[BioC] limma analysis of 2-color experiment with tech reps [was: Help with]

[Gordon K Smyth]

Dear Guillaume,

On Mon, 23 May 2011, Guillaume Meurice wrote:

> Dear Gordon,
>
> many thanks for your answer.
>
>> I am going to assume that the main purpose of your experiment is to 
>> find genes for which the d7 vs d1 response is different between the two 
>> groups of patients.
>
> you're right, this exactly what I'm looking for.
>
>> Since you have gone to the trouble of dye-swapping, you should also 
>> include an intercept term in the design matrix, so as to soak up any 
>> probe-specific dye effects:
>>
>>  design <- cbind(Dye=1,design)
>
> I'll try this but is cost one degree of freedom (I guess ?).
> Is it a could procedure to run the statistical test with the intercept 
> term, then eventually discard the dye-specific probes, and then 
> re-perform the test without the intercept ?

I suppose that you could do this, but seems a lot of effort and risk for 
little gain.  I think you can well afford the one df.

>> Then
>>
>>  cor <- duplicateCorrelation(MAn, design, weights=NULL,
>>         block=targets$TechnicalReplicat)
>
> Great !! Could I also use this approach to model my biological 
> replicates ? I mean, here, patients A, B and C (one side) and D,E,F 
> (other side) are biological replicates. Could I use something like :
>
> cor <- duplicateCorrelation(MAn, design, weights=NULL,  block=targets$Issu) ?

No, you can't.

>>  fit <- lmFit(MAn, design, weights=NULL,
>>    block=targets$TechnicalReplicat, correlation=cor$consensus)
>
>
> then are the following line correct ?

Yes, all the lines after this are fine.

Best wishes
Gordon

> # computing the contrast matrix
> cont.mat = makeContrasts(RvsNR = R_D7vsD1  - NR_D7vsD1, levels = design)
>
> # Fitting the model to the contrast matrix:
> fit2 <- contrasts.fit(fit, cont.mat)
> fit2 <- eBayes(fit2)
>
> # the probe list I want:
> res.RvsNR = topTable(fit2,number=nrow(MAm), coef="RvsNR")
>
> # the dye-specific probes :
> res.RvsNR = topTable(fit2,number=nrow(MAm), coef="Dye")
>
>
> many thank again.
>
>
> Best regards,
> --
> Guillaume
>
>> Dear Guillaume,
>>
>> I am going to assume that the main purpose of your experiment is to find genes for which the d7 vs d1 response is different between the two groups of patients.
>>
>>> Date: Fri, 20 May 2011 15:44:45 +0200
>>> From: Guillaume Meurice <guillaume.meurice at igr.fr>
>>> To: Guillaume Meurice <guillaume.meurice at igr.fr>
>>> Cc: bioconductor at stat.math.ethz.ch
>>> Subject: Re: [BioC] Help with
>>>
>>> Sorry, I made some mistake in my previous mail, into the contrast matrix (bad copy paste from another project), so here I have corrected them.
>>>
>>>> I have a question regarding the way to properly design biological replicate and technical replicates.
>>>>
>>>> In the projet, we have two groups of sample : R (respond to the treatment), NR (no response). For each groups, there is several replicates (3) that come from different patients (so not strictely biological). Each patient have two sample : one at day one(d1), the second after one week (d7) The hybridization are planned to be performed in dual color, with dye-sap.
>>>>
>>>> Here is the target file :
>>>>
>>>> Cy3	Cy5	Patient	Issu	TechnicalReplicat
>>>> d1	d7	A	R	1
>>>> d7	d1	A	R	1
>>>> d1	d7	B	R	2
>>>> d7	d1	B	R	2
>>>> d1	d7	C	R	3
>>>> d7	d1	C	R	3
>>>> d1	d7	D	NR	4
>>>> d7	d1	D	NR	4
>>>> d1	d7	E	NR	5
>>>> d7	d1	E	NR	5
>>>> d1	d7	F	NR	6
>>>> d7	d1	F	NR	6
>>>>
>>>>
>>>> my question is how to properly design the matrix design and the contrast matrix to answer the difference between the two groups ? Which column should I use as biological replicat (so as I can use the duplicateCorrelation function) ?
>>>>
>>>> here is how I've started, but I can't figure out how to use duplicateCorrelation with this design
>>>>
>>>> design <- cbind(
>>>> 		R_D7vsD1  = c(-1,1,-1,1,-1,1,  0,0, 0,0, 0,0),
>>>> 		NR_D7vsD1 = c( 0,0,0,0,0,0,-1,1,-1,1,-1,1)
>>>> )
>>
>> Since you have gone to the trouble of dye-swapping, you should also include an intercept term in the design matrix, so as to soak up any probe-specific dye effects:
>>
>>  design <- cbind(Dye=1,design)
>>
>> Then
>>
>>  cor <- duplicateCorrelation(MAn, design, weights=NULL,
>>         block=targets$TechnicalReplicat)
>>
>>  fit <- lmFit(MAn, design, weights=NULL,
>>    block=targets$TechnicalReplicat, correlation=cor$consensus)
>>
>> Best wishes
>> Gordon
>>
>>>> fit <- lmFit(MAn, design, weights = NULL)
>>>> cont.mat = makeContrasts(NRvsR = NR_D7sD1 - R_D7vsD1, levels = design)
>>>> fit2 <- contrasts.fit(fit, cont.mat)
>>>> fit2 <- eBayes(fit2)
>>>>
>>>>
>>>> Many thanks by advance for any help.
>>>>
>>>> --
>>>> Guillaume

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