First of all I would abandon tophat and cufflinks since both methods are now considered deprecated. I would switch to a quantification tool such as salmon or kallisto followed by differential analysis with something like DESeq2 and edgeR. Both the latter tools have options to test against a certain fold change (instead of the default test against 0) which allow to reduce the number of DGEs to those that are probably the most biologically-meaningful. This is recommended by the edgeR authors if you feel that you have "too many genes" and want to filter in a data-driven fashion without tweaking the p-values too much. Effectively this means that only genes with higher FCs will be retained. In edgeR the function is called glmTreat, for the DESeq2 analogon please check the documentation. Still I think there is no formal way of obtaining exactly <int> DGEs since this is not how DGE analysis works. It only tells you how many genes at the given depth, number of replicates are significantly different from the expectation which again is based on the underlying model.