[BioC] Nested Design (Again) & Subset WithinArray Correlation

Gordon K Smyth smyth at wehi.EDU.AU
Thu Jul 29 01:53:54 CEST 2010


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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