This is related to NAs being sorted above other numbers by default in
numpy. At the moment, the only way to prevent this sort of behavior is
by specifying --missingDataAsZero in computeMatrix.
Having said that, I've always found this sort of behavior a bit weird.
There are drop-in replacement functions that handle NAs in the way
you're expecting and those of us on the deepTools team should just sit
down and think whether it really makes sense to continue with the
current behavior or to switch to the aforementioned functions.
BTW, if anyone on this list is strongly in favor of keeping the current
behavior then please speak up! We'd prefer not to overlook use cases.
Devon Ryan, PhD
Bioinformatician / Data manager
Bioinformatics Core Facility
Max Planck Institute for Immunobiology and Epigenetics