Signal subtraction from raw micrographs?

Hi all,

Is it currently possible to subtract signal not from extracted particles, but from the raw micrographs?
I am familiar with the workflow of doing it via Particle Subtraction, but this job does not allow micrographs as an input. Thanks for your help!



@twg Do you have a motivating use case where particle subtraction from “raw” micrographs would work better than the current implementation?

For very heterogeneous samples, I could see picking particles of minor components from micrographs where the signal of the major species has been subtracted being useful (and then extracting from the original micrographs). Would need to be tested though, unclear how much benefit it would offer

Hi again,

Sorry for not giving more context.

@olibclarke is right, our goal would be to pick minor components that are obscured by the major component.

A similar approach was recently used in this paper (using Relion):
Structure of dynein–dynactin on microtubules shows tandem adaptor binding

Figure 1a highlights how microtubule signal was subtracted from micrographs, allowing the picking and alignment of the desired particles.


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It was also done here:

If you take a look at or in the pyem repo, it wouldn’t be too hard to add the capability. I’d suggest that approach since it’s kind of a specialized task.


This type of signal subtraction is not implemented in cryoSPARC, and there are no plans for an implementation. You may want to try some of the suggestions in this thread, like