Entering edit mode
Dear Osee,
I haven't seen anyone else try to answer your first question, so I
will.
You're trying to put too many terms in your design matrix, making the
experiment much more complicated than it actually is. Your experiment
simply compares two treatment groups. It doesn't make sense to
estimate
effects for fish or tanks, because these are just your randomly
sampled
experimental units. The only real complication of your experiment is
that
some fish share the same tank, so you need to allow for possible
correlations with a tank. You can do this is limma by:
design <- model.matrix(~Key)
fitcor <- duplicateCorrelation(ES,design,block=tank)
fit <- lmFit(ES,design,block=tank,correlation=fitcor$consensus)
fit <- eBayes(fit)
topTable(fit,coef=2)
This approach finds genes which respond to your treatment.
Best wishes
Gordon
> Date: Tue, 27 Jul 2010 06:57:36 -0500 (CDT)
> From: "Y. Osee Sanogo" <sanogo at="" illinois.edu="">
> To: bioconductor at stat.math.ethz.ch
> Subject: [BioC] Nested Design (Again) & Subset WithinArray
Correlation
>
> Hello,
>
> I have two questions which may be really trivial...but since I am
stuck,
> I'll appreciate any help.
>
> Question 1: Nested design: This has been addressed before, but I am
just not
> sure whether I am doing it right. The experiment consisted of two
groups of
> fishes (treated and not treated) with three tanks in each group.
Each tank
> hosted three fishes (total =18) of those fishes n=10 (5 per
treatment group)
> were selected for microarray (Notice unequal number of fishes per
tank!).
>
> I am interested in 1) Treatment effect (individual fishes)
> 2) Treatment effect (fishes nested
within
> tanks, i.e. Need to average the gene expression of fishes within
each tank )
> 3) Whether there is tank effect
>
> #ExpressionSet =ES_Filt
> #targets= see below:
>
> Sample Key tank Fish SAMPLE_LABEL
> 25407102_532.xys CON 1 CON_3 SOM01K28
> 25407202_532.xys CON 1 CON_2 SOM01K29
> 25414902_532.xys EXP 2 EXP_1 SOM01K2D
> 25407302_532.xys CON 3 CON_1 SOM01K2C
> 25406602_532.xys EXP 4 EXP_2 SOM01K25
> 25407002_532.xys EXP 4 EXP_3 SOM01K27
> 25415502_532.xys EXP 4 EXP_4 SOM01K2E
> 25405602_532.xys CON 5 CON_4 SOM01K23
> 25406702_532.xys CON 5 CON_5 SOM01K26
> 25415702_532.xys EXP 6 EXP_5 SOM01K24
>
> I have tried the following design based upon what I found online,
but was
> not really sure whether this is the right way of doing it.
>
> design.nested_ES<- model.matrix(~Key + (tank/Fish), data=targets)
> colnames(design.nested_ES)
> #I am getting many contrasts, and I am not sure which one represents
> ?tank/Fish?
>
> fit.nested_ES <- lmFit(ES_Filt, design.nested_ES)
> Fit.nested_ES <- eBayes(fit.nested_ES)
> Pred2_Nested_ES<-topTable(Fit.nested_ES, coef=2, adjust="BH", n=Inf)
> Pred2_Nested_ES[1:10,]
>
> I will really appreciate your help.
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