[BioC] R: design matrix with technical and biologial replicates
Manuela Di Russo
manuela.dirusso at for.unipi.it
Wed Apr 18 16:11:50 CEST 2012
Thank you James!
I had already applied both methods you suggested but I wanted to see if
there was a better way to handle this kind of experimental design.
I have another question: I computed means of the duplicates arrays after
preprocessing and filtering but before fitting the linear model, is this
correct?
Thank you!
Manuela
----------------------------------------------------------------------------
----------
Manuela Di Russo, Ph.D. Student
Department of Experimental Pathology, MBIE
University of Pisa
Pisa, Italy
e-mail: manuela.dirusso at for.unipi.it
mobile: +393208778864
phone: +39050993538
-----Messaggio originale-----
Da: James W. MacDonald [mailto:jmacdon at uw.edu]
Inviato: mercoledì 18 aprile 2012 15:43
A: Manuela Di Russo
Cc: bioconductor at r-project.org
Oggetto: Re: [BioC] design matrix with technical and biologial replicates
Hi Manuela,
On 4/18/2012 7:52 AM, Manuela Di Russo wrote:
> Dear list,
>
> I'm working with microarray expression data and I am using limma to
> detect differentially expressed genes. I have some questions about the
> design matrix and the handling of biological and technical replicates.
>
> The target file is:
>
> Sample_name sample_type
sample_replicate
> disease_status
>
> MPM_07 1
> 1 1
>
> MPM_08 1
> 2 1
>
> MPM_09 1
> 3 1
>
> MPM_10_a 1
> 4 1
>
> MPM_10_b 1
> 4 1
>
> MPM_11 1
> 5 1
>
> MPM_12 1
> 6 1
>
> PP_01_a 2
> 7 0
>
> PP_01_b 2
> 7 0
>
> PP_02 2
> 8 0
>
> PP_03 2
> 9 0
>
> PP_04 2
> 10 0
>
> PP_05 2
> 11 0
>
> PP_06 2
> 12 0
>
> PV_02 3
> 13 0
>
> PV_03 3
> 14 0
>
> PV_04 3
> 15 0
>
> PV_05 3
> 16 0
>
> Each sample is hybridized on an Affymetrix HG-U133-Plus2 array.
>
> So I have 7 mesothelioma samples (sample_type=1) where 2 were from the
> same patient (MPM_10 a e b)), 7 parietal pleural samples (sample_type=
> 2) where 2 were from the same patient (PP_01 a e b) and 4 visceral
> pleural samples (sample_type= 3). In reality 4 parietal pleural
> samples (PP_02,PP_03,PP_04 and PP_05) and 4 visceral pleural samples
> (PV_02,PV_03,PV_04 and PV_05) come from the same patients.
>
> pd<- data.frame(sample_type= c(rep(1,7),rep(2,7),rep(3,4)),
> sample_replicate = c(1:4,4,5,6,7,7,8:12,13:16),
> disease_status=c(rep(1,7),rep(0,11)))
>
> biolrep<-pd$sample_replicate
>
> f<- factor(pd$sample_type)
>
> design<- model.matrix(~0+f)
>
> colnames(design)<- c("MPM", "PP", "PV")
>
> I tried to handle technical replicates using the block argument of
> function
> duplicatecorrelation() as follows:
I don't think you can use duplicateCorrelation() here, as you don't have
duplicates for all samples. I believe lmFit() with a cor argument will fit a
block diagonal correlation matrix, which is clearly not applicable here. I
may be in error however, in which case Gordon Smyth will surely post a
correction around 5-6 pm EDT or so.
With a mixture of duplicated and not duplicated samples, you will likely
have to do one of two less than ideal things. First, you could simply ignore
the duplication, and analyze as if the duplicates were independent samples.
This is less than ideal because there will be a correlation between these
samples, which will tend to lower your estimate of intra-sample variation.
Second, you could compute means of the duplicates and then use those in lieu
of the original data. Again, this is not ideal, as the means will have an
intrinsically lower variance than individual samples. All things equal, this
is probably the better way to go.
Best,
Jim
>
> corfit<- duplicateCorrelation(eset_norm_genes_ff_filtered, design,
> ndups=1, block= biolrep) # eset_norm_genes_ff_filtered is an
> ExpressionSet object containing pre-processed and filtered data
>
> I am interested in identifying differentially expressed genes between
> MPM and PP and between PV and PP.
>
> contrast.matrix_all.contrasts<-
> makeContrasts(MPMvsPP=MPM-PP,PVvsPP=PV-PP,levels=design)
>
> fit_ff<-lmFit(eset_norm_genes_ff_filtered, design,block=biolrep,
> ndups=1,cor=corfit$consensus)
>
> fit2_ff<- contrasts.fit(fit_ff, contrast.matrix_all.contrasts)
>
> fit2e_ff<-eBayes(fit2_ff)
>
> I think that my approach is correct for the first contrast (MPM vs PP)
> but not for the second one because biolrep doesn't consider the fact
> that some samples between PP and PV are paired.
>
> Am I correct?
>
> What about defining biolrep<-c(1:4,4,5,6,7,7,8:12,8:11)?
>
> Is there a method to handle such an experimental design?
>
> Sorry for my long post!
>
> Any suggestion/comment is welcome.
>
> Cheers,
>
> Manuela
>
>
>
> ----------------------------------------------------------------------
> ------
> ----------
>
> Manuela Di Russo, Ph.D. Student
> Department of Experimental Pathology, MBIE University of Pisa Pisa,
> Italy e-mail:<mailto:manuela.dirusso at for.unipi.it>
> manuela.dirusso at for.unipi.it
> mobile: +393208778864
>
> phone: +39050993538
>
>
>
>
> [[alternative HTML version deleted]]
>
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