Dear Vang Quy Le,
I don't have any experience analysing MeDIP data myself, so I will assume that you know how to generate counts from MeDIP that are suitable for edgeR, and I will just answer the linear modelling question.
I also assume that you have read the section of the edgeR User's Guide called "What to do if you have no replicates."
In terms of an edgeR analysis, you have two main choices. First, you could simply omit the dispersion estimation steps of the edgeR pipeline and run glmFit() and glmLRT() with a manually set value for the dispersion. You have already mentioned this possibility. There is no problems with this except that the number of DE genes will be highly dependent on the dispersion value you choice. Nevertheless, it is much better than assuming Poisson variability. We took this approach for our paper in Cell Reports:
http://www.ncbi.nlm.nih.gov/pubmed/23375371
Second, you could manufacture residual degrees of freedom by fitting a smooth curve to the time course trends. This approach is explained the section called "Many time points" in the limma User's Guide. We took this approach to analyse the development stages of Drosophila melanogaster in the voom paper:
http://genomebiology.com/2014/15/2/R29
Personally, I would take the second approach. I would fit an orthogonal quadratic polynomial to the time trend for the Control and Treated groups separately. This will allow you to use the complete edgeR pipeline including dispersion estimation. This will allow you to test for time trends for each gene in each of the groups. It will also allow you examine at which time point each gene has its peak expression. See the analysis in the voom paper.
Best wishes
Gordon
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