[BioC] Designing a model with blocking and other interactions

[Gordon K Smyth]

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 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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