Is it okay to use deeptools bamCompare (SES normalization) for comparisons across different ATAC-Seq datasets? We are trying to use deeptools for analysis of ATAC Seq datasets. We have datasets with different sequencing depths and are wondering if bamCompare's SES based normalization is appropriate for comparing two ATACSeq datasets as opposed to ChipSeq datasets. If such normalization is okay to perform, does it make sense to compare difference in values across comparisons? Let's say I do bamCompare on A and B using SES, and then I do bamCompare (using SES) on A and C. Will it then make sense to compare the two comparisons (A-B &amp; A-C)?

If such normalization isn't okay, what are the best tools to normalize ATAC-Seq datasets with different sequencing depths so that one can perform comparisons like above? 
Thanks for any suggestions or comments.
I would be very hesitant to use SES normalization in a case like this. We recommend that people run plotFingerprint first and see if there is good separation between the samples and only then using SES. I would not expect there to be drastic separation between the samples for ATAC-seq data, so I don't think it will work well. Our standard ATAC-seq normalization is RPGC (aka, "1x normalization"). That should work well for your use-case as well.

As an aside, if you really want to make statistically-backed comparisons between groups I can recommend the CSAW package.