Dear Dr. Smyth,
Thank you very much for the answer.

That clarifies a lot of things!

I have one last question to make sure I understood you correctly - in the
case of two colored arrays, even if we are regressing the M values on the
treatment (sample annotation), we will call the coefficients as logFC.

Thanks in advance for your help.

Best Regards,
S.

On Mon, Sep 27, 2010 at 7:17 PM, Gordon K Smyth <smyth@wehi.edu.au> wrote:

> Dear Sunny,
>
>  Date: Mon, 27 Sep 2010 03:14:01 -0400
>> From: Sunny Srivastava <research.baba@gmail.com>
>> To: bioconductor <bioconductor@stat.math.ethz.ch>
>> Subject: [BioC] Question: How does limma derives its logFC value in
>>        two     colored arrays?
>>
>> Hello Bioconductor Gurus,
>>
>>
>> I have the a data about gene expression from TWO COLORED Agilent array. I
>> wanted to check differential expression between p3 and wild strain of
>> yeast.
>> In one array p3 is colored with Cy5 and wild is colored with Cy3 and in
>> the
>> second array the dyes are swapped. Assuming I have normalized my data
>> using
>> VSN and obtained M values for the two arrays, I now want to use limma to
>> derive the differentially expressed genes.
>>
>> My model matrix (say design) in this case will be
>>
>> p3
>> 1
>> -1
>>
>> if wild type is the reference.
>>
>> If my understanding is correct about how limma analyzes differential
>> expression, then M value is the dependent variable, sample annotation
>> (whether p3 or wild, provided by design) is the independent (explanatory)
>> variable, and a linear model is fit per gene using the following equation.
>>
>>
>> lmFit( M , design)
>>
>
> This is all correct.
>
>  As the data per gene is small, it is better to use eBayes method to obtain
>> genewise p-value. But the object obtained from eBayes (say fit3) doesn't
>> contain the value *logFC*.
>>
>
> Yes it does--the logFC is fit3$coefficients.  This terminology is because
> the logFCs are estimated as the coefficients of the linear model.
>
> Best wishes
> Gordon
>
>  When I use topTable to order the genes, then
>> logFC appears.
>>
>> The concept of logFC is clear to me in case of a Affy single colored array
>> (ie log (Int_trt/ Int_control) ), but somehow I am still confused how to
>> interpret this in two colored arrays.
>>
>> In my opinion M value (for each array) should represent logFC if color
>> bias
>> is ignored. How does limma derives its logFC value in two colored arrays?
>> Is
>> it based on the B statistics? Please enlighten me !
>>
>> Thanks in advance for any help.
>>
>> Best Regards,
>> S.
>>
>
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