I've simulated RNA abundance with wgsim. The simulation itself was error free. There is a single factor in my experiment that looks like:

```r
           A1     A2     A3     B1     B2     B3
R1_101    113    113    113     13     11      9
R1_102    247    246    246     12     12     14
R1_103  20835  20915  20788   9973   9955   9973
```

A1, A2, A3 are the simulated replicates for the first level. B1, B2 and B3 are the simulated replicated for the second level.

As expected, the reads counts for each level are very close because it was an error-free simulation. The purpose of the experiment is to compare it with cuffdiffs (another differential package) in detecting log-fold changes.

Unfortunately, I run into an error in DESq2:

```r
Error in estimateDispersionsFit(object, fitType = fitType, quiet = quiet) : 
    all gene-wise dispersion estimates are within 2 orders of magnitude
```

It looks like the package is unable to estimate a dispersion factor (most likely it's too small). However, I had no problem with cuffdiffs. Is there anything that I can do to make it work?
The DESeq2 method has empirical Bayes parts to it, which involve sharing information across genes to improve estimates (see [the paper][1]). In this case, during dispersion estimation, we look across the genes at the distribution of gene-wise dispersion estimates to improve the final estimates (posterior modes). If you simulate data which has no (over)dispersion, then these methods don't make sense.

Did you clip the warning message which was printed in the console for some reason? Because the rest of it tells you what to do:

```r
# all gene-wise dispersion estimates are within 2 orders of magnitude
# from the minimum value, and so the standard curve fitting techniques will not work.
# One can instead use the gene-wise estimates as final estimates:
dds <- estimateDispersionsGeneEst(dds)
dispersions(dds) <- mcols(dds)$dispGeneEst
```

 [1]: http://www.genomebiology.com/2014/15/12/550