Hi Benjamin,

Your fundamental issue is that your sibship and treatment variables are 
confounded, since each animal belongs to one and only one treatment. So, 
when you put both animal and treatment into the same design matrix, the 
result is not full rank. I believe that you must treat the animal factor 
not as a single factor with 6 levels, but rather as two separate factors 
with 3 levels each, within the two treatments. I'm not aware of a way to 
do this automatically, but you can accomplish it by manually 
constructing an appropriate contrasts matrix for the animal factor, 
which requires a bit of extra work. I've prepared some example code 
here: https://gist.github.com/DarwinAwardWinner/1b2ba6e980280cadd73c

I am reasonably sure that the design matrix constructed above accurately 
represents your experimental design, but you may want to check with a 
statistician at your local institution to check my logic.

Also, note that, when considering only biological replicates, you have 
12 samples with a design matrix of rank 8, leaving only 4 residual 
degrees of freedom. Because of this, you might consider doing the 
analysis without using the animal factor at all, which would leave 8 
residual df but lose the advantage of having a paired design for the 
timepoint test.

-Ryan

On 5/14/14, 7:47 AM, Otto, Benjamin wrote:
> Dear list members,
>
> though I am aware of each of the single design models, I'm not quite sure if I got the design for the following setting correct:
>
> The experiment consists of 24 single-color samples in total obtained from 6 animals. From each animal two time points (prior to treatment and post treatment) exist. In addition each sample was obtained in a technical duplicate. 3 of the animals were treated with a reagent, the other three with a control. The matrix is provided below.
>
> Now what I would like to do is a classic calculation of time, treatment and time x treatment effects ... under consideration of 1) sample pairing and 2) technical replication.
>
> My first (simple) approach neglecting treatment is as follows:
> -----------------------------
> Line 1 > biolrep <- pData$Replicate
> Line 2 > timep <- factor(pData$Timepoint, levels=c("Pre", "Post"))
> Line 3 > treat <- factor(pData$Treatment, levels=c("reagent","control"))
> Line 4 > sibship <- factor(pData$Animal)
> Line 5 > design <- model.matrix(~sibship+timep) # I will first skip treatment here for simplicity
>
> Line 6 > corfit<-duplicateCorrelation(eset,design, ndups=1, block=biolrep) # account for tech-reps.
> Line 7 > fit <- lmFit(eset, design, block=biolrep, cor=corfit$consensus) #include it in fit
>
> Line 8 > fit <- lmFit(eset, design)
> Line 9 > fit <- eBayes(fit)
> Line 10 > topTable(fit, adjust = "BH", coef="timepPost")
> --------------------------------
>
> Once I incorporate the treatment instead of the time
>
> Line 5 > design <- model.matrix(~sibship+treat)
>
> ... I get an error:
>
>>> Partial NA coefficients for 96 probe(s)
> Note: Our array has 96 probes in total so actually all probe coefficients are NA.
>
>
> If I remove the animal source info  and swap line 5 by:
>
> Line 5 > design <- model.matrix(~treat)
>
> ... then everything is fine. Probably because of the overlap of the groups (all samples of each animal are either part of the treatment or the non treatment group).
>
>
> So my question is: Is there some design model where I can incorporate time AND treatment under consideration of sample pairing and technical replication at the same time?
>
>
> Thanks for the help
>
> Benjamin
>
>
>
>
>
>
> Sample Animal Replicate Timepoint Treatment
> 1 Animal 1 1 Pre control
> 2 Animal 1 1 Pre control
> 3 Animal 1 2 Post control
> 4 Animal 1 2 Post control
> 5 Animal 2 3 Pre reagent
> 6 Animal 2 3 Pre reagent
> 7 Animal 2 4 Post reagent
> 8 Animal 2 4 Post reagent
> 9 Animal 3 5 Pre control
> 10 Animal 3 5 Pre control
> 11 Animal 3 6 Post control
> 12 Animal 3 6 Post control
> 13 Animal 4 7 Pre reagent
> 14 Animal 4 7 Pre reagent
> 15 Animal 4 8 Post reagent
> 16 Animal 4 8 Post reagent
> 17 Animal 5 9 Pre control
> 18 Animal 5 9 Pre control
> 19 Animal 5 10 Post control
> 20 Animal 5 10 Post control
> 21 Animal 6 11 Pre reagent
> 22 Animal 6 11 Pre reagent
> 23 Animal 6 12 Post reagent
> 24 Animal 6 12 Post reagent
> --
>
> DANKE FÜR 125 JAHRE ENGAGEMENT UND VERTRAUEN.
> www.uke.de/125
> _____________________________________________________________________
>
> Besuchen Sie uns auf: www.uke.de
> _____________________________________________________________________
>
> Universitätsklinikum Hamburg-Eppendorf; Körperschaft des öffentlichen Rechts; Gerichtsstand: Hamburg
> Vorstandsmitglieder: Prof. Dr. Christian Gerloff (Vertreter des Vorsitzenden), Prof. Dr. Dr. Uwe Koch-Gromus, Joachim Prölß, Rainer Schoppik
> _____________________________________________________________________
>
> SAVE PAPER - THINK BEFORE PRINTING
>
> 	[[alternative HTML version deleted]]
>
>
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor@r-project.org
> https://stat.ethz.ch/mailman/listinfo/bioconductor
> Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor


	[[alternative HTML version deleted]]

