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.
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.
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 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
>> To: "bioconductor@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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