Hello Somnath:
Can you please post some example of the data and the code? It will make help
to make your description clearer.

If I understand correctly, you are assuming the replicates to be identical;
so if rep_1 receives dose_1 and rep_2 receives dose_2, there is no replicate
effect for your response (equivalent to assuming no random rep effect).

basically your data is like:

             dose1                               dose2
         r11   r12   .....                      r21   r22 ....
g1      x      x                                 x      x
g2      x      x                                 x      x
.
.

rij represent the jth replicate for ith treatment


If I am correct, the adj. p-values are for the hypotheses tests for
differential gene expression corrected for multiple comparisons. The
coefficients are the log fold change for a particular gene if dose changes
from 1 -> 2.

All these interpretations hold under the assumption that replicates are
"identical".

Thanks,
S.



On Tue, Nov 9, 2010 at 7:17 PM, somnath bandyopadhyay <
genome1976@hotmail.com> wrote:

>
> Hi there,
> I am trying to use LIMMA to analyze gene expression data from an experiment
> which has dose response but only one replicate at each dose. I tried to fit
> a linear model using lmfit(). I used the doses as continuous variable. I do
> the ebayes fit and finally do decide tests with adj p values (BH corrected).
> I get coefficients, intercept and dose as output with t-stat and p values
> for each. I was wondering how to interpret these. What does intercept, dose
> and coefficients mean in this case? The data matrix I read into R was Affy
> Plus2 chip data for 4 doses of a compound. Any help would be greatly
> appreciated.
> Best Regards,Som.
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