[BioC] Differential expression in time series

[James W. MacDonald]

Hi Hyungwon,

They do, and you could probably use that package as well. However, in 
this case with only two time points I am not sure you need anything more 
than a two factor ANOVA.

Best,

Jim



Hyungwon Choi wrote:
> 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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>>>     
>>
>>   
> 

-- 
James W. MacDonald, M.S.
Biostatistician
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109
734-647-5623