[BioC] Nested Design (Again) & Subset WithinArray Correlation
Gordon K Smyth
smyth at wehi.EDU.AU
Fri Jul 30 01:04:34 CEST 2010
Dear Osee,
Despite the name of the function, which I admit does suggest more narrow
applicability, duplicateCorrelation() can be used for any nested error
structure.
Best wishes
Gordon
On Thu, 29 Jul 2010, Y. Osee Sanogo wrote:
> Dear Gordon,
>
> Thank you for the code. It works!!
> My only question is does it then matter that the probes set are not
> duplicated and there is no technical replicate per se? I thought
> duplicateCorrelation is meant for duplicates or technical replicates? Please
> clarify.
>
> Thanks again.
>
> Osee
>
>
> On 7/28/10 6:53 PM, "Gordon K Smyth" <smyth at wehi.EDU.AU> wrote:
>
>> 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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