DeSeqDataSet experimental design: column with integer values
Any IDs (e.g. replicate number) don't need to be included in the design formula if it's not a repeated factor in multiple samples.

In this case, you have said that mouse number is related to the number of mice used in the pool (and is a variable factor that is consistent across multiple samples), in which case it should be included in the design matrix. This would also be the case if the same mouse were being used for multiple RNASeq experiments. Converting an integer value to a factor is done by the factor method:

sampleTable$MiceUsed &lt;- factor(sampleTable$MiceUsed);


Other examples of things that might be useful in the design formula would be sex, age, and sequencing run batch (assuming these are not consistent across samples).

With a design formula of something like ~Groups + Mouse, it will also be comparing Groups, it's just hidden in the results. The DESeq2 vignette is really great at explaining what's going on, but as a quick answer you can pull out the Groups results with the contrast function. This will pull out group differences that are independent of mouse number:

results(dds, contrast=c("Groups", "GF", "Wld"))


[first value is the variable name, second value is the numerator condition, third is the denominator condition]