Hi Gordon,

I understand now. Thank you so much for your help.

Best,
Eleanor



On Wed, Apr 2, 2014 at 11:17 PM, Gordon K Smyth <smyth@wehi.edu.au> wrote:

> I mean when you get to the glmLRT() step of the analysis pipeline that you
> set coef=ncol(design).  The design matrix has 7 columns, so
>
>   lrt <- glmLRT(fit,coef=7)
>
> etc.
>
> One of your earlier emails said you had completed a differential
> expression analysis in edgeR, so I assume that you know what the above
> means.  If you don't, then follow one of the case studies in the edgeR
> User's Guide.
>
> Gordon
>
>
> On Wed, 2 Apr 2014, Eleanor wrote:
>
>  Hi Gordon,
>>
>> Could you elaborate on how to test for the last coefficient? Sorry if
>> that's an elementary question. I'm a master's student working with VERY
>> limited resources in my department, and I appreciate all the help I can
>> get. Thanks for all your help!
>>
>> Best,
>> Eleanor
>>
>> On Apr 2, 2014, at 8:25 PM, Gordon K Smyth <smyth@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@gmail.com>
>>>> To: "bioconductor@stat.math.ethz.ch" <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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>>
>>
>>
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