume dye
> effects are zero).
>
> -Ryan
>
>
> On Sat Jan  4 09:43:02 2014, Joseph Shaw [guest] wrote:
>
>>
>> Hi all,
>>
>> I'm currently looking at data collected from a two-channel microarray
>> experiment; the experimental design is as follows:
>>
>>   - The data represents the results of a competitive hybridization
>> process between control RNA and treatment RNA.
>>   - The data comprises n*m slides (*n* biological replicates and *m*
>> technical replicates for each biological replicate).
>>   - The control label dye (cy5) treatment label dye (cy3) remain the same
>> across all slides - hence, **there is no dye-swap aspect to the
>> experiment**.
>>   - The data were generated by ScanArray Express and slide data are
>> stored in separate .csv files.
>>
>> I'm very new to the limma package. Is it possible to use the limma
>> package to identify differentially expressed genes for this experimental
>> setup?
>>
>> If so,
>>
>>   - how can the design matrix be specified? will a "dye effect" term
>> still be required even if there is no dye-swap?
>>   - is a contrast matrix necessary for this procedure?
>>   - are there any specialist normalisation techniques required for this
>> setup?
>>
>> My code so far is as follows:
>>
>>
>>> # Assuming the contents of the targets file have been identified:
>>>
>>>
>>> RG<-read.maimages(targets, source="scanarrayexpress", sep=",")
>>> RGbk <- backgroundCorrect(RG, method="normexp", offset=50)
>>> MA <- normalizeWithinArrays(RGbk, method="loess")
>>> MA.b=normalizeBetweenArrays(MA, method="quantile")
>>> design <- modelMatrix(targets, ref="control") # nmx1 matrix; all
>>> elements set to  -1.
>>> fit <- lmFit(MA, design)
>>> fit <- eBayes(fit)
>>> topTable(fit, coef=1, adjust="fdr")
>>>
>>>
>> Any assistance with the above would be greatly appreciated.
>>
>> Joseph
>>
>>   -- output of sessionInfo():
>>
>>  sessionInfo()
>>>
>> R version 3.0.2 (2013-09-25)
>> Platform: x86_64-apple-darwin10.8.0 (64-bit)
>>
>> locale:
>> [1] en_IE.UTF-8/en_IE.UTF-8/en_IE.UTF-8/C/en_IE.UTF-8/en_IE.UTF-8
>>
>> attached base packages:
>> [1] stats     graphics  grDevices utils     datasets  methods   base
>>
>> other attached packages:
>> [1] limma_3.18.7
>>
>> --
>> Sent via the guest posting facility at bioconductor.org.
>>
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>>
>

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