Hi, Jenny:
Thanks for the quick reply. And thanks for pointing out about posting. I
thought maybe my subject was not good enough to be noticed and that is why I
posted again. This is my first post, so long way to go!
Regarding your second point: I don't think my question is a general one
about why ANOVA is better than a series of t-tests. I actually did both, but
realized that the result from one single model ( use all samples) gave me
much lower p-values, but when I looked at the expression value, the fold
change was nothing , like 0.5. That is why I wonder if the inflated DOF gave
me much low p-values. Any thoughts on that?
Thanks!
Sabrina
On Thu, Jan 21, 2010 at 12:05 PM, Jenny Drnevich wrote:
> Hi Sabrina,
>
> First, a little list ettiquette. If you don't get a response to a post
> within a day, it's not considered polite to just repost the same question
> verbatim the next day under a different Subject.
>
> Second: your question isn't specific to the modeling of lmFit. Instead,
> it's a general statistical question about why it's better to one ANOVA model
> instead of a series of t-tests. I suggest you consult a basic statistical
> textbook or a local statistician to find the answer.
>
> Cheers,
> Jenny
>
>
> At 10:39 AM 1/21/2010, sabrina s wrote:
>
>> Hello, everyone:
>>
>> I have a question related to conceptual understanding of lmFit.
>>
>> I have the following experiment that I want to conduct, but I am not sure
>> which is the right way to use design matrix and contrasts. Here is the
>> experiment:
>>
>> say I have 3 different strains that are genetically different, A, B and C
>> where A is the control. I also have two different treatments,
>> T1 and T2. For each strain, I have 4 arrays for each treatment, so in
>> total, I have 24 arrays. What I want to find out is the significantly
>> differentially expressed genes for the following comparison:
>> 1) for control strain A: T1 vs T2
>> 2) under T1, B vs. A (control)
>> 3) under T1, C vs. A
>> 4) for B, T1 vs T2
>> 5) for C, T1 vs T2
>> 6) interaction term of A and B , T1 and T2
>> 7) interaction term of A and C, T1 and T2.
>>
>> There are two ways I could use lmFit
>>
>> One is:
>>
>> for the design matrix, I will include all 3 strains and 2 conditions,
>> I use the following code:
>> A_T1, A_T2, B_T1, B_T2, C_T1, C_T2
>> sample1: 1 ,0 ,0, 0, 0 , 0
>> sample2 :
>>
>> Then make a contrast matrix and follow the code below:
>>
>> fitGene<-lmFit(gene,design=design,weights=arrayWt);
>> fitGene2<-contrasts.fit(fitGene,cont.matrix)
>> fitGene2<-eBayes(fitGene2,proportion=p);
>>
>>
>> Two:
>> Instead of using all samples at one time to fit into a lmFit function, I
>> use
>> two design matrix only involves A and B, T1 and T2,
>> and second design matrix that involves A and C, T1 and T2, and make
>> contrast
>> matrix and fit separately. and later on I can compare these two
>> results if I want to.
>>
>>
>>
>> The question I have is: which one is the right one? For the first method,
>> I
>> will have large DOF , and much lower p-values, but it was testing the
>> same thing as the second one, so am I creating an artifact? Thanks for
>> your help!
>>
>>
>>
>>
>> Sabrina
>>
>> [[alternative HTML version deleted]]
>>
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>>
>
> Jenny Drnevich, Ph.D.
>
> Functional Genomics Bioinformatics Specialist
> W.M. Keck Center for Comparative and Functional Genomics
> Roy J. Carver Biotechnology Center
> University of Illinois, Urbana-Champaign
>
> 330 ERML
> 1201 W. Gregory Dr.
> Urbana, IL 61801
> USA
>
> ph: 217-244-7355
> fax: 217-265-5066
> e-mail: drnevich@illinois.edu
>
--
Sabrina
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