[BioC] Differential expression in time series

[Hyungwon Choi]

Hi James,

Just a guess, but doesn't Tai and Speed (Ann Statist, 34(5), 2006) have 
timecourse package for this?

Hyungwon

James W. MacDonald wrote:
> Hi Sanjat,
>
> Sanjat Kanjilal wrote:
>   
>> James W. MacDonald <jmacdon at ...> writes:
>>
>>     
>>>>   I have two strains (B and W) and two different time points (2 and 1 
>>>>         
>> hours). I am trying to find genes which
>>     
>>> respond differently:
>>>       
>>>>   1. in B vs W, when comparing time points 1 and 2 (i.e. I compare BvsW in 
>>>>         
>> time point 1 to BvsW in time point 2)
>>     
>>>>   2. in time in different strains (i.e. I compare B1vsB2 versus W1vsW2) 
>>>>    
>>>>   I am getting the same results, or they should be the same?
>>>>   Could you comment on whether this is a right way to analyse time series?
>>>>         
>>> It appears you want to test for the interaction between time and strain. 
>>>   Both of your points (1 and 2 above) are essentially identical. You 
>>> want to know what genes react differently over time in the two strains 
>>> (and worded differently, but meaning the same thing - what genes react 
>>> differently between the strains at different times). These two things 
>>> are verbo-algebraically the same (yup, new word. My next move is to 
>>> start a new Wikipedia entry describing exactly what it means ;-D)
>>>
>>>       
>>>>    
>>>>   My code is below.
>>>>    
>>>>    
>>>>   Thank you,
>>>>   Lev.
>>>>    
>>>>   > temp<-rma(data)
>>>>   > targets <- readTargets("Targets.txt")
>>>>
>>>>         
>>>>> lev <- c("W.1","B.1","W.2","B.2")
>>>>> f <- factor(targets$Target, levels=lev)
>>>>> design <- model.matrix(~0+f)
>>>>> colnames(design) <- lev
>>>>> fit <- lmFit(temp, design)
>>>>> cont.dif <- makeContrasts(Diff.Time=(B.2-W.2)-(B.1-W.1), Diff.Strain=(B.2-
>>>>>           
>> B.1)-(W.2-W.1), levels=design)
>>     
>>> As the statements above are verbo-algebraically the same, these two 
>>> terms are algebraically the same.
>>>
>>> (B.2-W.2)-(B.1-W.1) = (B.2-B.1)-(W.2-W.1)
>>>
>>> Hence you should get the same results from each contrast. And yes, this 
>>> contrast does give you the interaction.
>>>
>>> Best,
>>>
>>> Jim
>>>
>>>       
>> Thanks for your helpful explanation Jim.
>>
>> As a follow-up question:
>>
>> Can the (B.2-W.2) - (B.1-W.1) contrast be thought of as comparing B @ time 2 
>> to B @ time 1 while 'normalizing' to the effects of the wild type (ie I'd like 
>> to subtract out the effects of the control group)?  If not, is there a term 
>> that would do that?
>>     
>
> Yes, you can think of it that way. That's the same idea behind a paired 
> t-test. Each individual might have an inherently different baseline (the 
> wt in this case), and you are just interested in seeing if the treatment 
> has the same relative effect in both samples. Since the difference in 
> baseline between samples is probably not interesting, you subtract it out.
>
> Best,
>
> Jim
>
>
>   
>> Thanks,
>> Sanjat
>>
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>>     
>
>