I have a question about the recently released “Reference Based Motion Correction” recently released in the nice last update.
Based on the fact that the motion correction depends on a spatial prior to allowing a better motion estimation despite the low SNR, To what extent a low density of particles by micrograph will influence the robustness of the estimation? For example, if a micrograph contains 2 particles from the final subset I guess the spatial prior will not help much. If you deem that low particle density is redhibitory for a good motion estimation what would be the limit of particles by micrographs to get a good motion estimation?
Great question. Anecdotally, I’ve seen reference motion succeed on very sparse datasets, despite the fact that in principle you’re right: the spatial prior has less data to work with on sparse micrographs. It seems that the temporal prior can perform surprisingly well on its own. If we get a large volume of feedback indicating that there really is a servere problem, we might implement some sort of warning in the future. At the moment, we only prohibit micrographs with a single pick.
It would be nice to be able to override the single-pick limit, as with some of the samples I work with are lucky if we acquire four or five particles on a micrograph (the fun of working on giant viruses!) so after 2D classification and 3D heterogeneous refinement/classification the possibility of a single particle per micrograph is very real…
Hi @rbs_sci, thanks for the feature request. We’ll consider this for a future release.