On Thu, 3 Apr 2014, Eleanor Su wrote:
> Hi Gordon,
>
> When I enter the design that you've suggested,
>
> design1 <- model.matrix(~Family)
> design2 <- model.matrix(~mitoHap*Treatment)
> design <- cbind(design1,design2[,3:4])
>
> and test for the last coefficient, I see that I get DE for the
interaction
> between Treatment:mitoHap (which is what I wanted to look at). As I
look
> through the other columns in the design matrix, I see that I have
data for
> Treatment (coef=6) but not for mitoHap.
No, naturally you can't have a mitoHap column because that factor is
confounded with Family.
> If I use an equivalent formula for design2
>
> design2<-model.matrix(~mitoHap+Treatment+mitoHap:Treatment)
>
> would this allow me to see both factors (treatment and mitoHap)
> independently in other columns of the design matrix AND the
interaction
> between the two in the last coefficient? I'd like to be able to look
at
> differential expression in each factor independently and the
interaction
> between the two.
Well, now you are ignoring Family, which previously you felt it was
important to account for.
You are also asking for things that are impossible. It isn't
meaningful
to test for factors independently of their interaction.
You might find it helpful to read the sections on "multi level
designs" in
the edgeR and limma User's Guides.
Gordon
> If so, how would the last "design" formula change?
>
> design<-cbind(design1,design2[?])
>
> Thanks for you help.
>
> Best,
> Eleanor
>
>
> On Wed, Apr 2, 2014 at 8:25 PM, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote:
>
>> Dear Eleanor,
>>
>> design1 <- model.matrix(~Family)
>> design2 <- model.matrix(~mitoHap*Treatment)
>> design <- cbind(design1,design2[,3:4])
>>
>> Then test for the last coefficient.
>>
>> Best wishes
>> Gordon
>>
>> Date: Tue, 1 Apr 2014 11:24:52 -0700
>>> From: Eleanor Su <eleanorjinsu at="" gmail.com="">
>>> To: "bioconductor at stat.math.ethz.ch" <bioconductor at="" stat.math.ethz.ch="">
>>> Subject: [BioC] Designing a model with blocking and other
interactions
>>>
>>> Hi All,
>>>
>>> I'm trying to set up a model matrix where I can look at the
interaction
>>> between Treatment and mitochondrial haplotypes in my paired
samples. These
>>> are the preliminary commands that I've set up:
>>>
>>> rawdata<-read.delim("piRNAtotalcount<10.txt", check.names=FALSE,
>>>>
>>> stringsAsFactors=FALSE)
>>>
>>>> y <- DGEList(counts=rawdata[,2:11], genes=rawdata[,1])
>>>> Family<-factor(c(6,6,9,9,11,11,26,26,28,28))
>>>> Treatment<-factor(c("C","H","C","H","C","H","C","H","C","H"))
>>>> mitoHap<-factor(c("S","S","S","S","S","S","D","D","D","D"))
>>>> data.frame(Sample=colnames(y),Family,Treatment,mitoHap)
>>>>
>>> Sample Family Treatment mitoHap
>>> 1 6C (S) 6 C S
>>> 2 6H (S) 6 H S
>>> 3 9C (S) 9 C S
>>> 4 9H (S) 9 H S
>>> 5 11C (S) 11 C S
>>> 6 11H (S) 11 H S
>>> 7 26C (D) 26 C D
>>> 8 26H (D) 26 H D
>>> 9 28C (D) 28 C D
>>> 10 28H (D) 28 H D
>>>
>>> design<-model.matrix(?)
>>>>
>>>
>>> I have 10 sequencing samples from 5 different families (a
treatment and
>>> control sample from each family) and two different types of
mitochondrial
>>> haplotypes. How do I set up a design where I can look at the
interaction
>>> between the Treatments and mitoHap while still accounting for
Family?
>>>
>>> Any help would be greatly appreciated. Thank you for your time.
>>>
>>> Best,
>>> Eleanor
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