2D classification without alignments can be useful after a consensus refinement to remove junk (gold particles, carbon/graphene edges, overlapping particles, etc).
Would it be possible to add an option to 2D classification to switch off alignments and just perform classification (assuming that the input particles have an alignments.3d slot?
Thanks for the interesting feature request. We checked out the linked article and we have one question.
Are you referring to the process of taking already 3D aligned particles, binning them into different groups by their viewing direction, and then doing 2D Classification on these binned groups of particles? If so – what would the benefit of this approach be over standard 3D Classification?
Or, do you mean just regular 2D Classification, where the alignments are fixed after a certain iteration?
I’ve occasionally had (higher symmetry) datasets with lower quality particles that are next to impossible to remove by 3D classification, where bouncing from 2D-3D-2D-3D-2D-3D has finally managed to sift them out. Admittedly, they’re highly specific cases, but I was experimenting with “can this actually be done automatically or do I really need to go through the particle stack manually…?”
Are you referring to the process of taking already 3D aligned particles, binning them into different groups by their viewing direction, and then doing 2D Classification on these binned groups of particles? If so – what would the benefit of this approach be over standard 3D Classification ?
This - just skipping alignments and using alignments from 3D (no binning required). Same principle as classification without alignments in 3D - one can get good results with a larger number of classes, and smaller groups, if you don’t need to search over orientations/offsets. The main application is for removing junk/damaged particles, as described in the article. E.g. let’s say you have carbon edges or small gold particles in your dataset, overlapping with good particles. This is an inherently 2D problem, but regular 2D classification will often force these junk particles to align with good particles. Classification without alignments in 2D can be helpful to address this, as described in the linked article.
The idea of fixing alignments after a certain iteration and sub-classifying each class could be interesting too though - this is akin to the “Deep 2D” classification approaches implemented in ROME etc.
Thanks for the clarification, this makes sense and I see its potential value as an additional method of junk removal by prohibiting the 2D class algorithm from “absorbing” junk into other classes via overfitting the alignments. We’ve recorded it!
To piggy back on top of this, if this gets implemented, it’d be amazing if we could provide the templates for classification, similar to how 3D class can work.
Just thought I’d see if any of the team had any further updates/thoughts on this? Ran face-first into trouble cleaning a new dataset this week and thought of this again.
Just to add - this would also be useful when cleaning subparticles after extraction, in cases where one part of the whole particle is usable, but some of the subparticles are not (e.g. due to aggregation, particle overlap, contamination, graphene edges, etc)
Just wondering if there is any updates on this request? I have a good 3D class which unfortunately still contains junk particles. Further 3D classification is time-consuming and does not do a good job separating the junks. 2D classification without alignment would be very valuable in my case. Currently my only solution is to resort to relion, but switching data back and forth is such a pain
Same. In more than one of our difficult cases, we are looking for needles in haystacks. We need several rounds of 2D+3D classifications and heterogeneous refinements (from several mi ptcls) to get the map, and still we can see it is contaminated with the predominant particles. (I use 3DVA and 3DFlex a lot). 2D classification without alignment would help identifying and eliminating the contaminants, I believe - at least I’d like to try it.